Download or read book Protein Structure Accuracy Prediction with Deep Learning and Its Application to Structure Prediction and Design written by Naozumi Hiranuma and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the rules of protein structure folding has always been one of the central goals in computational biology. Deep learning is gaining popularity in protein machine learning due to its ability to learn complex functions on large amounts of protein geometry data. To help understand the rules of protein folding better, we developed neural networks (DeepAccNet and Pluto) that estimate the error in protein models. In other words, these networks estimate how much a computationally modeled protein structure deviates from its experimentally determined conformation. Approximately two million conformations from 21000 protein sequences located at different local energy minima with a large diversity of errors were sampled and used for training. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution. The network should be broadly helpful in assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. The DeepAccNet methods were selected as top-performing methods for the estimation of model accuracy (EMA) category in CASP14. We extended the accuracy prediction models for proteins to more general chemistry by training graph neural networks on a wide variety of protein and non-protein datasets. We showed that the resulting framework (GAAP) successfully estimates the accuracy of non-protein molecules, such as peptides and Protein-DNA complexes. Our results illustrate how deep learning can impact the efficiency and accuracy of large-scale simulations for both modeling and designing of molecules.

Download or read book AlphaFold 3 A Revolution in Protein Structure Prediction written by StoryBuddiesPlay and published by StoryBuddiesPlay. This book was released on 2024-05-19 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: AlphaFold 3: Unveiling the Secrets of Life, One Molecule at a Time Demystifying the intricate world of proteins just got a whole lot easier with the arrival of AlphaFold 3. This groundbreaking AI model, developed by DeepMind and Isomorphic Labs, has revolutionized protein structure prediction, a field that has captivated scientists for decades. No longer limited to laborious experimental methods, AlphaFold 3 utilizes the power of deep learning to accurately predict protein structures from their amino acid sequences. This capability unlocks a treasure trove of possibilities for various scientific disciplines. From accelerating drug discovery and personalized medicine to understanding complex diseases and designing novel biomaterials, AlphaFold 3 stands poised to transform our understanding of life at the molecular level. Here's what you'll discover in this comprehensive guide: The Protein Folding Problem Explained: Dive into the challenges of traditionally predicting protein structures and the significance of solving this scientific puzzle. The Rise of Deep Learning: A New Hope for Protein Science: Explore how deep learning has emerged as a powerful tool for tackling complex scientific problems like protein structure prediction. A Deep Dive into AlphaFold: Birth of a Revolutionary AI Model: Learn about the development of AlphaFold by DeepMind, its groundbreaking architecture, and its impact on the scientific community. From AlphaFold 2 to AlphaFold 3: Pushing the Boundaries of Accuracy: Witness the continuous advancements in AlphaFold's capabilities, culminating in AlphaFold 3's ability to predict structures of a wider range of biomolecules beyond proteins. Revolutionizing Drug Discovery: How AlphaFold 3 is Changing the Game: Discover how AlphaFold 3 is accelerating the identification and design of new drugs by providing precise structural insights into protein-drug interactions. Unlocking the Secrets of Diseases: A New Lens for Diagnosis and Treatment: Explore how AlphaFold 3 is transforming our understanding of diseases at the molecular level, paving the way for earlier diagnosis, personalized medicine, and the development of new therapies. Beyond Proteins: Expanding the Horizons of AlphaFold 3: Uncover the exciting potential of AlphaFold 3 in predicting the structures of DNA, RNA, and other biomolecules, leading to a more holistic view of cellular processes. The Future of Protein Science: A Collaborative and Responsible Approach: Delve into the ethical considerations surrounding AlphaFold 3 and the importance of responsible development, open access, and international collaboration to maximize its benefits for humanity. This blog post goes beyond just summarizing the features of AlphaFold 3. It provides a compelling narrative that explores the history, scientific significance, and future potential of this groundbreaking technology. Whether you're a scientist, student, or simply curious about the future of scientific discovery, this guide offers a comprehensive exploration of AlphaFold 3 and its transformative impact on the world of science.

Download or read book The Science Behind AlphaFold written by StoryBuddiesPlay and published by StoryBuddiesPlay. This book was released on 2024-06-03 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: AlphaFold, a groundbreaking AI system, has cracked the code on protein structure prediction, a challenge that baffled scientists for decades. This book explores the science behind AlphaFold, delving into deep learning, big data, and the inner workings of this remarkable program. Uncover how AlphaFold is revolutionizing protein science, with the potential to accelerate drug discovery, personalize medicine, and design innovative materials. This comprehensive guide explores: The significance of protein structures and the challenges of prediction How AlphaFold leverages deep learning and vast data resources The process of protein structure prediction with AlphaFold, including its strengths and limitations The ethical considerations surrounding AI in protein science The exciting future applications of AlphaFold in various scientific fields Whether you're a scientist, student, or simply curious about the future of biology, this book provides a clear and engaging exploration of AlphaFold and its transformative impact on protein science.

Download or read book Introduction to Protein Structure Prediction written by Huzefa Rangwala and published by John Wiley & Sons. This book was released on 2011-03-16 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.

Download or read book Machine Learning Meets Quantum Physics written by Kristof T. Schütt and published by Springer Nature. This book was released on 2020-06-03 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.

Download or read book Prediction of Protein Secondary Structure written by Yaoqi Zhou and published by Humana. This book was released on 2016-10-28 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thorough volume explores predicting one-dimensional functional properties, functional sites in particular, from protein sequences, an area which is getting more and more attention. Beginning with secondary structure prediction based on sequence only, the book continues by exploring secondary structure prediction based on evolution information, prediction of solvent accessible surface areas and backbone torsion angles, model building, global structural properties, functional properties, as well as visualizing interior and protruding regions in proteins. Written for the highly successful Methods in Molecular Biology series, the chapters include the kind of detail and implementation advice to ensure success in the laboratory. Practical and authoritative, Prediction of Protein Secondary Structure serves as a vital guide to numerous state-of-the-art techniques that are useful for computational and experimental biologists.

Download or read book Protein Structure Prediction written by Anna Tramontano and published by John Wiley & Sons. This book was released on 2006-02-20 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: While most textbooks on bioinformatics focus on genetic algorithms and treat protein structure prediction only superficially, this course book assumes a novel and unique focus. Adopting a didactic approach, the author explains all the current methods in terms of their reliability, limitations and user-friendliness. She provides practical examples to help first-time users become familiar with the possibilities and pitfalls of computer-based structure prediction, making this a must-have for students and researchers.

Download or read book Applications of Deep Neural Networks to Protein Structure Prediction written by Chao Fang (Computer scientist) and published by . This book was released on 2018 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Protein secondary structure, backbone torsion angle and other secondary structure features can provide useful information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this dissertation, several new deep neural network architectures are proposed for protein secondary structure prediction: deep inception-inside-inception (Deep3I) networks and deep neighbor residual (DeepNRN) networks for secondary structure prediction; deep residual inception networks (DeepRIN) for backbone torsion angle prediction; deep dense inception networks (DeepDIN) for beta turn prediction; deep inception capsule networks (DeepICN) for gamma turn prediction. Every tool was then implemented as a standalone tool integrated into MUFold package and freely available to research community. A webserver called MUFold-SS-Angle is also developed for protein property prediction. The input feature to those deep neural networks is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, HHBlits profile and/or predicted shape string. The deep architecture enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, the proposed deep neural architectures outperformed the best existing methods and other deep neural networks significantly: The proposed DeepNRN achieved highest Q8 75.33, 72.9, 70.8 on CASP 10, 11, 12 higher than previous state-of-the-art DeepCNF-SS with 71.8, 72.3, and 69.76. The proposed MUFold-SS (Deep3I) achieved highest Q8 76.47, 74.51, 72.1 on CASP 10, 11, 12. Compared to the recently released state-of-the-art tool, SPIDER3, DeepRIN reduced the Psi angle prediction error by more than 5 degrees and the Phi angle prediction error by more than 2 degrees on average. DeepDIN outperformed significantly BetaTPred3 in both two-class and nine-class beta turn prediction on benchmark BT426 and BT6376. DeepICN is the first application of using capsule network to biological sequence analysis and outperformed all previous gamma-turn predictors on benchmark GT320.

Download or read book Protein Structure Prediction written by David Webster and published by Springer Science & Business Media. This book was released on 2008-02-03 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: The number of protein sequences grows each year, yet the number of structures deposited in the Protein Data Bank remains relatively small. The importance of protein structure prediction cannot be overemphasized, and this volume is a timely addition to the literature in this field. Protein Structure Prediction: Methods and Protocols is a departure from the normal Methods in Molecular Biology series format. By its very nature, protein structure prediction demands that there be a greater mix of theoretical and practical aspects than is normally seen in this series. This book is aimed at both the novice and the experienced researcher who wish for detailed inf- mation in the field of protein structure prediction; a major intention here is to include important information that is needed in the day-to-day work of a research scientist, important information that is not always decipherable in scientific literature. Protein Structure Prediction: Methods and Protocols covers the topic of protein structure prediction in an eclectic fashion, detailing aspects of pred- tion that range from sequence analysis (a starting point for many algorithms) to secondary and tertiary methods, on into the prediction of docked complexes (an essential point in order to fully understand biological function). As this volume progresses, the authors contribute their expert knowledge of protein structure prediction to many disciplines, such as the identification of motifs and domains, the comparative modeling of proteins, and ab initio approaches to protein loop, side chain, and protein prediction.

Download or read book Machine Learning Algorithms for Characterization and Prediction of Protein Structural Properties written by Maxim V Shapovalov and published by . This book was released on 2019 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteins are large biomolecules which are functional building blocks of living organisms. There are about 22,000 protein-coding genes in the human genome. Each gene encodes a unique protein sequence of a typical 100-1000 length which is built using a 20-letter alphabet of amino acids. Each protein folds up into a unique 3D shape that enables it to perform its function. Each protein structure consists of some number of helical segments, extended segments called sheets, and loops that connect these elements. In the last two decades, machine learning methods coupled with exponentially expanding biological knowledge databases and computational power are enabling significant progress in the field of computational biology. In this dissertation, I carry out machine learning research for three major interconnected problems to advance protein structural biology as a field. A separate chapter in this dissertation is devoted to each problem. After the three chapters I conclude this doctoral research with a summary and direction of our future work. Chapter 1 describes design, training and application of a convolutional neural network (SecNet) to achieve 84% accuracy for the 60-year-old problem of predicting protein secondary structure given a protein sequence. Our accuracy is 2-3% better than any previous result, which had only risen 5% in last 20 years. We identified the key factors for successful prediction in a detailed ablation study. A paper submitted for publication includes our secondary-structure prediction software, data set generation, and training and testing protocols [1]. Chapter 2 characterizes the design and development of a protocol for clustering of beta turns, i.e. short structural motifs responsible for U-turns in protein loops. We identified 18 turn types, 11 of which are newly described [2]. We also developed a turn library and cross-platform software for turn assignment in new structures. In Chapter 3 I build upon the results from these two problems and predict geometries in loops of unknown structure with custom Residual Neural Networks (ResNet). I demonstrate solid results on (a) locating turns and predicting 18 types and (b) prediction of backbone torsion angles in loops. Given the recent progress in machine learning, these two results provide a strong foundation for successful loop modeling and encourage us to develop a new loop structure prediction program, a critical step in protein structure prediction and modeling.

Download or read book Computational Protein Design written by Ilan Samish and published by Humana. This book was released on 2016-12-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim this volume is to present the methods, challenges, software, and applications of this widespread and yet still evolving and maturing field. Computational Protein Design, the first book with this title, guides readers through computational protein design approaches, software and tailored solutions to specific case-study targets. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Protein Design aims to ensure successful results in the further study of this vital field.

Download or read book Introduction to AlphaFold 3 written by StoryBuddiesPlay and published by StoryBuddiesPlay. This book was released on 2024-06-09 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: AlphaFold 3: Unveiling the Secrets of Life, One Protein at a Time Proteins are the workhorses of our cells, playing a crucial role in everything from building tissues to fighting off disease. But understanding how a protein functions depends on its unique 3D structure, a mystery that has long puzzled scientists. Enter AlphaFold 3, a groundbreaking AI tool that is revolutionizing protein structure prediction. This comprehensive guide unravels the fascinating world of AlphaFold 3 and its impact on the field of bioinformatics. We'll delve into the protein folding problem and the limitations of traditional methods. Then, we'll explore how AlphaFold 3 leverages the power of machine learning to predict protein structures with unprecedented accuracy. Demystifying AI for Everyone: No prior knowledge of AI is required! We'll break down complex concepts into clear and concise explanations, allowing you to grasp the core principles behind AlphaFold 3. Beyond Protein Structure: AlphaFold 3's capabilities extend beyond just proteins. We'll explore how it can predict interactions between different molecules, paving the way for advancements in drug discovery and understanding complex biological processes. Unlocking a Treasure Trove of Information: Discover the AlphaFold Protein Structure Database, a freely available online resource that grants access to a vast collection of predicted protein structures. Learn how to navigate the database and utilize this valuable information to accelerate your research. A Glimpse into the Future: This guide doesn't just focus on the present. We'll explore the exciting potential of AlphaFold 3 and AI in bioinformatics. Imagine personalized medicine tailored to individual protein structures, or the development of entirely new biomaterials with desired properties. Here's what you'll gain from this comprehensive guide: A clear understanding of the protein folding problem and its significance. Demystified explanations of AI concepts relevant to AlphaFold 3. Exploration of AlphaFold 3's capabilities beyond protein structure prediction. Insights into the AlphaFold Protein Structure Database and its functionalities. A glimpse into the transformative future of AI in bioinformatics. Whether you're a student, researcher, or simply curious about the latest advancements in science, this guide is your roadmap to unlocking the secrets of life with AlphaFold 3.

Download or read book Protein Structure Prediction written by Daisuke Kihara and published by Humana. This book was released on 2021-07-18 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thorough new edition explores web servers and software for protein structure prediction and modeling that are freely available to the academic community. Taking into account the numerous advances in the computational protein structure prediction/modeling field, the book includes residue-contact prediction via deep learning, a wide variety of protein docking models, as well as cryo-electron microscopy (cryo-EM) techniques. Written by renowned experts in the field and for the highly successful Methods in Molecular Biology series, chapters include the kind of key detail and implementation advice necessary for researchers to achieve optimal results in their own work. Authoritative and fully updated, Protein Structure Prediction, Fourth Edition is a practical and immediately useful guide for biology researchers working toward modeling protein structures./div/div/div/div/div/div/div/div/div/div/div/div/div/div/div/div/divdiv

Download or read book Protein Structure Prediction A Practical Approach written by Michael J. E. Sternberg and published by Oxford University Press, USA. This book was released on 1996-11-28 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-dimensional structure of proteins is a key factor in their biological activity. There is an increasing need to be able to predict the structure of a protein once its amino-acid sequence is known; this book presents practical methods of achieving that ambitious aim, using the latest computer modelling algorithms. - ;The prediction of the three-dimensional structure of a protein from its sequence is a problem faced by an ever-increasing number of biological scientists as they strive to utilize genetic information. The increasing sizes of the sequence and structural databases, the improvements in computing power, and the deeper understanding of the principles of protein structure have led to major developments in the field in the last few years. This book presents practical computer-based methods using the latest computer modelling algorithms. -

Download or read book Change of Representation in Machine Learning and an Application to Protein Structure Prediction written by Thomas Richard Ioerger and published by . This book was released on 1996 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Novel Machine Learning Approach for Protein Structure Prediction written by Ken Nagata and published by . This book was released on 2014 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: The side-chain prediction and residue-residue contact prediction are sub-problems in the protein structure prediction. Both predictions are important for protein prediction and other applications. We have developed a new algorithm, SIDEpro, for the side-chain prediction where an energy function for each rotamer in a structure is computed additively over pairs of contacting atoms. A family of 156 neural networks indexed by amino acids and contacting atom types is used to compute these rotamer energies as a function of atomic contact distances. Although direct energy targets are not available for training, the neural networks can still be optimized by converting the energies to probabilities and optimizing these probabilities using Markov Chain Monte Carlo methods. The resulting predictor SIDEpro makes predictions by initially setting the rotamer probabilities for each residue from a backbone-dependent rotamer library, then iteratively updating these probabilities using the trained neural networks. After convergences of the probabilities, the side-chains are set to the highest probability rotamer. Finally, a post processing clash reduction step is applied to the models. SIDEpro represents a significant improvement in speed and a modest, but statistically significant, improvement in accuracy when compared with the state-of-the-art for rapid side-chain prediction method SCWRL4 on the 379 protein test set of SCWRL4. Using the SCWRL4 test set, SIDEpro's accuracy (X1 86.14%, X1+2 74.15%) is slightly better than SCWRL4-FRM (X1 85.43%, X1+2 73.47%) and it is 7.0 times faster. SIDEpro can also predict the side chains of proteins containing non-standard amino acids, including 15 of the most frequently observed PTMs in the Protein Data Bank and all types of phosphorylation. For PTMs, the X1 and X1+2 accuracies are comparable with those obtained for the precursor amino acid, and so are the RMSD values for the atoms shared with the precursor amino acid. In addition, SIDEpro can accommodate any PTM or unnatural amino acid, thus providing a flexible prediction system for high-throughput modeling of proteins beyond the standard amino acids. We have also developed a novel machine learning approach for contact map prediction using three steps of increasing resolution. First, we use 2D recursive neural networks to predict coarse contacts and orientations between secondary structure elements. Second, we use an energy-based method to align secondary structure elements and predict contact probabilities between residues in contacting alpha-helices or strands. Third, we use a deep neural network architecture to organize and progressively refine the prediction of contacts, integrating information over both space and time. We train the architecture on a large set of non-redundant proteins and test it on a large set of non-homologous domains, as well as on the set of protein domains used for contact prediction in the two most recent CASP8 and CASP9 experiments. For long-range contacts, the accuracy of the new CMAPpro predictor is close to 30%, a significant increase over existing approaches. Both SIDEpro and CMAPpro are part of the SCRATCH suite of predictors and available from: http://scratch.proteomics.ics.uci.edu/.

Download or read book Protein Structure Prediction written by Mohammed Zaki and published by Springer Science & Business Media. This book was released on 2007-09-12 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers elements of both the data-driven comparative modeling approach to structure prediction and also recent attempts to simulate folding using explicit or simplified models. Despite the unsolved mystery of how a protein folds, advances are being made in predicting the interactions of proteins with other molecules. Also rapidly advancing are the methods for solving the inverse folding problem, the problem of finding a sequence to fit a structure. This book focuses on the various computational methods for prediction, their successes and their limitations, from the perspective of their most well known practitioners.