Podcasts about Structural biology

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

In this episode, we talk with Prof. Tej Pal Singh, a leading figure in structural biology whose work has revealed the shapes and functions of hundreds of important proteins. He shares how these discoveries have helped us better understand immunity, inflammation, and disease—and how they're guiding new approaches to designing medicines. From breakthrough crystal structures to innovative peptide work, Prof. Singh offers a clear, inspiring look at how structural biology drives modern medical progress.

Looking to go molecule deep in atopic dermatitis? We've got just the expert. This week, we're joined by Dr. Christopher Bunick as he brings structural biology into the atopic dermatitis discourse. Listen in as he discusses cytokines, itch, and the new definition of “skin clearance.” Each Thursday, join Dr. Raja and Dr. Hadar, board-certified dermatologists, as they share the latest evidence-based research in integrative dermatology. For access to CE/CME courses, become a member at LearnSkin.com.   Christopher Bunick, MD PhD is an Associate Professor of Dermatology at Yale School of Medicine in the Department of Dermatology. He specializes in general medical dermatology and dermatologic surgery. He also performs unique dermatologic research studying the three-dimensional structures of skin-related proteins using x-ray crystallography and cryo-electron microscopy. He completed medical internship, dermatology residency, and a dermatology research fellowship (mentored by Nobel Laureate Dr. Thomas A. Steitz) at Yale School of Medicine. Chris' research has pioneered a new focus in dermatology on fundamental biochemistry and structural biology, particularly connecting the atomic resolution mechanisms of action of a therapeutic to its clinical performance and safety.   Sponsored by: LEO Pharma Visit LEO Pharma website for more information. 

In this episode of Crazy Wisdom, Stewart Alsop speaks with German Jurado about the strange loop between computation and biology, the emergence of reasoning in AI models, and what it means to "stand on the shoulders" of evolutionary systems. They talk about CRISPR not just as a gene-editing tool, but as a memory architecture encoded in bacterial immunity; they question whether LLMs are reasoning or just mimicking it; and they explore how scientists navigate the unknown with a kind of embodied intuition. For more about German's work, you can connect with him through email at germanjurado7@gmail.com.Check out this GPT we trained on the conversation!Timestamps00:00 - Stewart introduces German Jurado and opens with a reflection on how biology intersects with multiple disciplines—physics, chemistry, computation.05:00 - They explore the nature of life's interaction with matter, touching on how biology is about the interface between organic systems and the material world.10:00 - German explains how bioinformatics emerged to handle the complexity of modern biology, especially in genomics, and how it spans structural biology, systems biology, and more.15:00 - Introduction of AI into the scientific process—how models are being used in drug discovery and to represent biological processes with increasing fidelity.20:00 - Stewart and German talk about using LLMs like GPT to read and interpret dense scientific literature, changing the pace and style of research.25:00 - The conversation turns to societal implications—how these tools might influence institutions, and the decentralization of expertise.30:00 - Competitive dynamics between AI labs, the scaling of context windows, and speculation on where the frontier is heading.35:00 - Stewart reflects on English as the dominant language of science and the implications for access and translation of knowledge.40:00 - Historical thread: they discuss the Republic of Letters, how the structure of knowledge-sharing has evolved, and what AI might do to that structure.45:00 - Wrap-up thoughts on reasoning, intuition, and the idea of scientists as co-evolving participants in both natural and artificial systems.50:00 - Final reflections and thank-yous, German shares where to find more of his thinking, and Stewart closes the loop on the conversation.Key InsightsCRISPR as a memory system – Rather than viewing CRISPR solely as a gene-editing tool, German Jurado frames it as a memory architecture—an evolved mechanism through which bacteria store fragments of viral DNA as a kind of immune memory. This perspective shifts CRISPR into a broader conceptual space, where memory is not just cognitive but deeply biological.AI models as pattern recognizers, not yet reasoners – While large language models can mimic reasoning impressively, Jurado suggests they primarily excel at statistical pattern matching. The distinction between reasoning and simulation becomes central, raising the question: are these systems truly thinking, or just very good at appearing to?The loop between computation and biology – One of the core themes is the strange feedback loop where biology inspires computational models (like neural networks), and those models in turn are used to probe and understand biological systems. It's a recursive relationship that's accelerating scientific insight but also complicating our definitions of intelligence and understanding.Scientific discovery as embodied and intuitive – Jurado highlights that real science often begins in the gut, in a kind of embodied intuition before it becomes formalized. This challenges the myth of science as purely rational or step-by-step and instead suggests that hunches, sensory experience, and emotional resonance play a crucial role.Proteins as computational objects – Proteins aren't just biochemical entities—they're shaped by information. Their structure, function, and folding dynamics can be seen as computations, and tools like AlphaFold are beginning to unpack that informational complexity in ways that blur the line between physics and code.Human alignment is messier than AI alignment – While AI alignment gets a lot of attention, Jurado points out that human alignment—between scientists, institutions, and across cultures—is historically chaotic. This reframes the AI alignment debate in a broader evolutionary and historical context, questioning whether we're holding machines to stricter standards than ourselves.Standing on the shoulders of evolutionary processes – Evolution is not just a backdrop but an active epistemic force. Jurado sees scientists as participants in a much older system of experimentation and iteration—evolution itself. In this view, we're not just designing models; we're being shaped by them, in a co-evolution of tools and understanding.

In our first episode of 2025, our hosts, Chris Williams and Dave Thompson, have the pleasure of speaking to Fernando Garces, CEO and co-founder of BioGlyph. Leading us from the sunny climes of Portugal through the tropical paradise that is London, on route to the West Coast of the US and A, Fernando shares his evolving love of science through the heights of academia, into industry, and now as CEO of a software company he co-founded to improve the modeling of complex multispecifics. Important questions are unpacked throughout - is London really a tropical paradise? How long can we keep milking podcast episode titles that take advantage of the homophonic property of pharma and farm? Oh, and we have another surprising guest answer to the ‘Deschênes Dilemma'… It is not to quote G. K. Chesterton, a proper nailbiter!

Race Oncology Ltd (ASX: RAC, OTC: RAONF) executive chair Dr Peter Smith joins Proactive's Tylah Tully to discuss the latest updates at the company. It has completed a board renewal process, marking a significant milestone as the company prepares for its next phase of development. The company expressed gratitude to non-executive chair Mary Harney and non-executive director and former CEO/MD Phil Lynch for their four years of service. As part of the renewal, Dr Smith has been appointed as the executive chair, while Dr Daniel Tillett, the current CEO, will also take on the role of managing director. Additionally, Dr Serge Scrofani has joined the board as an independent non-executive director, bringing more than 28 years of experience in the healthcare sector, including key strategic roles at CSL. Dr Scrofani's extensive experience in research, strategy and corporate development, particularly in driving strategic initiatives and executing major mergers and acquisitions, is expected to play a pivotal role in Race Oncology's future. Currently, Dr Scrofani is the principal of Poplar Advisory and he serves on the boards of the Burnet Institute and The Centre for Eye Research. He also holds a PhD in Structural Biology and an MBA from Melbourne Business School. #Proactiveinvestors #RaceOncology #ASX #BoardRenewal, #HealthcareLeadership, #Bisantrene, #CancerTherapies, #ASXNews, #CorporateDevelopment, #StrategicLeadership, #Pharmaceuticals, #HealthcareSector, #BiotechNews, #ExecutiveAppointments, #MedicalResearch, #CorporateStrategy, #HealthcareInnovation #invest #investing #investment #investor #stockmarket #stocks #stock #stockmarketnews

In this episode of The Chain, host Brandon DeKosky, associate professor at MIT, speaks with Andrew Kruse, PhD, professor of biological chemistry and molecular pharmacology at Harvard University, about protein signaling and structural biology. Kruse explains what exactly bias signaling is and discusses the problems he and his team are working to resolve, as well as the tools they use to work out the dynamics of structures. He also shares the findings in signaling receptors and biology that he's most excited about, recent advancements that have caught his attention, and the new directions for him and his lab.  

It's time for another trip around the solar system on the BIGGER and BETTER Science Weekly!  This episode of the Fun Kids Science Weekly we continue our bigger and better podcast where we put YOUR questions to our team of experts, have scientists battle it out for which science is the best & learn all about the discovery of the earliest and most distant galaxy. Dan starts with the latest science news, where we learn about a rare bridled tern and a golden puffin discovered just off the North East coast of the UK, how firefighters are dealing with wildfires in the world's biggest tropical wetlands in Brazil - The Pantanel and Kevin Hainline from the University of Arizona tells us about the earliest and most distant galaxy discovered by the James Webb Telescope called JADES-GS-z14-0. Then we delve into your questions where Dan explains how viruses are cured and we pose Tiffany's question on how the northern lights are formed to Astronomer Tom Kerss. Dangerous Dan continues and we learn all about the Goliath Tiger Fish and why it's so feared in African waters.The Battle of the Sciences continues where Dan chats to Charlotte Dodson from the University of Bath about why Structural Biology is the best kind of science? What do we learn about? - Two rare fish discovered off the coast of the UK - Brazil's Tropical Wildfires - The most distant and earliest galaxy discovered by the James Webb Telescope - How the Northern Lights are formed? - Is Structural Biology the best type of science? All on this week's episode of Science Weekly!Join Fun Kids Podcasts+: https://funkidslive.com/plusSee omnystudio.com/listener for privacy information.

Dr. Daniel Stone speaks with Drs. Mario Mietzsch and Robert McKenna from the University of Florida to discuss a recent article published in the Biomanufacturing in Gene and Cell Therapy special issue of Molecular Therapy Methods & Clinical Development by Drs. Mietzsch, McKenna, and colleagues titled Production and characterization of an AAV1-VP3-only capsid: An analytical benchmark standard. If you enjoy today's conversation, you'll also enjoy the upcoming ASGCT Policy Summit in Washington, DC, September 23-24.  This can't-miss event brings together policymakers and gene and cell therapy experts, including FDA leaders Julie Tierney and Dr. Nicole Verdun, to discuss the latest policies impacting this rapidly evolving field.  Register now at https://www.asgct.org/PolicySummit for invaluable insights on navigating the regulatory landscape.  In This Episode: Dr. Daniel StoneAssociate Editor-in-Chief of Molecular Therapy Methods & Clinical Development and Senior Staff Scientist, Infectious Disease Sciences, Vaccine and Infectious Disease Division at Fred Hutch Cancer Center Dr. Mario MietzschAssistant Scientist, Department of Biochemistry and Molecular Biology, University of Florida Dr. Robert McKennaProfessor and Director of the Center for Structural Biology, Department of Biochemistry and Molecular Biology, University of Florida 'Electric Dreams' by Scott Buckley - released under CC-BY 4.0.www.scottbuckley.com.auShow your support for ASGCT!: https://asgct.org/membership/donateSee omnystudio.com/listener for privacy information.

Roderick MacKinnon won the 1999 Lasker Award for elucidating the structure of potassium channels. His work provided the first molecular description of an ion selective channel and helped knock down what he called “psychological barriers” in the field. After MacKinnon, the structure of transmembrane ion channels went from being seen as unsolvable to solvable. In this 1999 interview with Chris Miller, Professor of Biochemistry at Brandeis University, MacKinnon shares anecdotes from his early career, discusses how the field reacted to his groundbreaking work, and talks about what motivates him scientifically. This interview has been edited for clarity and brevity. Find the full 55-minute interview here: https://laskerfoundation.org/winners/function-and-structure-of-ion-channels/  

Episode 85. Fay Lin is a Senior Editor at GEN Biotechnology. She completed her PhD in Biochemistry, Molecular, and Structural Biology at UCLA and undergrad in Biology at NYU with minors in Computer Science and Chemistry.GEN Biotechnology website: https://home.liebertpub.com/genbioCall for Papers: Special Issue in AI in Biotechnology: https://home.liebertpub.com/cfp/special-issue-artificial-intelligence-in-biotechnology/497/

Du kender helt sikkert Johan Olsen som forsanger i Magtens Korridorer, hertil spiller han teater, arbejder som radiovært, og han har også udgivet flere bøger. Men som om det ikke var nok, så er Johan Olsen også forsker. Nærmere bestemt er han ansat som adjunkt på Biomolecular Science ved afdelingen Structural Biology and NMR Laboratory ved Københavns Universitet. Men hvad er det egentligt hele Danmarks Johan Olsen forsker i, når han ikke underholder på scenen eller formidler sin viden og tanker i radiostudiet og gennem sine bøger? Hvordan skruer han en hverdag sammen, hvor han kan have så mange forskellige hatte på? Og hvad er det fedeste ved at være både musikstjerne og kæmpe biologi-nørd? Medvirkende: Johan Olsen. Værter: Peter Løhde & Andrew Davidson.See omnystudio.com/listener for privacy information.

This episode, we discuss how to advance from probiotic mechanisms to human applications, with Prof. Graciela Lorca PhD at the University of Florida in Gainesville, USA. Prof. Lorca talks about her experiences seeking out the mechanisms of action of a probiotic – including which molecules from bacteria may have beneficial effects – and bringing a probiotic through drug trials for use in Type 1 diabetes. They also discuss probiotic responders versus nonresponders and how dietary intake may provide clues about who will respond to an intervention. Key topics from this episode: Prof. Lorca's lab is primarily concerned with discovering the mechanisms of action of specific probiotics, including the molecules they produce that can have beneficial effects on a host. Knowing how a probiotic works allows scientists to select strains that are likely to be effective for a certain application. Prof. Lorca's lab found that L. johnsonii produces extracellular vesicles (EVs) and that a few proteins carried in these EVs may be important markers of where and how they affect the host. She triggered antibodies against these proteins, allowing them to be tracked in the host. EVs are small protrusions from the bacterial membrane, and only some bacteria produce them. Evs have complex cargo, which mostly represents the metabolic state of the cell. Prof. Lorca studied bacteria that appeared to affect autoimmunity in animal models. In humans, administering these bacteria changed immune markers; this intervention is now in a Phase II trial with humans who have Type 1 diabetes. The bacteria may be acting in the small intestine, but they don't colonize permanently. Extensive data on safety were required to advance the probiotic through to a Phase II trial. Although administering EVs could be an even safer approach, they are difficult to purify from bacteria. Prof. Lorca continues to investigate the bioactive components of these EVs to perhaps administer only those components. Prof. Lorca is also interested in responders versus nonresponders to a probiotic intervention. One of her clinical trials showed that people had either high lactic acid bacteria (LAB) or low LAB at baseline. For those with high levels of LAB, the levels didn't change much over time. But for those with initially low levels of LAB, the levels increased over time. The latter responded better to treatment. Furthermore, people with high LAB were shown to consume a diet with more long-chain fatty acids, which LAB can utilize. Overall, dietary intake may be a key part of uncovering responders and nonresponders. Over the next ten years in this field, Prof. Lorca believes we will be able to increasingly personalize probiotics according to someone's genetics and dietary intake. Regulatory aspects are complicated but continue to evolve. Episode links: Article on how bacterial EVs may have effects on type 1 diabetes: Internalization of extracellular vesicles from Lactobacillus johnsonii N6.2 elicit an RNA sensory response in human pancreatic cell lines Tracking bacterial EVs in the host: Identification of Biomarkers for Systemic Distribution of Nanovesicles From Lactobacillus johnsonii N6.2 Clinical trial showing feasibility of using a probiotic strain in applications related to type 1 diabetes: Lactobacillus johnsonii N6.2 Modulates the Host Immune Responses: A Double-Blind, Randomized Trial in Healthy Adults Using diet to predict levels of bacteria in the gut: Identification of food and nutrient components as predictors of Lactobacillus colonization Master of Science degree at University of Florida: Microbiome in Health and Disease Additional resources: New paper outlines the value of studying probiotics in the small intestine. ISAPP blog. About Prof. Graciela Lorca PhD: Dr. Graciela Lorca is currently a Professor in the Department of Microbiology and Cell Science at the University of Florida. She completed her Licentiate in Genetics studies at the National University of Misiones and later received her doctoral degree in Food Technology at the National University of Tucuman in Argentina. She completed her postdoctoral studies at the University of California San Diego in Molecular Microbiology and at the University of Toronto in Structural Biology and Gene Regulation. Since joining the Department of Microbiology and Cell Science at the University of Florida in 2007, Dr. Lorca has focused on the identification of environmental signals that modulate host-microbe interactions. Using multiomic approaches, her laboratory is investigating the bacterial components such as extracellular vesicles that target host pathways involved on those beneficial interactions in vitro and in vivo. Furthermore, Dr. Lorca's laboratory is currently conducting human trials to evaluate the use of Lactobacillus johnsonii Type 1 Diabetes patients. Dr. Lorca currently teaches a graduate and undergraduate level Probiotics course. She is also in charge of the new concentration on Microbiome in health and disease within the Online Master program at Department of Microbiology and Cell Science.

Caezar Al-Jassar is the director at Alley Cat Games and heads of product development and business strategy. He originally started out as a postdoctoral academic working at British universities and institutions such as Birmingham University, UCL and the LMB with multiple first author papers in the field known as “Structural Biology”. He started Alley Cat Games, which he co-founded with his wife, back in 2016 with the successful Kickstarter campaign for their inaugural game: Lab Wars. With over 80 skus since then and games in high street stores such as Barnes and Noble and Waterstones, Caezar likes to ensure that everything they do is of the highest quality bursting with theme and fun. Alley Cat Games features games that are mass market friendly all the way up to complex hobby games so they're always something in their line compatible with any type of gamer!You can learn more about Alley Cat Games at alleycatgames.comFOLLOW US ON: Facebook: https://www.facebook.com/groups/boardgamebingeInstagram: https://www.instagram.com/boardgamebingepodcast/ Twitter: https://www.twitter.com/boardgamebingeWHERE TO FIND OUR PODCAST:Spotify: https://open.spotify.com/show/5RJbdkguebb3MSLAatZr7riHeart Radio: https://www.iheart.com/podcast/269-board-game-binge-72500104/Tune In: https://tunein.com/embed/player/p1344218/Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5jYXB0aXZhdGUuZm0vYm9hcmRnYW1lYmluZ2U=Apple Podcasts: https://podcasts.apple.com/ca/podcast/board-game-binge/id1522623033Visit Our Websites: Board Game Binge: https://boardgamebinge.com/Tin Robot Games: https://tinrobotgames.comElixir Board Games: https://www.elixirboardgames.com/our-gamesBoard Game Design Course: https://boardgamedesigncourse.com/

This podcast format is inspired by the Charles Dicken's novella “A Christmas Carol” whereby Phil Jeffrey (Bicycle Therapeutics), Beth Williamson (UCB) and Daniel Price (Nimbus) discuss DMPK Past, Present and Future with Scott.  Key elements discussed include the evolution of DMPK as a predictive discipline that links helps the chemistry of drug design with the biology of drug disposition and effect.The episode addresses the following questions:The founding names and principles of DMPK and the emergence of Discovery DMPKToday's use of technologies and in silico modelling and DMPK's role in the 3 pillars/5Rs and PK/PD The outlook for better prediction with advances in AI/MLSpeakers:Phil Jeffrey - Senior Vice President of Preclinical Development at Bicycle TherapeuticsPhil Jeffrey is now semi-retired after contributing to DMPK for over 35 years in the pharmaceutical industry.  Most recently, Phil was Senior Vice President of Preclinical Development at Bicycle Therapeutics and previously has held positions at Pfizer, GlaxoSmithKline, SmithKline Beecham and The Upjohn Company.  Phil is a longstanding contributor the Society for Medicines Research and is an Honorary Professor at the William Harvey Research Institute, Queen Mary University of London.  Phil has a wide breadth of experience across DMPK from, Drug Discovery and early lead optimisation through to clinical proof of mechanism and proof of concept across a wide variety therapeutic areas and drug modalities and made significant contributions to the advancement of understanding the CNS penetration of drugs into the brain. Beth Williamson - Head of ADME in the DMPK group at UCBBeth graduated with a PhD in Pharmacology from the University of Liverpool and is now Head of ADME in the DMPK group at UCB where she also represents DMPK on projects throughout discovery and development. Beth's work has focussed on in vitro and in vivo ADME assay optimisation and validation within drug discovery, particularly to address bespoke questions. Beth has worked in the fields of oncology, neurology and immunology. Her main research interests include drug-drug interactions, extrapolation of pre-clinical in vitro and in vivo data for the prediction of human pharmacokinetics and application of AI/ML approaches within DMPK.  Daniel Price - Vice President of Computational Chemistry & Structural Biology at Nimbus TherapeuticsDr. Daniel Price is Vice President of Computational Chemistry & Structural Biology at Nimbus Therapeutics, where he leads a team of internal and external scientists focused on delivering breakthrough medicines through structure-based design, leveraging both physics-based and knowledge-based predictive modeling.  Before joining Nimbus, he spent 16 years at GlaxoSmithKline, where he led a team of computational chemists and data scientists across diverse areas of structure- and ligand-based drug design, high-content screening analytics, predictive ADME, predictive synthesis, and chemogenomics.  He has led drug discovery programs, contributed to 4 clinical candidates, led the development of GSK's first generation R&D data lake, and authored/co-authored 26 publications.  Dr. Price received his undergraduate degree in chemical engineering from University of Colorado at Boulder followed by his Ph.D. in Molecular Biophysics & Biochemistry from Yale University with Prof. Bill Jorgensen.   He completed an NIH postdoctoral fellowship with Prof. Charlie Brooks, III at The Scripps Research Institute prior to joining GSK. Stay tuned for more podcasts in our Pharmaron DMPK Insights Series!

Ion channel block unraveled Transcript of this podcastHello and welcome to the NanoLSI podcast. Thank you for joining us today. In this episode we feature the latest research by Takashi Sumikama at the Kanazawa University NanoLSI in collaboration with Katsumasa Irie from Wakayama Medical University and colleagues.The research described in this podcast was published in Nature Communications in July 2023 Kanazawa University NanoLSI websitehttps://nanolsi.kanazawa-u.ac.jp/en/Ion channel block unraveledResearchers at Kanazawa University report in Nature Communications how calcium ions can block sodium ion channels located in cell membranes. Structural analysis and computer simulations made it possible to identify where and why calcium ions get stuck. Ion channels are structures within cell membranes that enable specific ions to travel to and from the cell. Such transfer is essential for a variety of physiological processes like muscle cell contraction and nerve excitation. In so-called tetrameric cation channels, the ion selectivity results from the unique structural and chemical environment of the part referred to as the selectivity filter, which is located between two intertwined helical structures. Tetrameric ion channels are prone to ‘divalent cation block', the blocking of the channel by ions like calcium (as in Ca2+). Such blocking regulates the ionic current, which is involved in various neural activities such as memory formation. How divalent cation block happens exactly is still unclear at the moment — in particular, a direct observation of the cation actually blocking the ion pathway has not been reported yet. Now, Takashi Sumikama from Kanazawa University in collaboration with Katsumasa Irie from Wakayama Medical University and colleagues has discovered the mechanism behind divalent cation block in NavAb, a well-known tetrameric sodium (Na) channel. Through structural analysis and computer simulations, the researchers were able to reveal the relevant structural features and molecular processes at play.So how did they go about this structural analysis?NavAb is a sodium channel cloned from a bacterium (Arcobacter butzleri) and has a well-known structure. Sumikama and Irie's colleagues performed experiments with NavAb and three mutants. The structures of the mutants were determined for environments with and without calcium. The scientists focused on the differences in electron densities for the different structures, as these provide insights into the locations of the calcium ions. They found that for the mutants displaying calcium blocking, one or two calcium ions are located at the bottom of the selectivity filter. They also discovered that two other divalent cations — magnesium (as in Mg2+) and strontium (Sr2+) ions — blocked the calcium-blocking mutant sodium channels.The researchers then performed computer simulations to obtain a detailed understanding of the interaction between the calcium ions and the mutated NavAb channels. The simulations reproduce the dynamics of ions passing — or not passing — the channel. In the absence of calcium ions, sodium ions were observed to penetrate the channel. In the presence of calcium ions, penetration significantly decreased in the calcium-blocking mutants. The simulations also confirmed that the blocking calcium ions are ‘stuck' at the bottom of the selectivity filter, and revealed that this ‘sticking' is related to the increased hydrophilicity (affinity to water) of relevant structural parts of the mutated channels.The results of Sumikama and Irie's colleagues provide an important step forward towards a full understanding of the mechanism of divalent cation block in NavAb, an important and representaNanoLSI Podcast website

Today I am speaking with Dr. Amy Kruse.She is a neuroscientist, General Partner, and Chief Investment Officer at Satori Neuro, a venture capital fund focused on mental health, neurotechnology, and human flourishing.Amy earned a Bachelor of Science in Cell and Structural Biology and a Ph.D. in Neuroscience from the University of Illinois at Champaign-Urbana. She went on to serve as a program manager at the Defense Advanced Research Projects Agency (DARPA), where she oversaw the agency's first performance-oriented neuroscience programs and orchestrated scientific breakthroughs in augmented cognition, accelerated learning, optimized imagery analysis, team neurodynamics, and neuromodulation.After DARPA, she served as the Vice President and Chief Technology Officer of Cubic Global Defense and Chief Scientific Officer of Optios.Prior to joining Satori Neuro, Dr. Kruse was a General Partner at Prime Movers Lab, where she led the fund's life sciences investments in human augmentation and longevity.In this episode, we talk about:* The neuroscience of learning;* How to improve human performance and mastery;* The connection between meditation and psychedelic research; and* The varieties of neurotechnology.Listen to the episode on Substack, Spotify, Google, or Apple.Credits:* Hosted by Zach Haigney * Produced by Zach Haigney, Erin Greenhouse, and Katelin Jabbari* Find us at thetripreport.com* Follow us on Instagram, Twitter, LinkedIn and YouTube* Theme music by MANCHO Sounds, Mixed and Mastered by Rollin Weary This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.thetripreport.com

Kanazawa Univesity NanoLSI Podcast:Dynamic 3D structure extraction from HS-AFM imagesTranscript of this podcast Hello and welcome to the NanoLSI podcast. Thank you for joining us today. In this episode we feature the latest research by  Holger Flechsig and Toshio Ando at the Kanazawa University NanoLSI. The research described in this podcast was published in the journal Current Opinion in Structural Biology in April 2023 Kanazawa University NanoLSI website  https://nanolsi.kanazawa-u.ac.jp/en/ Dynamic 3D structure extraction from high-speed atomic force microscopy images  By allowing the direct observation of biomolecules in dynamic action, high-speed atomic force microscopy or AFM has opened a new avenue to dynamic structural biology. A vast number of successful applications within the past 15 years have provided unique insights into essential biological processes at the nanoscale – visualizing, for example, how molecular motors execute their specific functions. Some intrinsic limitations of AFM imaging are that only the surface topography can be acquired, and that the AFM tip is too large to resolve details below the nanometer scale. To facilitate the interpretation and understanding of high-speed AFM observations, post-experimental analysis and computational methods play an increasingly important role. In their review paper published in the Current Opinion in Structural Biology journal Holger Flechsig a computational scientist at the NanoLSI at Kanazawa University and Toshio Ando, a Distinguished Professor at NanoLSI, provide an overview of developments in this topical field of interdisciplinary research. Computational modeling and simulations already allow the reconstruction of 3D conformations with atomistic resolution from topographic resolution-limited AFM images. Furthermore, quantitative analysis methods allow for example automated recognition of biomolecular shape changes from topographic images, or feature assignment including the identification of amino acid residues on the molecular surface.So how is all this implemented?The developed computational methods are often implemented in open-access software, allowing for convenient applications by the broad Bio-AFM community to complement experimental observations. In that regard, the BioAFMviewer software project initiated at Kanazawa University in 2020 has gained significant attention and plays an important role in a plethora of collaboration projects.Combining high-speed AFM and computational modeling will elevate the understanding of how proteins function in atomistic detail. An ambitious future goal is the application of molecular modeling to reconstruct atomistic-level 3D molecular movies from high-speed AFM topographic movies.ReferenceHolger Flechsig and Toshio Ando. Protein dynamics by the combination of high-speed AFM and computational modelingNanoLSI Podcast website

This talk explores the fascinating world of crystals and their role in biology and medicine. Crystals have always been admired for their beauty and value, and many believe they have powerful, magic properties. They play critical roles in various biological processes, such as supporting bones, grinding food, protecting shells, and more. However, crystals can also lead to diseases like kidney stones, atherosclerosis, and gout. The speaker will explain the study of crystal formation, assembly, and their impact on health and disease. Speakers Lia Addadi, Professor of Structural Biology, The Weizmann Institute of Science

There have been a lot of stories in the news over the last few months about AI chatbots like ChatGPT that can respond to your questions with convincing and well-written answers. These so-called large language models can tell you how to build a treehouse, how to bake a cake, or how to sleep better. But notice that word large. Behind the scenes, these models have learned which word tend to cluster together by sifting through hundreds of billions of pieces of data—basically the entire Internet, in the cast of ChatGPT, including all of Wikipedia and thousands of published books. Now imagine that another chatbot came along that could learn how to generate convincing text response by studying only, say, 18 sentences. Something like that is what this week's guest Raphael Townshend, the founder and CEO of Atomic AI, has accomplished when it comes to predicting the structure of RNA molecules.RNA has been in the news a lot lately too. That's in part because some of the vaccines that helped us beat back the coronavirus pandemic were made from messenger RNA, a form of the molecule that instructs cells how to build proteins (in that case, antibodies to the virus). But RNA has many other functions in the body, and if we knew how to design small-molecule drugs to attach to binding pockets on any given RNA to interrupt or modulate its functions, it could open up a whole new realm of medical treatments. The problem is, if all you know about an RNA molecule is its nucleotide sequence, it's very hard to predict where those binding pockets might be and what kind of drug might fit into them. As a PhD student at Stanford, Townshend designed a deep learning model to tackle that problem. The model, called ARES, started with a proposed structure for an RNA molecule with a known nucleotide sequence, and predict how that proposal would compare to real-world data. ARES turned out to be stunningly accurate, and it acquired its skills by studying a remarkably small training set: just 18 examples of RNAs with known structures. So in a way, it was using the power of small data, together with a bit of physics. Now Atomic AI is building on that original model to create an engine for discovering new small-molecule drugs that could potentially interrupt any disease where RNA is a player.For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:1. Open the Podcasts app on your iPhone, iPad, or Mac. 2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.3. Scroll down to find the subhead titled "Ratings & Reviews."4. Under one of the highlighted reviews, select "Write a Review."5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.7. Once you've finished, select "Send" or "Save" in the top-right corner. 8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out. 9. After selecting a nickname, tap OK. Your review may not be immediately visible.That's it! Thanks so much.

Dr. Hao Wu is the Asa and Patricia Springer Professor of Structural Biology at Harvard Medical School. Her lab focuses on the molecular and cellular mechanisms that govern the assembly, regulation, and therapeutic intervention of supramolecular complexes in innate immunity. She talks about cryo-electron microscopy and how her team used it to study the structures of the NLRP3 inflammasome disc, the B cell antigen receptor, and the Gasdermin D pore. She also discusses the role of AlphaFold in structural biology research.

Over the last two decades, cryogenic electron microscopy (cryo-EM) has transformed from what Dr. Andrew Ward calls the “outcast of structural biology” to one of the most promising technologies in the field. Ward, professor of integrative structural and computational biology at Scripps Research Institute, speaks with moderator Brandon DeKosky, assistant professor of chemical engineering at the Massachusetts Institute of Technology, about the evolution of cryo-EM and how its direct detector transformative technology enables scientists to craft favorable antibody responses. Ward also talks about cryo-EM's technological advantages when working with proteins, sterilizing immunity, and designing accurate structural biology pipelines that lead to next-generation vaccines. Finally, Ward offers his predictions about the immunological breakthroughs he thinks structural biologists will accomplish in the very near future.  Links from this episode:  Scripps Research Institute PepTalk Conference Discovery on Target Conference 

Episode 65. Tari Suprapto is a Director of Search and Evaluation at Novo Nordisk and Board Chair at the San Diego Innovation Council. Tari completed her PhD at The Rockefeller University in Cellular and Structural Biology and has worked in technology transfer and business development at The Rockefeller University and Salk Institute.

In this month's episode of the Psychedelics in Medicine Podcast (PiMPOD), Dr Torsten Passie and Ben Clayden discuss LSD Derivatives, A nearly identical molecule with just a few differences. We discuss multiple derivatives of LSD, including LSA, 2-Bromo-LSD (BOL-148), 1P-LSD and use them in combination to better understand the intricacies of the neural mechanisms of LSD, and how slight changes to a molecules three-dimensional structure can completely alter their effects, through differences in ligand-receptor binding.We then look at a study using a non-hallucinogenic version of LSD, BOL-148 as preventative treatment for cluster headache, discussing its implications, and further research that could be completed relating to LSD derivatives.Dr Passie is a German psychiatrist, professor at Hannover Medical School and is an expert in altered states of consciousness. Torsten has performed clinical and experimental studies numerous psychoactive and psychedelic compounds ranging from nitrous oxide, to MDMA to ketamine.Ben Clayden is the creator and owner of this Podcast. He is a student at the University of York studying Natural Sciences specialising in Neuroscience. He is co-chair for the Drug Science Student Society Network as well as the president of his University's Psychedelics in Medicine Society.Psychedelics, LSD, Hallucinogens Biology, Chemistry, Structural Biology, Neuroscience Cluster Headaches,Useful LinksINSTAGRAM - https://www.instagram.com/psymedpod/LINKTREE - https://linktr.ee/psymedpodRSS - https://rss.com/podcasts/psymedpod/ReferencesKarst M, Halpern JH, Bernateck M, Passie T. The non-hallucinogen 2-bromo-lysergic acid diethylamide as preventative treatment for cluster headache: an open, non-randomized case series. Cephalalgia. 2010 Sep;30(9):1140-4. doi: 10.1177/0333102410363490. Epub 2010 Mar 26. PMID: 20713566.

Winds of change are howling in Germany, with the draft healthcare bill now approved to stabilise SHI fund finances. What will manufacturers, with innovative orphan drugs and cell and gene therapies, launch strategies be? With the latest decision from Janssen to avoid the German market altogether for x2 Rare Oncology innovative drugs, will this be a trend we are likely to see continue? Join Stefan Walzer & Fisentzos Stylianou discuss the new bill, in regards to the biggest changes that impact orphan drug (OD) manufacturers. Will OD manufacturers still see Germany as the first go to market within Europe and what does this means for rare disease patients? Will there be delays to new treatments or will manufacturers decide not to launch in Germany at all to protect the price of their new drug? We will be discussing this and so much more! If you are a drug manufacturer planning your launch strategy, this podcast is for you! Presenters: Georgie Rack & Owen Bryant Guests Stefan Walzer, CEO President & Founder of MArS Market Access & Pricing Strategy GmbH Fisentzos Stylianou, senior analyst and P4A's German country expert Dr Stefan Walzer, CEO President & Founder at MArS Market Access &Pricing Strategy GmbH https://marketaccess-pricingstrategy.de/en/ Bio & Fun Facts 1) Economist, PhD in health economics, diploma in clinical trials 2) Experience in MA, reimbursement, HE and pricing since 2004 in consultancy and industry 3) Founder and CEO of MArS - THE D-A-CH market access consultancy. Linked to that also co-founder of SMS2DACH including the full spectrum for D-A-CH support (distribution, management, launch, etc.): www.sms2dach.com 4) Founder of P&N (pricing-and-negotiations.ca) with a focus on negotiations across the world including being the co-founder of www.thenegotiationlab.com 5) Member of a European network Tesseract (https://www.tesseracteurope.com/) which can serve companies moving from outside Europe into Europe including not only reimbursement services but also logistics, customs, etc. 6) Teaching at various German universities 7) Love spending time with my family, being a soccer coach of under 14 years-old and being a supporter of Borussia Dortmund Fisentzos Stylianou, Senior Analyst & German Country Expert Fisentzos role at Partners4Access includes conducting primary and secondary research to support the development of market access and reimbursement strategies for clients in the pharmaceutical and biotechnology industries. With a passion for innovative treatments, he closely follows the cell and gene therapy field as it expands to treat more patients with rare diseases. Prior to joining Partners4Access, Fisentzos worked as a Research Associate at Imperial College London, where he also earned his Ph.D. in Structural Biology in 2020. During this time, he conducted research as part of a multidisciplinary team to advance his understanding of the structure and function of biofilm-forming proteins, paving the way for the design of novel antimicrobial therapeutics. Fisentzos also holds an M.Sc. in Biomedical and Molecular Sciences Research and a B.Sc. in Biomedical Science, both from King's College London.

ABOUT TODAY's EPISODE Is your hair stuck at a particular length for long periods, or worse, do you have regression in your hair growth? Are you using all the "how to grow hair fast methods" and yet, you somehow fall short. If so, you want to pay close attention to today's episode where I highlight hair structure focusing on African hair structure and how it ties to hair porosity and cuticle preservation. SUPPORT! Thank you SO much for your constant support. It means the world to me. If you are wondering how you can support, please see below Share share share to the ends of the world: You can share this podcast to as many people as you know and love. You does not love a fun podcast about hair and skin? See links for that here https://linktr.ee/ChiomaAgha Support financially: https://anchor.fm/chioma-agha Download: Do you know that you can download any episode? Take me (well my voice, LOL) anywhere you are. Just click on download, and voila! Listen: Errmmm listen listen and listen again. You can listen one zillion uncountable billion times. LOL. Listen to the Ads ooooooo. T for Tenks REFERENCES 1. Hessefort, Yin et al. “True porosity measurement of hair: a new way to study hair damage mechanisms.” Journal of cosmetic science vol. 59,4 (2008): 303-15. 2. Sandra L. Koch, Mark D. Shriver, Nina G. Jablonski. Variation in human hair ultrastructure among three biogeographic populations. Journal of Structural Biology, Volume 205, Issue 1, 2019, Pages 60-66, ISSN 1047-8477. Disclaimer The information provided on this podcast, including but not limited to, text, graphics, are for informational purposes only and does not serve as a diagnosis, treatment or substitute for medical advice. Always seek the advice of a qualified health care professional for any medical condition you may be experiencing. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/chioma-agha/support

 we're joined by Dr. Andrey Kovalevsky, Senior Scientist in Structural Biology & Biochemistry at Oak Ridge National Laboratory. Andrey and his team used neutrons and x-rays to map part of the internal structure of the coronavirus to create an accurate 3-D model. Specifically, the scientists mapped the main protease (Mpro), an enzyme involved in the virus replication, to which they had added a preliminary small molecule discovered using high-speed computer screening and virtual reality (VR). Using the MedChem tool in Nanome to look at the enzyme model, the scientists virtually constructed different small molecules by modifying their structures to see if any newly designed compounds could fit, or bind, to a key site on the Mpro enzyme surface. A strong enough binding could inhibit, or block, the enzyme from functioning, which is vital to stopping the virus from multiplying in patients with COVID-19. References: Kneller et al. (2021). Structural, Electronic, and Electrostatic Determinants for Inhibitor Binding to Subsites S1 and S2 in SARS-CoV-2 Main Protease. J. Med. Chem. 64, 8, 4991–5000. DOI: 10.1021/acs.jmedchem.1c01475 https://neutrons.ornl.gov/content/jou...https://www.pfizer.com/news/press-rel...https://www.rcsb.org/structure/7SI9

On Episode 31 of Black in Science, I sat down with Dr. Jamaine Davis who currently works as an Assistant Professor in the Department of Biochemistry and Cancer Biology at Meharry Medical College. To open, Dr. Davis discusses his childhood while growing up in Long Island, New York. He then segues into his experience as an undergraduate chemical engineering major at Temple University and Drexel University in Philadelphia, Pennsylvania before discussing his transition into the biomedical research field. After sharing the details of his Ph.D dissertation research in the Department of Biochemistry and Molecular Biophysics at the University of Pennsylvania, Dr. Davis delves into the work he did for both of his postdoctoral fellowships at the National Cancer Institute in Maryland. Following this, Dr. Davis describes the Breast cancer and Alzheimer's structural biology and health disparities research his lab focuses on before disclosing his short term and long term goals. To conclude, Dr. Davis shares his feelings on the importance of seeking help, remaining your authentic self and more. If you've enjoyed listening to Dr. Davis' episode of the podcast and wish to contact him with questions, feel free to reach out via: Email: jdavis@mmc.edu Twitter: @jscdavis

東大先進科学機構の加藤英明さん(@emeKato)をゲストに、今回のChRmine構造論文やこれまでの仕事の背景、神経科学者が構造生物学について知っておくと良いこと、今後の構造生物学の展開等を伺いました。(2/20収録) Show Notes(完全版は番組HP): 加藤研 加藤さん過去インタビュー 1 2 ChRmineの論文 濡木研 Brian Kobilkaラボ Brianノーベル賞のページ 2012年のNature論文（ChR1とChR2のキメラ、C1C2の構造解析） の新着論文レビュー 服部素之さん タンパク質結晶化の基礎と蒸気拡散法の解説 (pdf注意) 芳賀先生のNature 2012 （註：だいぶ年が間違ってましたね・・・ by 加藤さん） 脂質キュービックフェーズ（LCP）法 （pdf注意） Nanobodyを使ったBrianたちの論文 iC++の構造論文（Nature筆頭論文その３） GtACR1の構造論文（Nature筆頭論文その４） 上記論文の新着論文レビュー 微生物型ロドプシンの作動メカニズムについての神取先生の解説記事 加藤先生のアドバンスト理科 Kalium rhodopsins クライオ電顕の原理の解説（吉川研） David Juliusが取ってた NTSR1-Gi1のCanonical と Non Canonicalクライオ構造論文（N筆頭その５） AlphaFold2論文 死の脳内表象について(pdf注意) Yusteがやってたoptogeneticsのonline meeting質問のシーン KR2の構造解析（N筆頭その２） の新着論文レビュー 神取研 木暮先生 2013年のNature Communicationsの論文 吉澤研 Editorial Notes: なんか一人でベラベラ話していて、聞き直してみると非常に恥ずかしいですね。。穴があったら入りたい気分です。あと、ビタミンD不足で病院のお世話になった時は我ながらビックリしました。構造解析の比重は減らすと言いましたが、普通の構造解析の比重は減らす一方、dynamicsやin situでの構造解析は今後も続ける予定です（加藤） フェイク修論発表→本丸ねーちゃー、太陽の降り注ぐかりふぉーにゃでVitD不足、などなど、ついったー経由で仕入れていた武勇伝に関して突っ込むのを失念しておりやらかしたー (萩原) 思えば分子生物学にハマったのはK+チャネルのイオン選択性フィルターがK+より小さいNa+イオンを透過させない仕組みに感動したのがきっかけでした(宮脇)

Ben Oakes is Co-founder, President, & CEO @ Scribe Therapeutics. He has contributed to over 25 publications and patent applications across synthetic biology, molecular engineering, CRISPR, and zinc finger-based genetic modification. A previous Innovative Genomics Institute Entrepreneurial Fellow, Ben has been named to the San Francisco Business Times 40 Under 40, Endpoints 20 Under 40 in biopharma, Business Insider 30 Under 40 transforming healthcare, and the Biocom Life Sciences Catalyst Awards. He received a PhD in Molecular and Cellular Biology from the University of California, Berkeley, where he worked in the Doudna Lab and Savage Lab developing CRISPR-Cas9 molecules with enhanced characteristics.Dave Savage is Co-founder & Scientific Advisor @ Scribe Therapeutics. He is an Associate Professor of Biochemistry, Biophysics, and Structural Biology at the University of California, Berkeley and in 2021 was selected as an Investigator of the Howard Hughes Medical Institute. Dave is an expert in biochemistry and protein engineering, and his laboratory develops novel tools for studying and manipulating the genome. Previously, he was a Department of Energy Physical Biosciences Fellow of the Life Sciences Research Foundation at Harvard Medical School.Thank you for listening!BIOS (@BIOS_Community) unites a community of Life Science innovators dedicated to driving patient impact. Alix Ventures (@AlixVentures) is a San Francisco based venture capital firm supporting early stage Life Science startups engineering biology to create radical advances in human health.Music: Danger Storm by Kevin MacLeod (link & license)

Ray Stevens, CEO of ShouTi, on structural biology driven drug discovery.

The journey to understanding these critically important molecules, in their thousands of different flavors, began with a chance discovery. Today, after decades of painstaking lab work and dizzying technological leaps, the field of protein science is exploding.

Today's guest is Jasmine Cubuk, a 5th year PhD Candidate in the Biochemistry, Biophysics, and Structural Biology program at WashU. Jasmine currently studies intrinsically disordered proteins. In an attempt to advance research in regards to the current pandemic, Jasmine's lab work is focused on the SARS-CoV-2 Nucleocapsid protein. In this conversation, Jasmine shares tons of information about her introduction to STEM and road completing the PhD. Jasmine believes that science is for everyone, and loves seeing more women in the field. --- Support this podcast: https://podcasters.spotify.com/pod/show/theglamchemist/support

Three new studies shed more light on the Omicron variant of coronavirus suggesting the risk of hospitalisation is lower than with previous variants. But there are still questions to be answered, says James Naismith, Professor of Structural Biology at the University of Oxford, about how quickly it can spread and for how long – something that will have a huge impact on recovering economies. The price of liquified natural gas is spiking around the world to around eight times what it was earlier in the year. Anne-Sophie Corbeau at the Center on Global Energy Policy at Columbia University tells us why, and Nikos Tsafos at the Center for Stretegic and International Studies in Washington, DC, explains how Chinese demand for energy is contributing to rising prices. Apple's investors want to investigate the company's behaviour in China, including allegations of forced labour in the supply chain, and they might just get that: the US financial regulator has blocked Apple's proposals to prevent shareholders from demanding those reports – Patrick McGee of the Financial Times tells us more. The BBC's Elizabeth Hotson looks at the future of high street retail, and in Hong Kong, authorities have removed a statue commemorating the Tiananmen Square massacre under cover of darkness. throughout the programme we're joined by Samson Ellis - Taipei Bureau Chief Bloomberg News and Diane Brady, Assistant Managing Editor of Forbes. Picture: A coronavirus poster on a phone box Credit: Andrew Milligan/PA Wire

In this episode of the Mother Plus Podcast, we cover:When a career change upended Mandy's identity, she used her background in both biochemistry and mindset to create a business supporting the mental health of  prospective medical students who are preparing for the MCAT. Why and how Mandy made a professional pivot during the pandemic. What happens when the right thing for your family isn't the best for your career? Mandy shares her guilt-free perspective and why work makes her a better mom. Honor your motherhood style: why a “one size fits all” approach doesn't work when it comes to parenting choices. Small, incremental steps are the key to success: Be authentic and follow your gut when starting your business-- you do not have to do what everyone else is doing or have everything perfect.Mandy Siglin has a lot of titles: Mother to three + PhD, yoga instructor and mindset coach. Siglin received her PhD in Molecular Pharmacology and Structural Biology from Thomas Jefferson University.  She lovingly served as the Director of Juniata College's Health Professions Program for five years. When family circumstances took her away from her Director position right before the pandemic hit, she had to make a pivot. Inspired by her ability to change the students she worked with in the past with her unique science and mindset perspective, she founded ThiveMed, a company dedicated to the wellbeing of pre-med students. Mandy uses strategic, evidence-based practices to help students increase their confidence and lower their stress and anxiety around standardized entrance exams for admission to professional school. Mandy lives in North Carolina with her husband, Josh, and three children.Where you can find her:Instagram: https://www.instagram.com/thrive.med/LinkedIn: https://www.linkedin.com/in/mandy-siglin-a783955a/

Adam is CEO of Visby Medical (formerly Click Diagnostics), which he founded in 2012 to develop easy-use diagnostic tests. In March 2020, the company pivoted from work on a sexual health test to tackle the coronavirus, and in July received FDA emergency authorization approval for its portable COVID-19 test. He is also an associate professor in the Departments of Structural Biology and Electrical Engineering at Stanford University School of Medicine, with research interests spanning a broad field of molecular imaging. His lab is developing new optical imaging tools with applications to cancer and ophthalmic diseases, such as age-related macular degeneration.

Tuba B. from the University of Ottawa talks to Dr. Joanne Lemieux a Professor of Structural Biology at the University of Alberta in the Faculty of Medicine & Dentistry. Most recently, during the COVID-19 pandemic, as the principal investigator on the study, Dr. Lemieux worked to develop an antiviral drug against coronavirus disease. In this interview, Dr. Lemieux speaks on her discovery of antiviral protease agents that interfere with SARS-CoV-2, the chemical design process of drug development, and her perspective on the future of antiviral pills. Learn more: https://lemieuxlab.biochem.ualberta.ca/ 

You may have heard of Watson and Crick and their breakthrough of The Double Helix (The Structure of DNA) utilizing Sir Lawarence Bragg's X-Ray diffraction methodology in crystallography. There's an architecture in the field of Structural Biology related to DNA that's absolutely fascinating…a breakthrough that scientists such as Perutz, Kendrew, Hodgkin have already pioneered into. But have you heard of R2 RNA? Have you heard of splicing RNA for recodification…that there's an ability to reprint by blueprint a repair of the structure of RNA? In this short expanded episode from the PH10 52. TRANSFIGURATION AD II event listen in as Vickery off the cuff gives a theoretical account of how and possibly why “R2 RNA” was brought out of the encounter Moffitt had. The most immediate proof of the compatibility of religion (qualitative) and natural science (quantitative) under the most thorough critical scrutiny, is the historical fact that the very greatest natural scientists of all times—men such as Kepler, Newton, Leibniz—were permeated by a most profound religious attitude. Max Planck Both religion and science require a belief in God. For believers, God is in the beginning, and for physicists He is at the end of all considerations… To the former He is the foundation, to the latter, the crown of the edifice of every generalized world view. - Scientific Autobiography and Other Papers as translated by F. Gaynor (1949), p. 184 Glorification | The Final Frontier Going Boldly Where The Last Man has Gone Before! Decrease time over target: PayPal.me/mzhop or Venmo @clastronaut

The PodiumRunner Endurance Podcast is hosted by Ian Sharman, a professional ultra runner and coach with over 200 marathons or ultra finishes and more than 50 wins (www.sharmanultra.com, @sharmanian). We discuss training and racing topics with leading sports scientists and how to practically apply research findings for marathoners and ultra runners. Ep. 19: Returning from Injury with Hillary "Hillygoat" Allen  This episode we're talking to Hillary Allen who is an endurance athlete and coach specializing in mountain ultra marathons where she's earned the nickname, "Hillygoat." She started running in 2011 and comes from a background of high-level athletics, playing tennis in college. She's podiumed at many races around the world and is probably best known for her serious injury from a near-fatal fall at a race in Norway in 2017 and the return to racing afterwards, documented in the book, ‘Out and Back: A Runner's Story of Survival Against All Odds.' She also has a Masters in Neuroscience and Physiology and Structural Biology and co-hosts the Trail Society podcast. You can follow Hillary on Twitter (@hillygoatclimbs) and Instagram (@hillygoat_climbs). This show we're talking about returning from injury or a longer break from running. We discuss: Hillary's accident at the Tromsø Skyrace in 2017 and the resulting months. How she focused on physical rehab, despite seemingly minimal gains much of the time. Her mental approach to recovery, including returning to that same race to complete it two years later. What she's learned and useful takeaways for anyone who gets injured.

Vijay Pande is a General Partner @ Andreessen Horowitz (a16z), where he focuses on investments in Biopharma & Healthcare. As the founding investor of a16z's Bio Fund, Vijay leads the firm's investments at the cross section of biology and computer science, including applications in computation, machine learning, and artificial intelligence in healthcare; digital therapeutics; diagnostics; and other novel transformative scientific advances applied to industry that take bio beyond healthcare. Op-eds by Vijay defining trends and issues in this emerging space have been published by The New York Times, Scientific American, and Forbes, among others. He is also an Adjunct Professor of Bioengineering at Stanford University.Previously, Vijay was the Henry Dreyfus Professor of Chemistry and Professor of Structural Biology and of Computer Science at Stanford University, where he led a team of researchers pioneering computational methods and their application to medicine and biology (resulting in over 300 publications, two patents, and two novel drug candidates). Vijay was also concurrently the director of the Biophysics program at Stanford, where he led a team of more than 50 faculty members and propelled the program to the top in the country.During his time at Stanford, Vijay co-founded Globavir Biosciences, where he translated his research advances into a successful startup that aimed to discover cures for Dengue Fever and Ebola. Vijay also founded the Folding@Home Distributed Computing Project for disease research, which pushed the boundaries of computer science techniques (distributed systems, machine learning, and exotic computer architectures) into biology and medicine, in both research as well as the development of new therapeutics.Vijay holds a BA in Physics from Princeton University and a PhD in Physics from MIT. He has been awarded the DeLano Prize in Computation; a Guinness World Record for Folding@Home; the American Chemical Society Thomas Kuhn Paradigm Shift Award; and was selected for MIT TR10. In his teens, Vijay was the first employee at video game startup Naughty Dog Software, maker of Crash Bandicoot.Vijay serves on the board of the following Andreessen Horowitz portfolio companies: Apeel Sciences, BioAge Labs, Ciitizen, Devoted Health, Freenome, Insitro, Nautilus Biotechnology, Omada Health, Scribe Therapeutics, and Q Bio.Thank you for listening!BIOS (@BIOS_Community) unites a community of Life Science innovators dedicated to driving patient impact. Alix Ventures (@AlixVentures) is a San Francisco based venture capital firm supporting early stage Life Science startups engineering biology to create radical advances in human health.Music: Danger Storm by Kevin MacLeod (link & license)

Welcome to a new BioPOD series: Scotland's Biotech Stories. In this installment, BioPodder Liz Gaberdiel interviews Dr. Marcus Wilson on Cryogenic electron microscopy (CryoEM), a  technique that has undergone some serious upgrades since its initial development in the 1960s. Introduction by Neelakshi Varma & Editing by Sam Haynes Media by Hanna Peach and Chris Donohoe 

Now, when it comes to the obesity crisis facing many developed nations all across the world it can be easy to be reductive about it. “Just eat less” or “ just cut out the junk” or something like that. But what if there was something going on inside your brain that meant you never felt satisfied? What if you felt hungry all the time no matter how much you ate? How could we fight such a primal urge? Dr Moran Shalev-Benami, from the Department of Chemical & Structural Biology & Principal Investigator at the Weizmann Institute of Science in Israel joined Jonathan to discuss

Pod of Jake Podcast Notes Key Takeaways Biology is the science we need today: the trend is moving from physics to biology Machine learning is going to have a strong impression on pharmaceuticals, computer diagnostic tools, and healthcare as a wholeEducation, as it stands, is not scalable – if we can get the right structure and balance between absorbing information and network, education as we know it will change Two possible directions of San Francisco tech: (1) Scale the Bay – mix in-person and virtual work which will allow for collaboration and creativity but ease traffic; (2) Remain virtual and create distributed companies in the future but work to harness the spirit of San Francisco culture and big thinking “There's more to life than being efficient…working from home sounds good but too often I think you're really living at work.” – Vijay PandeTransitioning to remote work and lifestyle is a matter of “when” not “if” The cost of healthcare in the U.S. is rising to unsustainable levels and becoming a crisis Important to study older people and understand elements of what allows people to live longer and age slower versus contract diseaseThinking about reversing aging, longevity, and healthspan is now part of basic science Read the full notes @ podcastnotes.orgVijay is a general partner at Andreessen Horowitz, where he focuses on investments in biopharma and healthcare. As the founding investor of a16z's Bio Fund, he leads the firm's investments at the cross section of biology and computer science. Previously, Vijay was the Henry Dreyfus Professor of Chemistry and Professor of Structural Biology and of Computer Science at Stanford University. During his time at Stanford, Vijay co-founded Globavir Biosciences and the Folding@Home Distributed Computing Project for disease research. Vijay remains an Adjunct Professor of Bioengineering at Stanford University and holds a BA in Physics from Princeton University and a PhD in Physics from MIT. Disclaimer: a16z investments are discussed in this conversation. None of the content should be taken as investment advice. See a16z.com/disclosures for more information. [0:59] - How Vijay ended up at the intersection of biology and technology [6:15] - The academic shift from physics towards biology [12:32] - The difficulty of scaling education [16:38] - Vijay's predictions for the future of work and The San Francisco Bay Area [26:31] - Using distributed computing to simulate protein folding with Fold@Home [38:19] - The rising cost of healthcare and where the industry is moving [49:46] - Targeting longevity as a solution to harmful diseases --- homeofjake.com

Vijay is a general partner at Andreessen Horowitz, where he focuses on investments in biopharma and healthcare. As the founding investor of a16z’s Bio Fund, he leads the firm’s investments at the cross section of biology and computer science. Previously, Vijay was the Henry Dreyfus Professor of Chemistry and Professor of Structural Biology and of Computer Science at Stanford University. During his time at Stanford, Vijay co-founded Globavir Biosciences and the Folding@Home Distributed Computing Project for disease research. Vijay remains an Adjunct Professor of Bioengineering at Stanford University and holds a BA in Physics from Princeton University and a PhD in Physics from MIT. Disclaimer: a16z investments are discussed in this conversation. None of the content should be taken as investment advice. See a16z.com/disclosures for more information. [0:59] - How Vijay ended up at the intersection of biology and technology [6:15] - The academic shift from physics towards biology [12:32] - The difficulty of scaling education [16:38] - Vijay's predictions for the future of work and The San Francisco Bay Area [26:31] - Using distributed computing to simulate protein folding with Fold@Home [38:19] - The rising cost of healthcare and where the industry is moving [49:46] - Targeting longevity as a solution to harmful diseases --- homeofjake.com

In today’s Metabolic Moment, Dr. Power explains what structural biology is and how it is helpful in determining the effects, fates, and interactions of molecules. By determining the shape of a virus, drug makers will be able to craft very specific treatments, so it is an important and exciting specialty in the life sciences! Tune in today to learn more.

In this episode, I speak with Prof. Mohammed AlQuraishi. Mohammed is an Assistant Professor in the Department of Systems Biology at Columbia University. Mohammed gives us his unique thoughts and perspectives on a variety of problems that lie at the intersection of machine learning and structural biology.   (00:30) -- vision of Mohammed's lab (02:45) -- importance of abstractions to simulate a cell (04:29) -- conceptual advances in abstractions (06:03) -- protein folding (07:28) -- end-to-end differentiability (10:12) -- representations learned by protein structure deep learning models (14:07) -- predicting higher energy states in conformational space (15:46) -- is structure overrated? (18:10) -- protein localization prediction (20:37) -- pitfalls of having a programmatic view of a cell (23:34) -- why individuals with quantitative backgrounds may find biology interesting

Since the elucidation of the DNA structure by James Watson and Francis Crick in 1951, the importance of understanding the three-dimensional structure of biomolecules has become obvious. Over the last few decades scientists have resolved the structure of thousands of complex biomolecules enabling incredible innovations in drug design, biological and medical sciences. X-Ray crystallography has been the key technique, but in recent years Nuclear Magnetic Resonance (NMR) has emerged as an additional, complementary approach. Dr. Loren Andreas explains to us how NMR has grown to be the technology of choice as it has expanded its field of application from liquid solutions to condensed systems. The discussion is a surprising discovery of how progress in engineering and instrument design has completely changed the landscape in structural biology. Modern NMR allows scientists to study molecules in complex systems, simulating more closely their natural environment, including interaction between them. This episode offers an exciting glimpse of the future, through a few examples from today’s science.Visit https://thermofisher.com/bctl to register for your free Bringing Chemistry to Life T-shirt and https://www.alfa.com/en/chemistry-podcasts/ to access our episode summary sheet, which contains links to recent publications and additional content recommendations for our guest.

In which Vaibhav speaks with Dr. Mohammed AlQuraishi, an Assistant Professor of Systems Biology at the Columbia University Irving Medical Center, about using machine learning to predict protein structure. Among other things, they discuss the direction of algorithmic development in computational structure prediction, from neighborhood-based assembly of peptide fragments to modern applications of Deep Learning in structural modeling. They discuss the features of physical priors and discuss approaches in computationally optimizing protein-energy state predictions, taking into account the difficulties associated with the many local minima in an energy function. Throughout this discussion, Mohammed contextualizes the intuition behind the methods used by Deep Mind with their developments of AlphaFold.

Eric Rubin is the Editor-in-Chief of the Journal. Lindsey Baden is a Deputy Editor of the Journal. Jonathan Abraham is a structural biologist in the Department of Microbiology at Harvard Medical School and an infectious disease physician at Brigham and Women’s Hospital. Stephen Morrissey, the interviewer, is the Executive Managing Editor of the Journal. E.J. Rubin and Others. Audio Interview: The Implications of Changes in the Structural Biology of SARS-CoV-2. N Engl J Med 2021;384:e48.

Zu Weihnachten haben wir uns nochmal richtig bemüht und unseren ersten Gast eingeladen: Dr. Janina Sprenger ist Wissenschaftlerin am Deutschem Elektronen-Synchrotron "DESY". Sie ist auf Protein Kristallographie spezialisiert, was bedeutet, dass sie mithilfe von Röntgenstrahlung Biomoleküle "sichtbar" macht und analysiert. Diese Erkenntnisse können dann für Medikamente benutzt werden, zum Beispiel forscht sie gerade mit an möglichen Gegenmitteln für Corona. Wir haben ihr zahlreiche Fragen gestellt: Unteranderem über ihre Forschung, über ihre Doktorarbeit in Schweden und wie man als Wissenschaflter:in in der Gesellschaft wahrgenommen wird. Inhaltsverzeichnis 0:41 Vorstellung 2:12 Forschung am Desy bezüglich Covid 3:32 Was ist überhaupt Kristallographie 6:39 Doktorarbeit in Schweden, Ihre Forschung an Malaria 15:56 Wie sieht ein Tag als Protein Kristallograph:in aus? 19:35 Klischees über Wissenschaftler:innen 24:28 Rosalind Franklin & Frauen in der Wissenschaft 31:37 Was bedeutet künstliche Intelligenz für Kristallographie? Wird "Alpha Fold" ihre Arbeit unwichtig machen? 41:02 Schlusswort Links: Sprenger, Janina. (2014). hree-dimensional structures of Plasmodium falciparum spermidine synthase with bound inhibitors suggest new strategies for drug design. Acta Crystallographica Section D Biological Crystallography. Sprenger, Janina. (2020). Structural Biology of Pf AdoMetDC - Challenges in Crystallizing a Malarial Protein. https://www.youtube.com/watch?v=o25Nda3OHcM&feature=emb_logo https://en.wikipedia.org/wiki/Drug_discovery http://www.scienceandsociety.eu/2017/06/30/young-scientists-interview-with-janina-sprenger/

In his second presentation, Haas shares an example of how cryocrystallography has aided structure-based drug design.

In his second presentation, Haas shares an example of how cryocrystallography has aided structure-based drug design.

Dr. Vivian Chan is the founder and CEO of Sparrho, a platform that democratizes science using augmented intelligence. Dr. Chan has a PhD in biochemistry, and degrees in biotech. She has addressed the EU Ministers of Research and Innovation and keynoted the "Next Unicorn" series at Mobile World Congress.Episode NotesMusic used in the podcast: Higher Up, Silverman Sound StudioWere there fines in UK for leaving lock down in early COVID? Are there now? Under current regulations, organisers and facilitators of large gatherings (more than six) can be fined up to £10,000.Brits who do not wear a face covering in places where it is mandatory, such as in shops, supermarkets and on public transport, could be issued with a fine. Police can also break up any gatherings larger than six and can issue a £200 fine if they do not comply. Sparrho - www.sparrho.comB to B to C - It's the very definition of the oft-cited CPG industry “b-to-b-to-c” (b2b2c) model, where the supplier sells its products to retailers, who in turn sell to consumers. ... Many CPGs are today claiming a new focus on consumer centricity; that it's their consumer who matters, who they care about, and who they serve. (www.briansolis.com) B to B - Business-to-business, is a process for selling products or services to other businesses. Biotechnology - a broad area of biology, involving the use of living systems and organisms to develop or make products. (wikipedia)Biochemistry - the branch of science concerned with the chemical and physicochemical processes and substances that occur within living organisms. (wikipedia)Maths C covers additional pure-maths topics (including complex numbers, matrices, vectors, further calculus and number theory). Maths C gives the students an understanding of the methods and principles of mathematics and the ability to apply them in everyday situations and in purely mathematical contexts; the capacity to model actual situations and deduce properties from the model. (wikipedia)Structural Biology - a branch of molecular biology, biochemistry, and biophysics concerned with the molecular structure of biological macromolecules (especially proteins, made up of amino acids, RNA or DNA, made up of nucleotides, membranes, made up of lipids) how they acquire the structures they have, and how alterations in their structures affect their function. (wikipedia)RNA - ribonucleic acid, a nucleic acid present in all living cells. Its principal role is to act as a messenger carrying instructions from DNA for controlling the synthesis of proteins, although in some viruses RNA rather than DNA carries the genetic information. X-ray crystallography - the study of crystals and their structure by means of X-ray diffraction.Homeostasis - the tendency toward a relatively stable equilibrium between interdependent elements, especially as maintained by physiological processes.Ligand - is an ion or molecule (functional group) that binds to a central metal atom to form a coordination complex. The bonding with the metal generally involves formal donation of one or more of the ligand's electron pairs.

In this episode, I talk to Dr Onisha Patel, who is a structural biologist at the Walter and Eliza Hall Institute of Medical Research (WEHI) in Melbourne, Australia I speak to her about: – How she got into science from her interest in art – What inspired her growing up – Structural Biology and what […]

In this episode, your hosts, Dr. Sabah Kadri and Arshi Arora bring to you united yet diverse sub fields of Computational Biology in the voice of four different scientists, spanning areas of Structural Biology, Systems Biology, Chemistry, Proteomics and Genomics. They cover their background, speciality and what drew them to Computational ability in the first place. Contact us with questions, feedback and requests to collaborate on future episodes at computationallyyours@gmail.com. Follow us on Twitter: @compbiopodcast ; Dr. Sabah Kadri: @sabahkadri ; Arshi Arora: @arorarshi Website: http://computationallyyours.netlify.app/ Follow us on Instagram https://www.instagram.com/computationallyours/ Intro/Outro music: Riatsu (Shadaab Kadri) Thumbnail: Thanks to Zoltan Tasi for sharing their work on Unsplash. --- Send in a voice message: https://anchor.fm/computationally-yours/message

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.24.265553v1?rss=1 Authors: Currell, F., Villagomez Bernabe, B., Chan, S.-w., Roseman, A., Coulter, J. Abstract: Here we show an interplay between the structures present in ionization tracks and nucleocapsid RNA structural biology, using fast ion beam inactivation of the severe acute respiratory syndrome coronavirus (SARS-CoV) virion as an example. This interplay is one of the key factors in predicting dose-inactivation curves for high energy ion beam inactivation of virions. We also investigate the adaptation of well-established cross-section data derived from radiation interactions with water to the interactions involving the components of a virion, going beyond the density-scaling approximation developed previously. We conclude that solving one of the grand challenges of structural biology - the determination of RNA tertiary/quaternary structure structure - is intimately linked to predicting ion-beam inactivation of viruses and that the two problems can be mutually informative. Indeed, our simulations show that fast ion beams have a key role to play in elucidating RNA tertiary/quaternary structure. Copy rights belong to original authors. Visit the link for more info

BROADCAST YOURSELF - 8 Week Course: https://londonreal.tv/by/ 2021 SUMMIT TICKETS: https://londonreal.tv/summit/ NEW MASTERCLASS EACH WEEK: http://londonreal.tv/masterclass-yt LATEST EPISODE: https://londonreal.link/latest Professor Michael Levitt is a Nobel Prize-winning biophysicist who has conducted pioneering work on the molecular structure of essential biological compounds. He has made many significant contributions to the study of protein folding and helped to popularise the use of computer modelling in biology. Amongst the foremost of Michael’s numerous scientific achievements is his development of the first computerised model of an enzyme reaction, which was subsequently expanded to simulate more generalised protein dynamics. He has also carried out important research on the modelling of antibodies as well as DNA and messenger RNA — work that has informed practical advances in biomedical science. Michael has received many honours and awards for his research, including the 2013 Nobel Prize in Chemistry and the 2014 DeLano Award for Computational Biosciences. A member of the US National Academy of Sciences, since 1987 he has been a Professor of Structural Biology at Stanford University.

Join Kash as he interviews scientist Heidar Koning on his research into structural biology, DNA and RNA, CRISPR and much more!   

On March 11, 2020, people all over the world were thrust into a new reality when the coronavirus was declared a global pandemic. It was as if the globe had ground to a halt as businesses closed, schools shuttered, and stay-at-home orders emptied city streets. Thousands of lives have been lost; all lives have been changed.  Over the past two months, despite these uncertain and unprecedented circumstances, there have been shining examples of goodness and triumphs of the human spirit solidifying the fact that we as a species are both adaptable and resilient. Scientists around the globe have aligned their focus to find solutions to this challenge of historic proportions, and groundbreaking discoveries have been made. Everyday, we continue to make progress in quelling the impact of this virus and protecting our loved ones at risk.  Unfortunately, much of the COVID-19 news comes to us clouded by the business of mass media which built momentum selling us a narrative of fear.  In this episode, Tony goes on a journey to uncover the truth about coronavirus with a 7-person panel of highly qualified researchers, an experienced epidemiologist, a Nobel Laureate, and M.D.s testing and treating patients on the frontlines. Together, they reveal the evidence-based research that has come to light and the emerging facts that you’re not hearing in the headlines that are absolutely crucial in shaping decisions about how to move forward.  This is one of the most important interviews that Tony has conducted in his 40+ year career. It reminds us to stand guard at the door of our mind, practice discernment when determining trustworthy sources, and think critically for ourselves in order to stay flexible and maintain the ability to pivot in light of new information – especially when lives depend on it.  Podcast panelists in this episode include:  Dr. Michael Levitt, PhD, Nobel Prize – Chemistry; Professor of Structural Biology, Stanford University  Dr. Eran Bendavid, M.D., Associate Professor of Medicine, Stanford University  Dr. Michael Roizen, M.D., Chief Wellness Officer Emeritus, Cleveland Clinic Dr. Alan Preston, Sc.D, Former Professor of Epidemiology & Biostatistics, Texas A&M University Senator Scott Jensen, M.D., Family Physician; Senator (R-Minnesota)  Dr. Dan Erickson, M.D., Co-Owner, Accelerated Urgent Care Dr. Artin Massihi, M.D., Co-Owner, Accelerated Urgent Care EPISODE NOTES: [00:00:00] Introduction from Tony Robbins [00:08:30] Interview with Dr. Michael Levitt [00:27:00] Interview with Senator Scott Jensen, M.D. [00:39:00] Interview with Dr. Eran Bendavid, M.D [00:49:30] Interview with Dr. Alan Preston, Sc.D [01:17:30] Interview with Dr. Michael Roizen, M.D. [01:38:00] Interview with Dr. Dan Erickson, M.D. [01:45:30] Interview with Dr. Artin Massihi, M.D. [01:55:25] Round-Robin discussion [02:34:20] Tony’s closing comments

If the best way to know whether a medicine is effective is through a clinical trial, then where does (and doesn't) real-world data and real-world evidence come in? The topic is always top of mind in drug development, with additional focus as of 2016 thanks to the 21st Century Cures Act -- but is especially heated lately given recent concerns and claims around particular drugs in the context of the novel coronavirus pandemic.So in this short-but-deep dive episode of 16 Minutes on the News, a16z general partner in bio Vijay Pande -- previously a professor of Chemistry, Structural Biology, and Computer Science at Stanford University (as well as founder of Folding@Home) -- breaks down the debate between RWE vs. RCT (real world evidence and randomized controlled trials), in conversation with Sonal Chokshi. Is it a tradeoff between speed of innovation and safety, or is it a false dichotomy altogether? Where do and don't statistics come in when it comes to policy? How has, and could, the role of the FDA (as well as payers reimbursing healthcare) evolve here? And where can technology help?

Dr. Lisa Eshun-Wilson has a Ph.D. in Molecular and Structural Biology from UC Berkeley. She is the first African-American to graduate within her field of study from UC Berkeley. Even more interesting, in the summer of 2019 - Lisa went to a conference in her field in China, and out of thousands of applications, Lisa was the ONLY African American to apply. She is an advocate to increase the number of diverse students in this field, and make sure there is mentorship available as students navigate through undergrad and grad school. Tune in and learn more about her story!Support the show (https://www.gofundme.com/manage/stem-communications-fund)

Now a professor of biochemistry at the University of Cambridge, in 1969 Sir Thomas Blundell was one of the first people to see what the hormone insulin looked like. As part of the team led by Nobel Prize winner Dorothy Hodgkin, it was a medical breakthrough for diabetes patients everywhere. “I was always interested in doing a range of different things,” Professor Blundell says. “I came from a family where my grandfather was a very gifted artist and musician. And although my parents left school when they were 14 and 15, they always encouraged me to think more broadly.” “So I may be a little bit unusual because I’ve ended up doing things in politics, music and science, and that of course led me to advise prime ministers and to run organisations and found companies.” Professor Blundell’s research has focussed on understanding the structure and function of molecules for targets to improve drug design. “By using X-rays with very short wavelength, I can see these very tiny molecules. Add in other methods like electron microscopy and the individual molecules can be revealed. His work has contributed significantly to stopping the progression of HIV into AIDS and to developing new drugs for cancer treatment in both his academic career and through a spinoff company he initially founded with two former students. “In Europe, Australia and the United States, we are lucky, we have access to medicines that research has developed, but the real challenge is to make sure that it’s available not just to the rich, but to the world in general.” Episode recorded: September 26, 2019. Interviewer: Dr Andi Horvath. Producer, audio engineer and editor: Chris Hatzis. Co-production: Silvi Vann-Wall and Dr Andi Horvath. Image: Getty Images.

Nobel Prize-winning structural biologist, Knight Bachelor, President of the Royal Society, brilliant structural biologist and enthralling author Venki Ramakrishnan tells the story of his race to discover the inner workings of biology's most important molecule - the ribosome, in this articulate, witty and surprisingly philosophical series of videos.

Krishna starts a new format for the podcast, and talks about how the field of structural biology has dramatically changed in the past 5 years.

Professor Eva Nogales started her career in a time where barely any women were seen in science departments. In college, she skipped biology to focus on physics, relying on her high-school knowledge of the former to shape her career as a biophysicist. Now, she’s using her understanding of the microtubules in our cells for improving disease management, including slowing the uncontrollable growth of cancer. This niche understanding of our cell behaviour at the molecular level is already improving the lives of humans everywhere, and the technique used by Professor Nogales called “cryo-EM” is taking the world of structural biology by storm. She recently visited the University of Melbourne to receive the 2019 Grimwade Medal, and to deliver the oration titled: Visualising the molecular dance at the heart of human gene expression. Episode recorded: February 14, 2019.Interviewer: Steve Grimwade.Producer and editor: Chris Hatzis.Co-production: Silvi Vann-Wall and Dr Andi Horvath.Banner: Berkeley Lab.

The 2019 Haldane Lecture was delivered by Sir Venki Ramakrishnan, President of the Royal Society, on February 7th at Wolfson College, Oxford. The lecture was introduced by College President Sir Tim Hitchens. The thousands of genes in our DNA are translated by ribosomes - ancient, enormous molecular machines that read the genetic code to make the thousands of proteins that carry out the functions of life. Although the ribosome was discovered in the 1950s, unravelling its million atom structure took over four decades. Venki Ramakrishnan will frame this in term of his career and show how science does not proceed in a series of logical steps but in fits and starts, with many characters and their egos, rivalries, competition and collaboration, blunders and dead ends. Sir Venki is a structural biologist who in 2009 received the Nobel Prize in Chemistry and was knighted in 2012. In 2015, he was elected as President of the Royal Society.

Erica Ollmann Saphire discusses her research on Ebola virus glycoprotein and the changing nature of structural biology. The Ebola virus glycoprotein sequence can vary up to 50% between Ebola virus species, presenting a challenge to develop pan-Ebola therapeutics or vaccines. Erica Ollmann Saphire discusses her work on antibodies that neutralize all Ebola virus species and the changing nature of the structural biology toolkit used to study them. Check out all our great podcasts at asm.org/podcast MTM Listener Survey: asm.org/mtmpoll Ollmann-Saphire Lab Site Protein Database Isolation of Potent Neutralizing Antibodies from a Survivor of the 2014 Ebola Virus Outbreak. Science 2016. Systemic Analysis of Monoclonal Antibodies against Ebola Virus GP Defines Features that Contribute to Proteciton. Cell 2018. Structural Basis of Pan-Ebolavirus Neutralization by a Human Antibody against a Conserved, yet Cryptic Epitope. mBio 2018. Tenacious Researchers Identify a Weakness in All Ebolaviruses. mBio 2018. HOM Tidbit: How “Lassa,” a small Nigerian Town, was Stigmatized by having a Killer Virus Named after it.

11 września 2018 roku Rada Wydziału Biologii i Ochrony Środowiska UŁ podpisała porozumienie o współpracy oraz nadała tytuł honorowego pracownika naukowego UŁ Prof. Russelowi Reiterowi z University of Texas Health Science Center Department of Cellular & Structural Biology w Stanach Zjednoczonych. Uroczystości tej towarzyszył wykład prof. Russela Reitera “Success in Science: Requirements and Expectations”.

Melvyn Bragg and guests discuss enzymes, the proteins that control the speed of chemical reactions in living organisms. Without enzymes, these reactions would take place too slowly to keep organisms alive: with their actions as catalysts, changes which might otherwise take millions of years can happen hundreds of times a second. Some enzymes break down large molecules into smaller ones, like the ones in human intestines, while others use small molecules to build up larger, complex ones, such as those that make DNA. Enzymes also help keep cell growth under control, by regulating the time for cells to live and their time to die, and provide a way for cells to communicate with each other. With Nigel Richards Professor of Biological Chemistry at Cardiff University Sarah Barry Lecturer in Chemical Biology at King's College London And Jim Naismith Director of the Research Complex at Harwell Bishop Wardlaw Professor of Chemical Biology at the University of St Andrews Professor of Structural Biology at the University of Oxford Producer: Simon Tillotson.

Melvyn Bragg and guests discuss enzymes, the proteins that control the speed of chemical reactions in living organisms. Without enzymes, these reactions would take place too slowly to keep organisms alive: with their actions as catalysts, changes which might otherwise take millions of years can happen hundreds of times a second. Some enzymes break down large molecules into smaller ones, like the ones in human intestines, while others use small molecules to build up larger, complex ones, such as those that make DNA. Enzymes also help keep cell growth under control, by regulating the time for cells to live and their time to die, and provide a way for cells to communicate with each other. With Nigel Richards Professor of Biological Chemistry at Cardiff University Sarah Barry Lecturer in Chemical Biology at King's College London And Jim Naismith Director of the Research Complex at Harwell Bishop Wardlaw Professor of Chemical Biology at the University of St Andrews Professor of Structural Biology at the University of Oxford Producer: Simon Tillotson.

Thursday, August 25, 2016 Jim Lechleiter (Cellular & Structural Biology, UTHSCSA) gets us up to speed on astrocytes, their role in brain health and homeostasis, and their properties as excitable cells. Duration: 50 minutes Discussants:(in alphabetical order) Michael Beckstead (Assoc. Prof, UTHSCSA) Carlos Paladini (Assoc. Prof, UTSA) Salma Quraishi (Res. Asst Prof, UTSA) Matt Wanat (Asst Prof, UTSA) Charles Wilson (Ewing Halsell Chair, UTSA) acknowledgement: JM Tepper for original music.

Jim Lechleiter (Cellular & Structural Biology, UTHSCSA) gets us up to speed on astrocytes, their role in brain health and homeostasis, and their properties as excitable cells. Duration: 50 minutesDiscussants:(in alphabetical order)Michael Beckstead (Assoc. Prof, UTHSCSA)Carlos Paladini (Assoc. Prof, UTSA)Salma Quraishi (Res. Asst Prof, UTSA)Matt Wanat (Asst Prof, UTSA)Charles Wilson (Ewing Halsell Chair, UTSA) acknowledgement: JM Tepper for original music.

This is a great episode-- I had a chance to chat with Hillary Allen, a teacher in Neuroscience and Structural Biology who also happens to be a professional athlete on the ultrarunning team for The North Face. Hillary brings some valuable insight into achieving work-life balance and pushing her limits both mentally and physically. In an industry that is heavily guided by the pursuit of passion, there are some great takeaways for you about making time to achieve the goals that are most important to you and being a well-rounded human being outside of athletics.

Structure of viruses Professor David Stuart studies the structure of viruses at the molecular level. His work is particularly interested in virus-receptor interaction and the basic puzzles of virus assembly and he uses structural biology to answer these questions.

Understanding the function of a protein is an important step in finding out why the body succumbs to disease – but how do scientists find these proteins and figure out how they work?

Dr. Joel Levine is an Associate Professor of Biology and the Canada Research Chair in Neurogenetics at the University of Toronto, Mississauga. He received his PhD in Dr. Richard Miselis Anatomy and Structural Biology from the University of Pennsylvania. He then completed a Postdoctoral Fellowship with Rob Jackson at the Worcester Foundation for Biological Research, a postdoc fellowship with Dr. Steven Reppert at Harvard University, and a postdoc with Dr. Jeffrey Hall at Brandeis University before joining the faculty at the University of Toronto. Joel is here with us today to tell us all about his journey through life and science.

Structure of viruses Professor David Stuart studies the structure of viruses at the molecular level. His work is particularly interested in virus-receptor interaction and the basic puzzles of virus assembly and he uses structural biology to answer these questions.

Dr. Stephen Curry is a Professor of Structural Biology and Director of Undergraduate Studies in the Department of Life Sciences at Imperial College London. He received his PhD from Imperial College London. Stephen is a Fellow of the Society of Biology and was recently awarded the Peter Wildy Prize for Microbiology Education from the Society for General Microbiology. Stephen is here with us today to tell us all about his journey through life and science.

Dr. James Berger is a Professor in the Department of Biophysics and Biopysical Chemistry at the Johns Hopkins University School of Medicine. He received his PhD in Biochemistry and Structural Biology from Harvard University in 1995. Afterwards he was an independent research fellow at the Whitehead Institute of MIT until 1998. James then joined the faculty at UC Berkeley, where he remained for 15 years until coming to Johns Hopkins University in 2013. James has received many awards and honors during his career, including the National Academy of Sciences Award in Molecular Biology, the American Chemical Society Pfizer Award in Enzyme Chemistry, the American Society for Biochemistry and Molecular Biology Schering-Plough Scientific Achievement Award, a Packard Fellows award, and election to both the American Academy of Arts and sciences and the National Academy of Sciences.

Professor Yvonne Jones tells us how structural biology was brought into the field of immunology in Oxford, at the Wellcome Trust Centre for Human Genetics. Professor Jones also explains the developments of her current research on cell surface receptors as mediators of nerve cells guidance.

Diomedes Logothetis and Rahul Mahajan explain how G proteins activate a potassium channel to slow heart rate.

Meet our Division of Structural Biology. The Division of Structural Biology (STRUBI) is part of the Nuffield Department of Clinical Medicine (NDM) at the University of Oxford. STRUBI is also part of the Wellcome Trust Centre for Human Genetics. The Division includes the Oxford Protein Production Facility (OPPF) and the Oxford Particle Imaging Centre (OPIC).

Structural analysis reveals how a Wnt binds to its receptor.

When a young Mary Jane Osborn announced she wanted to be a nurse when she grew up, her father wondered aloud why she shouldn't be a doctor instead. Fueled by his faith that she could succeed in what was then a man's profession, Osborn went on to study physiology and biochemistry. Her work as a graduate student revealed how methotrexate, now a major cancer drug, acts on the body. Osborn then turned her abilities to microbiology, and spent decades exploring how bacteria make lipopolysaccharides-substances that help give potentially deadly bacteria their toxicity and virulence. Osborn is a professor in the Molecular, Microbial and Structural Biology department at the University of Connecticut Health Center. She was elected to the National Academy of Sciences in 1978.

Professor Yvonne Jones talks about cell-cell communication and how this can help us develop new drugs. Prof. Yvonne Jones is director of the Cancer Research UK Receptor Structure Research Group. Her research focuses on the structural biology of cell surface recognition and signalling complexes. Receptors embedded in the surface are potential targets for therapeutic intervention in many diseases including cancer.

Professor Yvonne Jones talks about cell-cell communication and how this can help us develop new drugs. Cells communicate through receptors on their surface; however, when these finely tuned systems don't work correctly, diseases can be triggered. Professor Yvonne Jones has been working to identify the structural biology of cell surface recognition and signalling complexes. Receptors embedded in the surface are potential targets for therapeutic intervention in many diseases including cancer. Professor Jones is director of the Cancer Research UK Receptor Structure Research Group.

Professor Yvonne Jones talks about cell-cell communication and how this can help us develop new drugs. Cells communicate through receptors on their surface; however, when these finely tuned systems don't work correctly, diseases can be triggered. Professor Yvonne Jones has been working to identify the structural biology of cell surface recognition and signalling complexes. Receptors embedded in the surface are potential targets for therapeutic intervention in many diseases including cancer. Professor Jones is director of the Cancer Research UK Receptor Structure Research Group.

Professor Yvonne Jones talks about cell-cell communication and how this can help us develop new drugs. Prof. Yvonne Jones is director of the Cancer Research UK Receptor Structure Research Group. Her research focuses on the structural biology of cell surface recognition and signalling complexes. Receptors embedded in the surface are potential targets for therapeutic intervention in many diseases including cancer.

Structural analysis reveals how increasing concentrations of monomeric actin prevent a transcriptional coactivator from promoting expression of target genes.

Brain Day 2011 is sponsored by the Neurological Foundation of NZ and the University of Otago. As part of Brain Awareness Week, we join this major international effort to communicate the wonders and achievements of brain research. Dr Louise Parr-Brownlie, from the Department of Anatomy and Structural Biology, speaks on “Shedding light on Parkinson’s Disease” Held March 19, 2011.

Audio PodcastAired date: 4/20/2011 3:00:00 PM Eastern Time

Video PodcastAired date: 4/20/2011 3:00:00 PM Eastern Time

Professor Chas Bountra explains how new drugs can offer novel treatments for neurodegenerative and gastrointestinal diseases, as well as pain disorders. Professor Chas Bountra is interested in identifying and validating target proteins for drug discovery. Various technologies and strategies have allowed him to progress promising clinical candidates into Phase I, II, III studies, and to market. Drug candidates are first selected by screening compounds capable of binding to a target protein. Those compounds are then tested in various assay systems, healthy volunteers and finally in patients. Academic research excels at defining good target proteins. Pharmaceutical companies then facilitate the transition from basic research to clinical trials, producing new therapies for patients.

Professor Chas Bountra explains how new drugs can offer novel treatments for neurodegenerative and gastrointestinal diseases, as well as pain disorders. Professor Chas Bountra is interested in identifying and validating target proteins for drug discovery. Various technologies and strategies have allowed him to progress promising clinical candidates into Phase I, II, III studies, and to market. Drug candidates are first selected by screening compounds capable of binding to a target protein. Those compounds are then tested in various assay systems, healthy volunteers and finally in patients. Academic research excels at defining good target proteins. Pharmaceutical companies then facilitate the transition from basic research to clinical trials, producing new therapies for patients.

Professor Chas Bountra explains how new drugs can offer novel treatments for neurodegenerative and gastrointestinal diseases, as well as pain disorders. Professor Chas Bountra is interested in identifying and validating target proteins for drug discovery. Various technologies and strategies have allowed him to progress promising clinical candidates into Phase I, II, III studies, and to market. Drug candidates are first selected by screening compounds capable of binding to a target protein. Those compounds are then tested in various assay systems, healthy volunteers and finally in patients. Academic research excels at defining good target proteins. Pharmaceutical companies then facilitate the transition from basic research to clinical trials, producing new therapies for patients.

Peter Parham, Professor in the Departments of Structural Biology and Microbiology & Immunology at the Stanford University School of Medicine, explores proteins of the human immune system that vary greatly between individuals and populations which modulate the immune response to infection and cancer, and also influence the success of reproduction and therapeutic transplantation of cells, tissues and organs. Series: "CARTA - Center for Academic Research and Training in Anthropogeny" [Science] [Show ID: 18704]

Peter Parham, Professor in the Departments of Structural Biology and Microbiology & Immunology at the Stanford University School of Medicine, explores proteins of the human immune system that vary greatly between individuals and populations which modulate the immune response to infection and cancer, and also influence the success of reproduction and therapeutic transplantation of cells, tissues and organs. Series: "CARTA - Center for Academic Research and Training in Anthropogeny" [Science] [Show ID: 18704]

An interview with Dr John Reynolds, Department of Anatomy and Structural Biology.