Podcasts about protein structure

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Play Episode Listen Later Feb 3, 2022 53:06

This episode is about AlphaFold and the impact it is having on junior scientists. I spoke with a group of them from different labs at the Max Planck Institute of Biochemistry. I spoke with Dr Isabell Bludau, a postdoctoral fellow and computational biologist in the lab of Dr Matthias Mann, Dr. Bastian Bräuning, a postdoctoral fellow and project group leader in the Department of Brenda Schulman and Juan Restropo a PhD student in the lab of Dr Jürgen Cox.

Predicting protein structure, episode 3

Play Episode Listen Later Feb 3, 2022 42:32

Biology and AI for predicting protein structure. This is a chat with conversation with some members of the Rost lab at the Technical University of Munich. Dr. Maria Littmann, postdoctoral fellow, and PhD students Konstantin Weissennow and Michael Heinzinger and Dr Burkhard Rost, principal investigator. We talked about AlphaFold, a computational approach from DeepMind Technologies that has changed the way and the speed at which proteins can be predicted.

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Predicting protein structure, episode 2

protein predicting pdb protein structure

Play Episode Listen Later Jan 11, 2022 35:34

Protein structure prediction is the Nature Methods Method of the Year for 2021. Here is my feature on that. https://www.nature.com/articles/s41592-021-01359-1 For the story, I chatted with Helen Berman, co-founder of the Protein Data Bank (PDB), which is home to experimentally determined structural data for over 180,000 proteins. What's next for the PDB. And of course this relates to the past. She's a bit secretive about the future, but discloses some of the plans currently underway. She is co-architect of the PDB's next chapter.

Predicting protein structure, episode 1

Best of US Investors's Podcast

Play Episode Listen Later Jan 11, 2022 58:37

Proteins are twirly, curly, dynamic structures. Crucial for life, complicated to study. Predicting protein structure has been tough but it's now easier as AlphaFold enters the scene. That doesn't mean that AlphaFold has solved all challenges, of course. AlphaFold was developed by DeepMind Technologies, a company that was bought by Google in 2014. Lots of protein puzzles remain. Dr. Janet Thornton from the European Bioinformatics Institute and Dr David Jones of University College London talk about what AlphaFold can do and what it cannot yet do. They look forward, backward and all around on this subject. He says, laughing, he has "extreme cautious optimism" about the prospects of this field. You can also find my feature story about protein structure prediction, which is the Nature Methods method of the year for 2021, here: https://www.nature.com/articles/s41592-021-01359-1

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Ep 551 | Ai Protein Structure treats Cancer | Pfizer using Cannabis | RNA fights Heart & Liver diseases | Zilliqa's Metaverse | BitIRA Crypto IRA

Simple Eats w/ Chef T

Play Episode Listen Later Dec 27, 2021 49:44

Host: Tito Dudley aka BioChefT Be informed! BioChefT: BitIRA: https://bitira.com/vektween Instagram: https://www.instagram.com/biocheft/ TikTok: https://vm.tiktok.com/ZMJrNHcMT/ Twitter: https://mobile.twitter.com/officialcheft Website Hub: vektween.channel References: Crypto Prices: https://coinmarketcap.com/ Advance Ai: https://scitechdaily.com/advanced-new-artificial-intelligence-software-can-compute-protein-structures-in-10-minutes/amp/ Pfizer and Cannabis: https://www.marketwatch.com/story/pfizers-arena-pharma-deal-may-lead-drug-company-to-a-cannabis-based-treatment-11640278900 RNA: https://www.inquirer.com/health/rna-heart-disease-cancer-vaccines-astrazeneca-20211223.html Zilliqa's Metaverse: https://www.crypto-news-flash.com/zilliqa-is-coming-to-the-metaverse-as-it-launches-its-xr-platform-metapolis/ Cancer Treatment | Cannabinoides | Blockchain --- Send in a voice message: https://anchor.fm/vektween/message Support this podcast: https://anchor.fm/vektween/support

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Google's Deep Mind + Alpha Fold + Open-Source AI Prediction of Protein Structure = 10X Stocks

Play Episode Listen Later Dec 23, 2021 19:54

:) = TradingViews: https://www.tradingview.com/?offer_id...Watch to get an understand of what is about to happen in the stock market:5 movies that explain what caused the 2009 financial crisis, and what happened after:The Big Short (2015)Margin Call (2011)Too Big to Fail (2011)99 Homes (2014)Inside Job (2010)Read these books to build your knowledge on how the markets work and what the future is going to look like.RECOMMENDED BOOKS (Affiliate Links)1. The Accidental Super Power by Peter Zeihan: https://amzn.to/3dEl9tL2. The Big Nine by Amy Webb: https://amzn.to/2yOJmPe3. The Code Breaker, Jennifer Doudna, Gene Editing, and the Future of the Human Race by Walter Isaacson. https://amzn.to/2WNi6wfThe Power of a Tribe: https://www.amazon.com/dp/B096TWBDM3/...Get Surfshark VPN at https://surfshark.deals/INVESTORS and enter promo code INVESTORS for 83% off and 3 extra months for free! Help Kerry and Nita win the race against Childhood Cancer and keep their daughter Shay's memory alive. Your support makes a direct impact in the fight against Pediatric Cancer at Children's of Alabama by helping advance research in finding a cure for cancer. http://give.childrensal.org/bestofus

Protein Structure (2021)

Medical Biochemistry

Play Episode Listen Later Sep 17, 2021 12:43

In this episode, I discuss about the 4 levels of protein structure: primary, secondary, tertiary and quaternary structures and correlate disease states to changes in protein structure.

protein structure

115 - Protein Structure II (w/ Clint Stalnecker!)

Curioscity

Play Episode Listen Later Apr 29, 2021 41:48

Our last episode about proteins discussed protein structure. What happens when protein has little or no structure? Let’s learn to be scientifically conversational. For all references and supplemental information, you can navigate to ascienceshow.com.

clint protein structure

Stronger magnets, stronger science

Bringing Chemistry to Life

Play Episode Listen Later Mar 24, 2021 36:50

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.

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Protein structure and AI: the excitement about the recent advance made by Google DeepMind’s AlphaFold Programme

Oxford Martin School: Public Lectures and Seminars

Play Episode Listen Later Feb 18, 2021 61:00

Why is it important to understand the 3-D structures of protein, why are they difficult to construct, and what is the nature of AlphaFold’s advance? Why is this so exciting and what further advances in medicine and the other biosciences may result? On the 30th November it was announced that the Artificial Intelligence computer programme AlphaFold had made a decisive breakthrough in the determination of the 3-D structures of proteins. The announcement was immediately hailed as one of the major scientific advances of the decade. To find out why, join a conversation between Yvonne Jones, Director, Cancer Research UK Receptor Structure Research Group, Professor Phil Biggin, Professor of Computational Biochemistry, and Charles Godfray, Director, Oxford Martin School, who will explore these fascinating issues.

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Homology Initialization for Protein Structure Determination via Distance Geometry

PaperPlayer biorxiv biochemistry

Play Episode Listen Later Oct 15, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.14.339903v1?rss=1 Authors: Tasissa, A., Lai, R., Wang, C. Abstract: The problem of finding the configuration of points given partial information on pairwise inter-point distances, the Euclidean distance geometry problem, appears in multiple applications. In this paper, we propose an approach that integrates homology modeling and a nonconvex distance geometry algorithm for the protein structure determination problem. Preliminary numerical experiments show promising results. Copy rights belong to original authors. Visit the link for more info

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Improved protein structure prediction by deep learning irrespective of co-evolution information

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Play Episode Listen Later Oct 12, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.12.336859v1?rss=1 Authors: Xu, J., Mcpartlon, M., Li, J. Abstract: We describe our latest study of the deep convolutional residual neural networks (ResNet) for protein structure prediction, including deeper and wider ResNets, the efficacy of different input features, and improved 3D model building methods. Our ResNet can predict correct folds (TMscore>0.5) for 26 out of 32 CASP13 FM (template-free-modeling) targets and L/5 long-range contacts for these targets with precision over 80%, a significant improvement over the CASP13 results. Although co-evolution analysis plays an important role in the most successful structure prediction methods, we show that when co-evolution is not used, our ResNet can still predict correct folds for 18 of the 32 CASP13 FM targets including several large ones. This marks a significant improvement over the top co-evolution-based, non-deep learning methods at CASP13, and other non-coevolution-based deep learning models, such as the popular recurrent geometric network (RGN). With only primary sequence, our ResNet can also predict correct folds for all 21 human-designed proteins we tested. In contrast, RGN predicts correct folds for only 3 human-designed proteins and zero CASP13 FM target. In addition, we find that ResNet may fare better for the human-designed proteins when trained without co-evolution information than with co-evolution. These results suggest that ResNet does not simply denoise co-evolution signals, but instead is able to learn important sequence-structure relationship from experimental structures. This has important implications on protein design and engineering especially when evolutionary information is not available. Copy rights belong to original authors. Visit the link for more info

CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction

Play Episode Listen Later Oct 7, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.06.327585v1?rss=1 Authors: Ju, F., Zhu, J., Shao, B., Kong, L., Liu, T.-Y., Zheng, W.-M., Bu, D. Abstract: Protein functions are largely determined by the final details of their tertiary structures, and the structures could be accurately reconstructed based on inter-residue distances. Residue co-evolution has become the primary principle for estimating inter-residue distances since the residues in close spatial proximity tend to co-evolve. The widely-used approaches infer residue co-evolution using an indirect strategy, i.e., they first extract from the multiple sequence alignment (MSA) of query protein some handcrafted features, say, co-variance matrix, and then infer residue co-evolution using these features rather than the raw information carried by MSA. This indirect strategy always leads to considerable information loss and inaccurate estimation of inter-residue distances. Here, we report a deep neural network framework (called CopulaNet) to learn residue co-evolution directly from MSA without any handcrafted features. The CopulaNet consists of two key elements: i) an encoder to model context-specific mutation for each residue, and ii) an aggregator to model correlations among residues and thereafter infer residue co-evolutions. Using the CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrated the successful application of CopulaNet for estimating inter-residue distances and further predicting protein tertiary structure with improved accuracy and efficiency. Head-to-head comparison suggested that for 24 out of the 31 free modeling CASP13 domains, ProFOLD outperformed AlphaFold, one of the state-of-the-art prediction approaches. Copy rights belong to original authors. Visit the link for more info

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DeepPSC (protein structure camera): computer vision-based reconstruction of proteins backbone structure from alpha carbon trace as a case study

Play Episode Listen Later Aug 13, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.12.247312v1?rss=1 Authors: Zhang, X., Luo, J., Cai, Y., Zhu, W., Yang, X., Cai, H., Lin, Z. Abstract: Deep learning has been increasingly used in protein tertiary structure prediction, a major goal in life science. However, all the algorithms developed so far mostly use protein sequences as input, whereas the vast amount of protein tertiary structure information available in the Protein Data Bank (PDB) database remains largely unused, because of the inherent complexity of 3D data computation. In this study, we propose Protein Structure Camera (PSC) as an approach to convert protein structures into images. As a case study, we developed a deep learning method incorporating PSC (DeepPSC) to reconstruct protein backbone structures from alpha carbon traces. DeepPSC outperformed all the methods currently available for this task. This PSC approach provides a useful tool for protein structure representation, and for the application of deep learning in protein structure prediction and protein engineering. Copy rights belong to original authors. Visit the link for more info

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Bio-informatic Analysis of Missense Single Nucleotide Polymorphisms (SNPs) in Human CD38 Gene Associated with B-Chronic Lymphocytic Leukemia

PaperPlayer biorxiv biophysics

Play Episode Listen Later Aug 12, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.12.241976v1?rss=1 Authors: Fadl, H. A. O., Abdelmoneim, A. H., Elbager, S. G. Abstract: ABSTRACT Background: CLL: Chronic lymphocytic leukemia is a chronic type of haematological malignancies that evoked from lymph proliferative origin of bone marrow and secondary lymphoid tissue, resultant in proliferation and progressive accumulation of distinct monoclonal CD5 /CD19 /CD23 B lymphocytes in the bone marrow, peripheral blood, and lymphatic organs. CD38 is a multifunctional ecto-enzyme, known to be a direct contributor in pathogenesis of CLL by poorly understood mechanism. Even though , it highly expressed in CLL. At specific position of CD38 gene sequence, substitution of single nucleotide may result in change in amino acid that ends by consequent alteration of protein structure. Aim: To study CD38 polymorphism and to predict its effect on structure and subsequently function of CD38 molecule. Methodology and Result: The bioinformatic analysis of CD38 gene had been carried out by using several soft wares. Functional analysis by SIFT,Polyphen2, and PROVEAN reveled 12 deleterious SNPs. These SNPs were further analyzed by SNAP2, SNP@GO. PMut, STRING and other soft wars. Furthermore, Stability analysis was done using I-Mutant and MUpro software where seven SNPs were found to decrease the stability of the protein by I-Mutant ,while two SNPs increase it. At the same time, eight SNPs were found to decrease the stability by Mupro software while only one SNP is predicted to increase it. Finally, Physiochemical analysis was done using Project Hope. Conclusion: In summary, CD38 genotype seems to have twelve SNP that possibly will result in deleterious effect on Protein Structure. This genetic variation eventually will lead to alteration in potential molecule functions .Which effect the progression of CLL By the end. Keywords: B-Chronic lymphocytic leukemia, missense single nucleotide polymorphism, and CD38 gene. Copy rights belong to original authors. Visit the link for more info

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Protein Structure Refinement Guided by Atomic Packing Frustration Analysis

Play Episode Listen Later Jul 20, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.19.211169v1?rss=1 Authors: Chen, M., Chen, X., Jin, S., Lu, W., Lin, X., Wolynes, P. G. Abstract: 1Recent advances in machine learning, bioinformatics and the understanding of the folding problem have enabled efficient predictions of protein structures with moderate accuracy, even for targets when there is little information from templates. All-atom molecular dynamics simulations provide a route to refine such predicted structures, but unguided atomistic simulations, even when lengthy in time, often fail to eliminate incorrect structural features that would allow the structure to become more energetically favorable owing to the necessity of making large scale motions and overcoming energy barriers for side chain repacking. In this study, we show that localizing packing frustration at atomic resolution by examining the statistics of the energetic changes that occur when the local environment of a site is changed allows one to identify the most likely locations of incorrect contacts. The global statistics of atomic resolution frustration in structures that have been predicted using various algorithms provide strong indicators of structural quality when tested over a database of 20 targets from previous CASP experiments. Residues that are more correctly located turn out to be more minimally frustrated than more poorly positioned sites. These observations provide a diagnosis of both global and local quality of predicted structures, and thus can be used as guidance in all-atom refinement simulations of the 20 targets. Refinement simulations guided by atomic packing frustration turn out to be quite efficient and significantly improve the quality of the structures. Copy rights belong to original authors. Visit the link for more info

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World’s Largest Supercomputer v. Biology’s Toughest Problems

a16z

Play Episode Listen Later Jun 14, 2020 32:31

Proteins are molecular machines that must first assemble themselves to function. But how does a protein, which is produced as a linear string of amino acids, assume the complex three-dimensional structure needed to carry out its job? That's where Folding at Home comes in. Folding at Home is a sophisticated computer program that simulates the way atoms push and pull on each other, applied to the problem of protein dynamics, aka "folding". These simulations help researchers understand protein function and to design drugs and antibodies to target them. Folding at Home is currently studying key proteins from the virus that causes COVID-19 to help therapeutic development. Given the extreme complexity of these simulations, they require an astronomical amount of compute power. Folding at Hold solves this problem with a distributed computing framework: it breaks up the calculations in the smaller pieces that can be run on independent computers. Users of Folding at Home - millions of them today - donate the spare compute power on their PCs to help run these simulations. This aggregate compute power represents the largest super computer in the world: currently 2.4 exaFLOPS!Folding at Home was launched 20 years ago this summer in the lab of Vijay Pande at Stanford. In this episode, Vijay (now a general partner at a16z) is joined by his former student and current director of Folding at Home, Greg Bowman, an associate professor at Washington University in St. Louis, and Lauren Richardson. We discuss the origins of the Folding at Home project along with its connection to SETI@Home and Napster; also the scientific and technical advances needed to solve the complex protein folding and distributed computing problems; and importantly what does understanding protein dynamics actually achieve?

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1:8. 4 Levels of Protein Structure

MCAT Modules - Review

Play Episode Listen Later Jun 7, 2020 5:33

Discusses the four levels of protein structure and what factors allow for distinction between them.

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Scientists Create Music to Represent Protein Structure of the Coronavirus - April 29, 2020

Science & Technology - Voice of America

Play Episode Listen Later Apr 29, 2020 4:57

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64 - Protein Structure I (w/ Shannon Speer!)

Curioscity

Play Episode Listen Later Apr 2, 2020 34:32

After an episode about lipids and nucleic acids, it’s time to forget about sugars and talk about my favorite bio-molecules: proteins. What are amino acids? What are some protein functions? Why do proteins’ structure often inform their function? Let’s learn to be scientifically conversational. For all references and supplemental information, you can navigate to ascienceshow.com.

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Podcast 21: Interview with Eva Nogales: How Cryogenic Electron Microscopy Informs Molecular Biology

Lessons from Lab and Life

Play Episode Listen Later Jan 29, 2020 31:56

Listen to Dr. Eva Nogales describe how cryo-electron microscopy addresses the challenge of visualizing macromolecular structures.

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5.3 Four Levels of Protein Structure

AP Bio Lectures

Play Episode Listen Later Sep 16, 2019 38:57

Talks about the four levels of protein structure and how amino acids/chemical reactions determine the shape of proteins.

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Protein Structure Deep Learning with Mohammed Al Quraishi

Machine Learning – Software Engineering Daily

Play Episode Listen Later Apr 15, 2019 60:39

RECENT UPDATES: Podsheets is our open source set of tools for managing podcasts and podcast businesses New version of Software Daily, our app and ad-free subscription service Software Daily is looking for help with Android engineering, QA, machine learning, and more FindCollabs Hackathon has ended–winners will probably be announced by the time this episode airs; The post Protein Structure Deep Learning with Mohammed Al Quraishi appeared first on Software Engineering Daily.

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FTM31 Protein Structure and Function

Medical School Audio

Play Episode Listen Later Mar 12, 2018 50:17

(Biochemistry) --- Support this podcast: https://anchor.fm/brad-richardson/support

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Kelsey Kean Elucidating Protein Structure with Crystals

Inspiration Dissemination

Play Episode Listen Later Apr 23, 2017 38:04

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X-rays for drug discovery

Translational Medicine

Play Episode Listen Later Sep 23, 2016 7:40

Professor Frank von Delft works to ensure that X-ray structures can serve as a routine and predictive tool for generating novel chemistry for targeting proteins. In the process of drug discovery, X-ray crystallography is the most sensitive way to find out which compounds bind to a target protein. Recent advances in technology allow researchers to test many more compounds, much more rapidly. The ultimate aim is to bring much needed new treatments to patients.

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X-rays for drug discovery

Translational and Clinical

Play Episode Listen Later Sep 23, 2016 7:40

Professor Frank von Delft works to ensure that X-ray structures can serve as a routine and predictive tool for generating novel chemistry for targeting proteins. In the process of drug discovery, X-ray crystallography is the most sensitive way to find out which compounds bind to a target protein. Recent advances in technology allow researchers to test many more compounds, much more rapidly. The ultimate aim is to bring much needed new treatments to patients.

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X-ray crystallography Shining a Light on Protein Structure

Moraine Valley Community College Library Podcast

Play Episode Listen Later Sep 15, 2016

Dr. David Neau describes the technique and show some examples of how a protein's structure reveals insights into its function.

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X-ray crystallography Shining a Light on Protein Structure

Moraine Valley Community College Library Podcast

Play Episode Listen Later Sep 15, 2016

Dr. David Neau describes the technique and show some examples of how a protein's structure reveals insights into its function.

shining x ray crystallography protein structure

Bioinformatics at the heart of biology and genomics medicine

Department of Statistics

Play Episode Listen Later Apr 27, 2016 49:21

The Ninth annual Florence Nightingale Lecture, given by Professor Dame Janet Thornton, European Bioinformatics Institute, Cambridge. Held on Thursday 21st April 2016. Florence Nightingale was a celebrated nurse who served the British Army during the Crimean War. Her ground-breaking use of data visualisation turned a spotlight on the terrible hospital sanitation, and brought the issue to the attention of the British establishment. She went on to epidemiological work in India, statistically proving the importance of sanitation to health. Her work is a testament both to the power of Statistics to change the world, and the broad range of backgrounds from which the contributors to the discipline are drawn. The Florence Nightingale Lecture aims to celebrate that diversity by inviting a distinguished speaker to lecture on a statistical topic of their choice, one which can inspire the current generation as Nightingale herself did.

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Bioinformatics at the heart of biology and genomics medicine

Department of Statistics

Play Episode Listen Later Apr 27, 2016 49:21

The Ninth annual Florence Nightingale Lecture, given by Professor Dame Janet Thornton, European Bioinformatics Institute, Cambridge. Held on Thursday 21st April 2016. Florence Nightingale was a celebrated nurse who served the British Army during the Crimean War. Her ground-breaking use of data visualisation turned a spotlight on the terrible hospital sanitation, and brought the issue to the attention of the British establishment. She went on to epidemiological work in India, statistically proving the importance of sanitation to health. Her work is a testament both to the power of Statistics to change the world, and the broad range of backgrounds from which the contributors to the discipline are drawn. The Florence Nightingale Lecture aims to celebrate that diversity by inviting a distinguished speaker to lecture on a statistical topic of their choice, one which can inspire the current generation as Nightingale herself did.

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10d. How NMR and Cyro-EM Find Protein Structure

Play Episode Listen Later Oct 23, 2015 18:40

cyro protein structure

10d. How NMR and Cyro-EM Find Protein Structure

Play Episode Listen Later Oct 23, 2015 18:40

Description Not Provided.

cyro protein structure

8c. Introduction to Protein Structure, Dihedral Angles, and Ramachandran Plots

angles plots ramachandran protein structure

Play Episode Listen Later Oct 19, 2015 23:36

Description Not Provided.

8c. Introduction to Protein Structure, Dihedral Angles, and Ramachandran Plots