Distance-Based Protein Folding Powered by Deep Learning
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Homology Modeling and Analysis of Structure Predictions of the Bovine Rhinitis B Virus RNA Dependent RNA Polymerase (Rdrp)
Int. J. Mol. Sci. 2012, 13, 8998-9013; doi:10.3390/ijms13078998 OPEN ACCESS International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Article Homology Modeling and Analysis of Structure Predictions of the Bovine Rhinitis B Virus RNA Dependent RNA Polymerase (RdRp) Devendra K. Rai and Elizabeth Rieder * Foreign Animal Disease Research Unit, United States Department of Agriculture, Agricultural Research Service, Plum Island Animal Disease Center, Greenport, NY 11944, USA; E-Mail: [email protected] * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +1-631-323-3177; Fax: +1-631-323-3006. Received: 3 May 2012; in revised form: 3 July 2012 / Accepted: 11 July 2012 / Published: 19 July 2012 Abstract: Bovine Rhinitis B Virus (BRBV) is a picornavirus responsible for mild respiratory infection of cattle. It is probably the least characterized among the aphthoviruses. BRBV is the closest relative known to Foot and Mouth Disease virus (FMDV) with a ~43% identical polyprotein sequence and as much as 67% identical sequence for the RNA dependent RNA polymerase (RdRp), which is also known as 3D polymerase (3Dpol). In the present study we carried out phylogenetic analysis, structure based sequence alignment and prediction of three-dimensional structure of BRBV 3Dpol using a combination of different computational tools. Model structures of BRBV 3Dpol were verified for their stereochemical quality and accuracy. The BRBV 3Dpol structure predicted by SWISS-MODEL exhibited highest scores in terms of stereochemical quality and accuracy, which were in the range of 2Å resolution crystal structures. The active site, nucleic acid binding site and overall structure were observed to be in agreement with the crystal structure of unliganded as well as template/primer (T/P), nucleotide tri-phosphate (NTP) and pyrophosphate (PPi) bound FMDV 3Dpol (PDB, 1U09 and 2E9Z). -
Arxiv:1911.09811V1 [Physics.Bio-Ph] 22 Nov 2019 Help to Associate a Folded Structure to a Protein Sequence
Machine learning for protein folding and dynamics Frank Noé Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195 Berlin, Germany Gianni De Fabritiis Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Doctor Aiguader 88, 08003 Barcelona, Spain, and Institucio Catalana de Recerca i Estudis Avanats (ICREA), Passeig Lluis Companys 23, Barcelona 08010, Spain Cecilia Clementi Center for Theoretical Biological Physics, and Department of Chemistry, Rice University, 6100 Main Street, Houston, Texas 77005, United States Abstract Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their sequences are now heavily based on machine learning tools. The way simulations are performed to explore the energy landscape of protein systems is also changing as force-fields are started to be designed by means of machine learning methods. These methods are also used to extract the essential information from large simulation datasets and to enhance the sampling of rare events such as folding/unfolding transitions. While significant challenges still need to be tackled, we expect these methods to play an important role on the study of protein folding and dynamics in the near future. We discuss here the recent advances on all these fronts and the questions that need to be addressed for machine learning approaches to become mainstream in protein simulation. Introduction During the last couple of decades advances in artificial intelligence and machine learning have revolu- tionized many application areas such as image recognition and language translation. -
Protein Contact Map Prediction Using Multiple Sequence Alignment
bioRxiv preprint doi: https://doi.org/10.1101/2021.05.12.443740; this version posted May 13, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Protein contact map prediction using multiple sequence alignment dropout and consistency learning for sequences with less homologs Abstract The prediction of protein contact map needs enough normalized number of effective sequence (Nf) in multiple sequence alignment (MSA). When Nf is small, the predicted contact maps are often not satisfactory. To solve this problem, we randomly selected a small part of sequence homologs for proteins with large Nf to generate MSAs with small Nf. From these MSAs, input features were generated and were passed through a consistency learning network, aiming to get the same results when using the features generated from the MSA with large Nf. The results showed that this method effectively improves the prediction accuracy of protein contact maps with small Nf. 1. Introduction De novo protein structure prediction has been a long-standing challenge in computational biology. Since 2015, protein contact map assisted protein structure prediction has shown its great advantage and thus has been implemented in almost all cutting-edge de novo protein structure prediction tools, such as RaptorX1, I-TASSER2, AlphaFold13, trRosetta4 and etc5-8. Protein contact map is predicted by using features extracted from multiple sequences alignment (MSA). Such features include mutual information and direct-coupling analysis (DCA). Mutual information was often used in early stage studies to predict co-evolving residues pairs.9-11 However, mutual information includes both direct coupling pairs and indirect coupling pairs, while the latter are bioRxiv preprint doi: https://doi.org/10.1101/2021.05.12.443740; this version posted May 13, 2021. -
Simultaneous Identification of Specifically Interacting Paralogs and Interprotein Contacts by Direct Coupling Analysis
Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis Thomas Gueudréa,1, Carlo Baldassia,b,1, Marco Zamparoa, Martin Weigtc,2, and Andrea Pagnania,b,2 aDepartment of Applied Science and Technology, Politecnico di Torino, 10129 Torino, Italy; bHuman Genetics Foundation, Molecular Biotechnology Center, 10126 Torino, Italy; and cSorbonne Universités, UPMC Université Paris 06, CNRS, Biologie Computationnelle et Quantitative, Institut de Biologie Paris Seine, 75005 Paris, France Edited by Barry Honig, Howard Hughes Medical Institute, Columbia University, New York, NY, and approved September 16, 2016 (received for review May 12, 2016) Understanding protein−protein interactions is central to our under- unphysiological treatment needed for protein purification, enrich- standing of almost all complex biological processes. Computational ment, and crystallization. It is therefore tempting to use the ex- tools exploiting rapidly growing genomic databases to characterize ponentially increasing genomic databases to design in silico protein−protein interactions are urgently needed. Such methods techniques for identifying protein−protein interactions (cf. refs. 3 should connect multiple scales from evolutionary conserved interac- and 4). Prominent techniques, to date, include the search for tions between families of homologous proteins, over the identifica- colocalization of genes on the genome (e.g., operons in bacteria) tion of specifically interacting proteins in the case of multiple -
Inter-Residue, Inter-Protein and Inter-Family Coevolution: Bridging the Scales
Inter-residue, inter-protein and inter-family coevolution: bridging the scales Hendrik Szurmant1+, Martin Weigt2+ Affiliations: 1. Department of Basic Medical Sciences, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona CA 91766, USA 2. Sorbonne Universités, UPMC Université Paris 06, CNRS, Biologie Computationnelle et Quantitative - Institut de Biologie Paris Seine, 75005 Paris, France + to whom correspondence might be addressed Corresponding authors: Hendrik Szurmant: 309 E. 2nd Street, Pomona, CA 91766-1854, USA, tel. +1-909-706-3938, email: [email protected] Martin Weigt: LCQB UMR7238, 4 place Jussieu 75005 Paris, France, tel. +33 1 44 27 73 68, email: [email protected] Short title: Coevolutionary modeling of interacting proteins Abstract Interacting proteins coevolve at multiple but interconnected scales, from the residue-residue over the protein-protein up to the family-family level. The recent accumulation of enormous amounts of sequence data allows for the development of novel, data-driven computational approaches. Notably, these approaches can bridge scales within a single statistical framework. While being currently applied mostly to isolated problems on single scales, their immense potential for an evolutionary informed, structural systems biology is steadily emerging. Highlights • Coevolutionary modeling has an immense potential in structural systems biology. • Interaction between two protein families can be detected. • Specific interaction partners inside families can be deduced. • Contact residues between interacting proteins can be predicted. • Predicted inter-protein contacts can guide in silico protein-complex assembly. Introduction The importance of protein-protein interactions (PPI) in living systems is unquestioned. However, concerning PPI, different research communities are interested in differing aspects. -
Computational Protein Structure Prediction Using Deep Learning
COMPUTATIONAL PROTEIN STRUCTURE PREDICTION USING DEEP LEARNING _______________________________________ A Dissertation presented to the Faculty of the Graduate School at the University of Missouri-Columbia _______________________________________________________ In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy _____________________________________________________ by ZHAOYU LI Professor Yi Shang, Dissertation Advisor May 2020 The undersigned, appointed by the dean of the Graduate School, have examined the dissertation entitled COMPUTATIONAL PROTEIN STRUCTURE PREDICTION USING DEEP LEARNING presented by Zhaoyu Li, a candidate for the degree of Doctor of Philosophy and hereby certify that, in their opinion, it is worthy of acceptance. Dr. Yi Shang Dr. Dong Xu Dr. Jianlin Cheng Dr. Ioan Kosztin DEDICATION To my wife Xian, my parents, and my son Ethan. ACKNOWLEDGEMENTS I would like to take the opportunity to thank the following people who have supported me through my PhD program. Without the support of them, this thesis would not have been possible. First, I would like to thank my advisor, Dr. Yi Shang. He has been my advisor since my master’s study. He opened the door of research for me, showed me instructions, guided me, supported me, encouraged me, and inspired me whenever I need help. It is a great pleasure to work with Dr. Shang together. I would also like to thank my committee members, Dr. Dong Xu, Dr. Jianlin Cheng, and Dr. Ioan Kosztin. They have been giving me constructive suggestions and advices to my dissertation and helping me solve problems in my research. Their support is essential, and I appreciate their time and efforts very much. I would also like to thank other lab mates. -
1 Codon-Level Information Improves Predictions of Inter-Residue Contacts in Proteins 2 by Correlated Mutation Analysis 3
1 Codon-level information improves predictions of inter-residue contacts in proteins 2 by correlated mutation analysis 3 4 5 6 7 8 Etai Jacob1,2, Ron Unger1,* and Amnon Horovitz2,* 9 10 11 1The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat- 12 Gan, 52900, 2Department of Structural Biology 13 Weizmann Institute of Science, Rehovot 7610001, Israel 14 15 16 *To whom correspondence should be addressed: 17 Amnon Horovitz ([email protected]) 18 Ron Unger ([email protected]) 19 1 20 Abstract 21 Methods for analysing correlated mutations in proteins are becoming an increasingly 22 powerful tool for predicting contacts within and between proteins. Nevertheless, 23 limitations remain due to the requirement for large multiple sequence alignments (MSA) 24 and the fact that, in general, only the relatively small number of top-ranking predictions 25 are reliable. To date, methods for analysing correlated mutations have relied exclusively 26 on amino acid MSAs as inputs. Here, we describe a new approach for analysing 27 correlated mutations that is based on combined analysis of amino acid and codon MSAs. 28 We show that a direct contact is more likely to be present when the correlation between 29 the positions is strong at the amino acid level but weak at the codon level. The 30 performance of different methods for analysing correlated mutations in predicting 31 contacts is shown to be enhanced significantly when amino acid and codon data are 32 combined. 33 2 34 The effects of mutations that disrupt protein structure and/or function at one site are often 35 suppressed by mutations that occur at other sites either in the same protein or in other 36 proteins. -
Dnpro: a Deep Learning Network Approach to Predicting Protein Stability Changes Induced by Single-Site Mutations Xiao Zhou and Jianlin Cheng*
DNpro: A Deep Learning Network Approach to Predicting Protein Stability Changes Induced by Single-Site Mutations Xiao Zhou and Jianlin Cheng* Computer Science Department University of Missouri Columbia, MO 65211, USA *Corresponding author: [email protected] a function from the data to map the input information Abstract—A single amino acid mutation can have a significant regarding a protein and its mutation to the energy change impact on the stability of protein structure. Thus, the prediction of without the need of approximating the physics underlying protein stability change induced by single site mutations is critical mutation stability. This data-driven formulation of the and useful for studying protein function and structure. Here, we problem makes it possible to apply a large array of machine presented a new deep learning network with the dropout technique learning methods to tackle the problem. Therefore, a wide for predicting protein stability changes upon single amino acid range of machine learning methods has been applied to the substitution. While using only protein sequence as input, the overall same problem. E Capriotti et al., 2004 [2] presented a neural prediction accuracy of the method on a standard benchmark is >85%, network for predicting protein thermodynamic stability with which is higher than existing sequence-based methods and is respect to native structure; J Cheng et al., 2006 [1] developed comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results MUpro, a support vector machine with radial basis kernel to demonstrate that deep learning is a promising technique for protein improve mutation stability prediction; iPTREE based on C4.5 stability prediction. -
Assessing the Utility of Residue-Residue Contact Information in a Sequence and Structure Rich Era
Supplementary Information: Assessing the utility of residue-residue contact information in a sequence and structure rich era Hetunandan Kamisetty, Sergey Ovchinnikov, David Baker Learning The basis of methods that predict contact information from Multiple Sequence Alignments is that pro- teins with statistically significant sequence similarity have similar structure and that this common structural constraint affects their compositional variability. Thus, a statistical model of the constraints on the composi- tional variability might recover structural constraints (contacts). Historically, these methods were based on Mutual information (MI) and its variations and tended to suffer from the problem of conflating direct and indirect statistical coupling: if positions (A,B) and (B,C) have a high MI; so do (A,C). Thus, even if only spatially proximal positions co-evolve in composition, non-adjacent positions can display a high MI due to transitivity. More recent methods use a global statistical model as described in Eq. 2. The use of a global statistical model allows disentangling direct interactions versus indirect transitive effects. Here, we briefly review alternate approaches to learning this model. Maximum Likelihood Estimation A common approach to learning statistical models from data is the maximum likelihood procedure [1] which for the case of the model in Eq. 2 having parameters Θ =(v,w) is v^; w^ = arg max ll(v; wjD) − R(v; w) v;w PN n where ll(v; wjD) = n=1 log P (x jv; w) is the log likelihood of the model given a set of protein se- quences D = [x1; x2::xN ] and R(v; w) is an optional penalty term added to prevent over-fitting, especially in high-dimensions. -
11: Catchup II Machine Learning and Real-World Data (MLRD)
11: Catchup II Machine Learning and Real-world Data (MLRD) Ann Copestake Lent 2019 Last session: HMM in a biological application In the last session, we used an HMM as a way of approximating some aspects of protein structure. Today: catchup session 2. Very brief sketch of protein structure determination: including gamification and Monte Carlo methods (and a little about AlphaFold). Related ideas are used in many very different machine learning applications . What happens in catchup sessions? Lecture and demonstrated session scheduled as in normal session. Lecture material is non-examinable. Time for you to catch-up in demonstrated sessions or attempt some starred ticks. Demonstrators help as usual. Protein structure Levels of structure: Primary structure: sequence of amino acid residues. Secondary structure: highly regular substructures, especially α-helix, β-sheet. Tertiary structure: full 3-D structure. In the cell: an amino acid sequence (as encoded by DNA) is produced and folds itself into a protein. Secondary and tertiary structure crucial for protein to operate correctly. Some diseases thought to be caused by problems in protein folding. Alpha helix Dcrjsr - Own work, CC BY 3.0, https://commons.wikimedia.org/w/index.php?curid=9131613 Bovine rhodopsin By Andrei Lomize - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=34114850 found in the rods in the retina of the eye a bundle of seven helices crossing the membrane (membrane surfaces marked by horizontal lines) supports a molecule of retinal, which changes structure when exposed to light, also changing the protein structure, initiating the visual pathway 7-bladed propeller fold (found naturally) http://beautifulproteins.blogspot.co.uk/ Peptide self-assembly mimic scaffold (an engineered protein) http://beautifulproteins.blogspot.co.uk/ Protein folding Anfinsen’s hypothesis: the structure a protein forms in nature is the global minimum of the free energy and is determined by the animo acid sequence. -
Leveraging Novel Information Sources for Protein Structure Prediction
Leveraging Novel Information Sources for Protein Structure Prediction vorgelegt von Dipl.-Biol. Michael Bohlke-Schneider, geboren Schneider geb. in Dortmund von der Fakultät IV — Elektrotechnik und Informatik der Technischen Universität Berlin zur Erlangung des akademischen Grades Doktor der Naturwissenschaften - Dr. rer. nat. - genehmigte Dissertation Promotionsausschuss: Vorsitzender: Prof. Dr. Klaus Obermayer 1. Gutachter: Prof. Dr. Oliver Brock 2. Gutachter: Prof. Dr. Juri Rappsilber 3. Gutachter: Prof. Dr. Jens Meiler Tag der wissenschaftlichen Aussprache: 22.12.2015 Berlin 2016 I would like to dedicate this thesis to my loving wife Nina and my mother Barbara. Acknowledgements Becoming a scientist was clearly the greatest challenge that I had to master in my life. The past five years changed my views and my life in a way that I never thought to be possible. Many people helped me during this journey. I am blessed with having many good friends and I cannot possibly list all the people that I am grateful for. First, I want to thank my adviser, Oliver Brock. Oliver is the sharpest mind I have ever met and his ability to think incredibly clearly has been very inspiring. In addition, Oliver has redefined my standard for scientific rigor and what it means to master all skills that a scientist should have. But his most important lesson was to never be satisfied, to constantly challenge the status quo, and to change the world for the better. This lesson will surely shape my further life, no matter where it will take me. I would also like to thank the members of my committee, Juri Rappsilber and Jens Meiler. -
Ensembling Multiple Raw Coevolutionary Features with Deep Residual Neural Networks for Contact‐Map Prediction in CASP13
Received: 17 April 2019 Revised: 20 July 2019 Accepted: 8 August 2019 DOI: 10.1002/prot.25798 RESEARCH ARTICLE Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13 Yang Li1,2 | Chengxin Zhang2 | Eric W. Bell2 | Dong-Jun Yu1,2 | Yang Zhang2 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Abstract Nanjing, China We report the results of residue-residue contact prediction of a new pipeline built 2 Department of Computational Medicine and purely on the learning of coevolutionary features in the CASP13 experiment. For a Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA query sequence, the pipeline starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two comple- Correspondence Yang Zhang, Department of Computational mentary Hidden Markov Model (HMM)-based searching tools. Three profile matrices, Medicine and Bioinformatics, University of built on covariance, precision, and pseudolikelihood maximization respectively, are then Michigan, Ann Arbor, MI 48109. Email: [email protected] created from the MSAs, which are used as the input features of a deep residual con- volutional neural network architecture for contact-map training and prediction. Two Dong-Jun Yu, School of Computer Science and Engineering, Nanjing University of Science and ensembling strategies have been proposed to integrate the matrix features through Technology, Xiaolingwei 200, Nanjing