51
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Zhao B, Katuwawala A, Oldfield CJ, Dunker AK, Faraggi E, Gsponer J, Kloczkowski A, Malhis N, Mirdita M, Obradovic Z, Söding J, Steinegger M, Zhou Y, Kurgan L. DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Res 2021; 49:D298-D308. [PMID: 33119734 PMCID: PMC7778963 DOI: 10.1093/nar/gkaa931] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/11/2020] [Accepted: 10/05/2020] [Indexed: 12/30/2022] Open
Abstract
We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.
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Affiliation(s)
- Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | | | - A Keith Dunker
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Eshel Faraggi
- Battelle Center for Mathematical Medicine at the Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Andrzej Kloczkowski
- Battelle Center for Mathematical Medicine at the Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Nawar Malhis
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Milot Mirdita
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Zoran Obradovic
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Johannes Söding
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Martin Steinegger
- School of Biological Sciences and Institute of Molecular Biology & Genetics, Seoul National University, Seoul, Republic of Korea
| | - Yaoqi Zhou
- Institute for Glycomics, Griffith University, Gold Coast, Queensland, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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52
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DuPai CD, Davies BW, Wilke CO. A systematic analysis of the beta hairpin motif in the Protein Data Bank. Protein Sci 2021; 30:613-623. [PMID: 33389765 DOI: 10.1002/pro.4020] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/29/2020] [Accepted: 12/29/2020] [Indexed: 12/31/2022]
Abstract
The beta hairpin motif is a ubiquitous protein structural motif that can be found in molecules across the tree of life. This motif, which is also popular in synthetically designed proteins and peptides, is known for its stability and adaptability to broad functions. Here, we systematically probe all 49,000 unique beta hairpin substructures contained within the Protein Data Bank (PDB) to uncover key characteristics correlated with stable beta hairpin structure, including amino acid biases and enriched interstrand contacts. We find that position specific amino acid preferences, while seen throughout the beta hairpin structure, are most evident within the turn region, where they depend on subtle turn dynamics associated with turn length and secondary structure. We also establish a set of broad design principles, such as the inclusion of aspartic acid residues at a specific position and the careful consideration of desired secondary structure when selecting residues for the turn region, that can be applied to the generation of libraries encoding proteins or peptides containing beta hairpin structures.
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Affiliation(s)
- Cory D DuPai
- Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas, USA.,Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA
| | - Bryan W Davies
- Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas, USA.,Center for Systems and Synthetic Biology, John Ring LaMontagne Center for Infectious Diseases, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas, USA
| | - Claus O Wilke
- Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA
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53
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Adhikari B, Shrestha B, Bernardini M, Hou J, Lea J. DISTEVAL: a web server for evaluating predicted protein distances. BMC Bioinformatics 2021; 22:8. [PMID: 33407077 PMCID: PMC7788990 DOI: 10.1186/s12859-020-03938-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 12/15/2020] [Indexed: 06/30/2024] Open
Abstract
Background Protein inter-residue contact and distance prediction are two key intermediate steps essential to accurate protein structure prediction. Distance prediction comes in two forms: real-valued distances and ‘binned’ distograms, which are a more finely grained variant of the binary contact prediction problem. The latter has been introduced as a new challenge in the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14) 2020 experiment. Despite the recent proliferation of methods for predicting distances, few methods exist for evaluating these predictions. Currently only numerical metrics, which evaluate the entire prediction at once, are used. These give no insight into the structural details of a prediction. For this reason, new methods and tools are needed. Results We have developed a web server for evaluating predicted inter-residue distances. Our server, DISTEVAL, accepts predicted contacts, distances, and a true structure as optional inputs to generate informative heatmaps, chord diagrams, and 3D models. All of these outputs facilitate visual and qualitative assessment. The server also evaluates predictions using other metrics such as mean absolute error, root mean squared error, and contact precision. Conclusions The visualizations generated by DISTEVAL complement each other and collectively serve as a powerful tool for both quantitative and qualitative assessments of predicted contacts and distances, even in the absence of a true 3D structure.
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Affiliation(s)
- Badri Adhikari
- Department of Computer Science, University of Missouri-St. Louis, 312 Express Scripts Hall, St. Louis, MO, USA.
| | - Bikash Shrestha
- Department of Computer Science, University of Missouri-St. Louis, 312 Express Scripts Hall, St. Louis, MO, USA
| | - Matthew Bernardini
- Department of Computer Science, University of Missouri-St. Louis, 312 Express Scripts Hall, St. Louis, MO, USA
| | - Jie Hou
- Department of Computer Science, Saint Louis University, 217 Ritter Hall, St. Louis, MO, USA
| | - Jamie Lea
- Department of Computer Science, University of Missouri-St. Louis, 312 Express Scripts Hall, St. Louis, MO, USA
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54
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Braberg H, Echeverria I, Bohn S, Cimermancic P, Shiver A, Alexander R, Xu J, Shales M, Dronamraju R, Jiang S, Dwivedi G, Bogdanoff D, Chaung KK, Hüttenhain R, Wang S, Mavor D, Pellarin R, Schneidman D, Bader JS, Fraser JS, Morris J, Haber JE, Strahl BD, Gross CA, Dai J, Boeke JD, Sali A, Krogan NJ. Genetic interaction mapping informs integrative structure determination of protein complexes. Science 2020; 370:eaaz4910. [PMID: 33303586 PMCID: PMC7946025 DOI: 10.1126/science.aaz4910] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 07/23/2020] [Accepted: 10/23/2020] [Indexed: 12/17/2022]
Abstract
Determining structures of protein complexes is crucial for understanding cellular functions. Here, we describe an integrative structure determination approach that relies on in vivo measurements of genetic interactions. We construct phenotypic profiles for point mutations crossed against gene deletions or exposed to environmental perturbations, followed by converting similarities between two profiles into an upper bound on the distance between the mutated residues. We determine the structure of the yeast histone H3-H4 complex based on ~500,000 genetic interactions of 350 mutants. We then apply the method to subunits Rpb1-Rpb2 of yeast RNA polymerase II and subunits RpoB-RpoC of bacterial RNA polymerase. The accuracy is comparable to that based on chemical cross-links; using restraints from both genetic interactions and cross-links further improves model accuracy and precision. The approach provides an efficient means to augment integrative structure determination with in vivo observations.
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Affiliation(s)
- Hannes Braberg
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Stefan Bohn
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Peter Cimermancic
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Anthony Shiver
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Richard Alexander
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jiewei Xu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Michael Shales
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Raghuvar Dronamraju
- Department of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Shuangying Jiang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Gajendradhar Dwivedi
- Department of Biology and Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454, USA
| | - Derek Bogdanoff
- Center for Advanced Technology, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kaitlin K Chaung
- Center for Advanced Technology, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ruth Hüttenhain
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Shuyi Wang
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David Mavor
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Riccardo Pellarin
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dina Schneidman
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joel S Bader
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - James S Fraser
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John Morris
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - James E Haber
- Department of Biology and Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454, USA
| | - Brian D Strahl
- Department of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Carol A Gross
- Department of Microbiology and Immunology and Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Junbiao Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jef D Boeke
- NYU Langone Health, New York, NY 10016, USA.
- High Throughput Biology Center and Department of Molecular Biology & Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY 11201, USA
| | - Andrej Sali
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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55
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Li B, Yang YT, Capra JA, Gerstein MB. Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks. PLoS Comput Biol 2020; 16:e1008291. [PMID: 33253214 PMCID: PMC7728386 DOI: 10.1371/journal.pcbi.1008291] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 12/10/2020] [Accepted: 08/26/2020] [Indexed: 12/22/2022] Open
Abstract
Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in CNNs, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D-CNNs can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.
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Affiliation(s)
- Bian Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Biological Sciences and Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Yucheng T. Yang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - John A. Capra
- Department of Biological Sciences and Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Mark B. Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Computer Science, Yale University, New Haven, Connecticut, United States of America
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56
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Chen X, Song S, Ji J, Tang Z, Todo Y. Incorporating a multiobjective knowledge-based energy function into differential evolution for protein structure prediction. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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57
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Skolnick J, Gao M. The role of local versus nonlocal physicochemical restraints in determining protein native structure. Curr Opin Struct Biol 2020; 68:1-8. [PMID: 33129066 DOI: 10.1016/j.sbi.2020.10.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/03/2020] [Accepted: 10/05/2020] [Indexed: 12/15/2022]
Abstract
The tertiary structure of a native protein is dictated by the interplay of local secondary structure propensities, hydrogen bonding, and tertiary interactions. It is argued that the space of known protein topologies covers all single domain folds and results from the compactness of the native structure and excluded volume. Protein compactness combined with the chirality of the protein's side chains also yields native-like Ramachandran plots. It is the many-body, tertiary interactions among residues that collectively select for the global structure that a particular protein sequence adopts. This explains why the recent advances in deep-learning approaches that predict protein side-chain contacts, the distance matrix between residues, and sequence alignments are successful. They succeed because they implicitly learned the many-body interactions among protein residues.
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Affiliation(s)
- Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, GA 30332, United States.
| | - Mu Gao
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, NW, Atlanta, GA 30332, United States.
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58
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Liu J, Zhou XG, Zhang Y, Zhang GJ. CGLFold: a contact-assisted de novo protein structure prediction using global exploration and loop perturbation sampling algorithm. Bioinformatics 2020; 36:2443-2450. [PMID: 31860059 DOI: 10.1093/bioinformatics/btz943] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 12/10/2019] [Accepted: 12/18/2019] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION Regions that connect secondary structure elements in a protein are known as loops, whose slight change will produce dramatic effect on the entire topology. This study investigates whether the accuracy of protein structure prediction can be improved using a loop-specific sampling strategy. RESULTS A novel de novo protein structure prediction method that combines global exploration and loop perturbation is proposed in this study. In the global exploration phase, the fragment recombination and assembly are used to explore the massive conformational space and generate native-like topology. In the loop perturbation phase, a loop-specific local perturbation model is designed to improve the accuracy of the conformation and is solved by differential evolution algorithm. These two phases enable a cooperation between global exploration and local exploitation. The filtered contact information is used to construct the conformation selection model for guiding the sampling. The proposed CGLFold is tested on 145 benchmark proteins, 14 free modeling (FM) targets of CASP13 and 29 FM targets of CASP12. The experimental results show that the loop-specific local perturbation can increase the structure diversity and success rate of conformational update and gradually improve conformation accuracy. CGLFold obtains template modeling score ≥ 0.5 models on 95 standard test proteins, 7 FM targets of CASP13 and 9 FM targets of CASP12. AVAILABILITY AND IMPLEMENTATION The source code and executable versions are freely available at https://github.com/iobio-zjut/CGLFold. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jun Liu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xiao-Gen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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59
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Ding W, Gong H. Predicting the Real-Valued Inter-Residue Distances for Proteins. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2020; 7:2001314. [PMID: 33042750 PMCID: PMC7539185 DOI: 10.1002/advs.202001314] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/06/2020] [Indexed: 05/04/2023]
Abstract
Predicting protein structure from the amino acid sequence has been a challenge with theoretical and practical significance in biophysics. Despite the recent progresses elicited by improved inter-residue contact prediction, contact-based structure prediction has gradually reached the performance ceiling. New methods have been proposed to predict the inter-residue distance, but unanimously by simplifying the real-valued distance prediction into a multiclass classification problem. Here, a lightweight regression-based distance prediction method is shown, which adopts the generative adversarial network to capture the delicate geometric relationship between residue pairs and thus could predict the continuous, real-valued inter-residue distance rapidly and satisfactorily. The predicted residue distance map allows quick structure modeling by the CNS suite, and the constructed models approach the same level of quality as the other state-of-the-art protein structure prediction methods when tested on CASP13 targets. Moreover, this method can be used directly for the structure prediction of membrane proteins without transfer learning.
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Affiliation(s)
- Wenze Ding
- MOE Key Laboratory of BioinformaticsSchool of Life SciencesTsinghua UniversityBeijing100084China
- Beijing Advanced Innovation Center for Structural BiologyTsinghua UniversityBeijing100084China
| | - Haipeng Gong
- MOE Key Laboratory of BioinformaticsSchool of Life SciencesTsinghua UniversityBeijing100084China
- Beijing Advanced Innovation Center for Structural BiologyTsinghua UniversityBeijing100084China
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60
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Du Z, Pan S, Wu Q, Peng Z, Yang J. CATHER: a novel threading algorithm with predicted contacts. Bioinformatics 2020; 36:2119-2125. [PMID: 31790141 DOI: 10.1093/bioinformatics/btz876] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 10/31/2019] [Accepted: 11/28/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Threading is one of the most effective methods for protein structure prediction. In recent years, the increasing accuracy in protein contact map prediction opens a new avenue to improve the performance of threading algorithms. Several preliminary studies suggest that with predicted contacts, the performance of threading algorithms can be improved greatly. There is still much room to explore to make better use of predicted contacts. RESULTS We have developed a new contact-assisted threading algorithm named CATHER using both conventional sequential profiles and contact map predicted by a deep learning-based algorithm. Benchmark tests on an independent test set and the CASP12 targets demonstrated that CATHER made significant improvement over other methods which only use either sequential profile or predicted contact map. Our method was ranked at the Top 10 among all 39 participated server groups on the 32 free modeling targets in the blind tests of the CASP13 experiment. These data suggest that it is promising to push forward the threading algorithms by using predicted contacts. AVAILABILITY AND IMPLEMENTATION http://yanglab.nankai.edu.cn/CATHER/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zongyang Du
- School of Mathematical Sciences, Nankai University, Tianjin 300071, China
| | - Shuo Pan
- School of Mathematical Sciences, Nankai University, Tianjin 300071, China
| | - Qi Wu
- School of Mathematical Sciences, Nankai University, Tianjin 300071, China
| | - Zhenling Peng
- Center for Applied Mathematics, Tianjin University, Tianjin 300072, China
| | - Jianyi Yang
- School of Mathematical Sciences, Nankai University, Tianjin 300071, China
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61
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Zhang C, Zheng W, Mortuza SM, Li Y, Zhang Y. DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins. Bioinformatics 2020; 36:2105-2112. [PMID: 31738385 DOI: 10.1093/bioinformatics/btz863] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 10/17/2019] [Accepted: 11/15/2019] [Indexed: 12/23/2022] Open
Abstract
MOTIVATION The success of genome sequencing techniques has resulted in rapid explosion of protein sequences. Collections of multiple homologous sequences can provide critical information to the modeling of structure and function of unknown proteins. There are however no standard and efficient pipeline available for sensitive multiple sequence alignment (MSA) collection. This is particularly challenging when large whole-genome and metagenome databases are involved. RESULTS We developed DeepMSA, a new open-source method for sensitive MSA construction, which has homologous sequences and alignments created from multi-sources of whole-genome and metagenome databases through complementary hidden Markov model algorithms. The practical usefulness of the pipeline was examined in three large-scale benchmark experiments based on 614 non-redundant proteins. First, DeepMSA was utilized to generate MSAs for residue-level contact prediction by six coevolution and deep learning-based programs, which resulted in an accuracy increase in long-range contacts by up to 24.4% compared to the default programs. Next, multiple threading programs are performed for homologous structure identification, where the average TM-score of the template alignments has over 7.5% increases with the use of the new DeepMSA profiles. Finally, DeepMSA was used for secondary structure prediction and resulted in statistically significant improvements in the Q3 accuracy. It is noted that all these improvements were achieved without re-training the parameters and neural-network models, demonstrating the robustness and general usefulness of the DeepMSA in protein structural bioinformatics applications, especially for targets without homologous templates in the PDB library. AVAILABILITY AND IMPLEMENTATION https://zhanglab.ccmb.med.umich.edu/DeepMSA/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - S M Mortuza
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.,School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
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62
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Adhikari B. A fully open-source framework for deep learning protein real-valued distances. Sci Rep 2020; 10:13374. [PMID: 32770096 PMCID: PMC7414848 DOI: 10.1038/s41598-020-70181-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 07/23/2020] [Indexed: 11/12/2022] Open
Abstract
As deep learning algorithms drive the progress in protein structure prediction, a lot remains to be studied at this merging superhighway of deep learning and protein structure prediction. Recent findings show that inter-residue distance prediction, a more granular version of the well-known contact prediction problem, is a key to predicting accurate models. However, deep learning methods that predict these distances are still in the early stages of their development. To advance these methods and develop other novel methods, a need exists for a small and representative dataset packaged for faster development and testing. In this work, we introduce protein distance net (PDNET), a framework that consists of one such representative dataset along with the scripts for training and testing deep learning methods. The framework also includes all the scripts that were used to curate the dataset, and generate the input features and distance maps. Deep learning models can also be trained and tested in a web browser using free platforms such as Google Colab. We discuss how PDNET can be used to predict contacts, distance intervals, and real-valued distances.
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Affiliation(s)
- Badri Adhikari
- Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO, 63132, USA.
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63
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Abriata LA, Dal Peraro M. State-of-the-art web services for de novo protein structure prediction. Brief Bioinform 2020; 22:5870389. [PMID: 34020540 DOI: 10.1093/bib/bbaa139] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 06/04/2020] [Accepted: 06/05/2020] [Indexed: 02/06/2023] Open
Abstract
Residue coevolution estimations coupled to machine learning methods are revolutionizing the ability of protein structure prediction approaches to model proteins that lack clear homologous templates in the Protein Data Bank (PDB). This has been patent in the last round of the Critical Assessment of Structure Prediction (CASP), which presented several very good models for the hardest targets. Unfortunately, literature reporting on these advances often lacks digests tailored to lay end users; moreover, some of the top-ranking predictors do not provide webservers that can be used by nonexperts. How can then end users benefit from these advances and correctly interpret the predicted models? Here we review the web resources that biologists can use today to take advantage of these state-of-the-art methods in their research, including not only the best de novo modeling servers but also datasets of models precomputed by experts for structurally uncharacterized protein families. We highlight their features, advantages and pitfalls for predicting structures of proteins without clear templates. We present a broad number of applications that span from driving forward biochemical investigations that lack experimental structures to actually assisting experimental structure determination in X-ray diffraction, cryo-EM and other forms of integrative modeling. We also discuss issues that must be considered by users yet still require further developments, such as global and residue-wise model quality estimates and sources of residue coevolution other than monomeric tertiary structure.
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Affiliation(s)
- Luciano A Abriata
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Matteo Dal Peraro
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
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64
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Runthala A, Chowdhury S. Refined template selection and combination algorithm significantly improves template-based modeling accuracy. J Bioinform Comput Biol 2020; 17:1950006. [PMID: 31057073 DOI: 10.1142/s0219720019500069] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
In contrast to ab-initio protein modeling methodologies, comparative modeling is considered as the most popular and reliable algorithm to model protein structure. However, the selection of the best set of templates is still a major challenge. An effective template-ranking algorithm is developed to efficiently select only the reliable hits for predicting the protein structures. The algorithm employs the pairwise as well as multiple sequence alignments of template hits to rank and select the best possible set of templates. It captures several key sequences and structural information of template hits and converts into scores to effectively rank them. This selected set of templates is used to model a target. Modeling accuracy of the algorithm is tested and evaluated on TBM-HA domain containing CASP8, CASP9 and CASP10 targets. On an average, this template ranking and selection algorithm improves GDT-TS, GDT-HA and TM_Score by 3.531, 4.814 and 0.022, respectively. Further, it has been shown that the inclusion of structurally similar templates with ample conformational diversity is crucial for the modeling algorithm to maximally as well as reliably span the target sequence and construct its near-native model. The optimal model sampling also holds the key to predict the best possible target structure.
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Affiliation(s)
- Ashish Runthala
- 1 Department of Biological Sciences, Birla Institute of Technology and Science, Pilani-333031, India
| | - Shibasish Chowdhury
- 1 Department of Biological Sciences, Birla Institute of Technology and Science, Pilani-333031, India
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65
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Li Y, Hu J, Zhang C, Yu DJ, Zhang Y. ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks. Bioinformatics 2020; 35:4647-4655. [PMID: 31070716 DOI: 10.1093/bioinformatics/btz291] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 03/18/2019] [Accepted: 04/17/2019] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank. RESULTS We developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% higher than the best of other state-of-the-art approaches ranging from coevolution coupling analysis to deep neural network training. Detailed data analyses show that the major advantage of ResPRE lies at the utilization of precision matrix that helps rule out transitional noises of contact-maps compared with the previously used covariance matrix. Meanwhile, the residual network with parallel shortcut layer connections increases the learning ability of deep neural network training. It was also found that appropriate collection of MSAs can further improve the accuracy of final contact-map predictions. The standalone package and online server of ResPRE are made freely available, which should bring important impact on protein structure and function modeling studies in particular for the distant- and non-homology protein targets. AVAILABILITY AND IMPLEMENTATION https://zhanglab.ccmb.med.umich.edu/ResPRE and https://github.com/leeyang/ResPRE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yang Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Jun Hu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
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66
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Correa Marrero M, Immink RGH, de Ridder D, van Dijk ADJ. Improved inference of intermolecular contacts through protein-protein interaction prediction using coevolutionary analysis. Bioinformatics 2020; 35:2036-2042. [PMID: 30398547 DOI: 10.1093/bioinformatics/bty924] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 10/11/2018] [Accepted: 11/05/2018] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Predicting residue-residue contacts between interacting proteins is an important problem in bioinformatics. The growing wealth of sequence data can be used to infer these contacts through correlated mutation analysis on multiple sequence alignments of interacting homologs of the proteins of interest. This requires correct identification of pairs of interacting proteins for many species, in order to avoid introducing noise (i.e. non-interacting sequences) in the analysis that will decrease predictive performance. RESULTS We have designed Ouroboros, a novel algorithm to reduce such noise in intermolecular contact prediction. Our method iterates between weighting proteins according to how likely they are to interact based on the correlated mutations signal, and predicting correlated mutations based on the weighted sequence alignment. We show that this approach accurately discriminates between protein interaction versus non-interaction and simultaneously improves the prediction of intermolecular contact residues compared to a naive application of correlated mutation analysis. This requires no training labels concerning interactions or contacts. Furthermore, the method relaxes the assumption of one-to-one interaction of previous approaches, allowing for the study of many-to-many interactions. AVAILABILITY AND IMPLEMENTATION Source code and test data are available at www.bif.wur.nl/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Richard G H Immink
- Laboratory of Molecular Biology, Department of Plant Sciences.,Bioscience, Wageningen Plant Research
| | | | - Aalt D J van Dijk
- Bioinformatics Group, Department of Plant Sciences.,Bioscience, Wageningen Plant Research.,Biometris, Department of Plant Sciences, Wageningen University & Research, Wageningen PB, The Netherlands
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67
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Buchan DWA, Jones DT. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res 2020; 47:W402-W407. [PMID: 31251384 PMCID: PMC6602445 DOI: 10.1093/nar/gkz297] [Citation(s) in RCA: 802] [Impact Index Per Article: 200.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/02/2019] [Accepted: 04/15/2019] [Indexed: 02/07/2023] Open
Abstract
The PSIPRED Workbench is a web server offering a range of predictive methods to the bioscience community for 20 years. Here, we present the work we have completed to update the PSIPRED Protein Analysis Workbench and make it ready for the next 20 years. The main focus of our recent website upgrade work has been the acceleration of analyses in the face of increasing protein sequence database size. We additionally discuss any new software, the new hardware infrastructure, our webservices and web site. Lastly we survey updates to some of the key predictive algorithms available through our website.
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Affiliation(s)
- Daniel W A Buchan
- UCL Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
| | - David T Jones
- UCL Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
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68
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Zaman AB, Kamranfar P, Domeniconi C, Shehu A. Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering. Molecules 2020; 25:E2228. [PMID: 32397410 PMCID: PMC7248879 DOI: 10.3390/molecules25092228] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 04/21/2020] [Accepted: 04/28/2020] [Indexed: 11/16/2022] Open
Abstract
Controlling the quality of tertiary structures computed for a protein molecule remains a central challenge in de-novo protein structure prediction. The rule of thumb is to generate as many structures as can be afforded, effectively acknowledging that having more structures increases the likelihood that some will reside near the sought biologically-active structure. A major drawback with this approach is that computing a large number of structures imposes time and space costs. In this paper, we propose a novel clustering-based approach which we demonstrate to significantly reduce an ensemble of generated structures without sacrificing quality. Evaluations are related on both benchmark and CASP target proteins. Structure ensembles subjected to the proposed approach and the source code of the proposed approach are publicly-available at the links provided in Section 1.
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Affiliation(s)
- Ahmed Bin Zaman
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA; (A.B.Z.); (P.K.)
| | - Parastoo Kamranfar
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA; (A.B.Z.); (P.K.)
| | - Carlotta Domeniconi
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA; (A.B.Z.); (P.K.)
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA; (A.B.Z.); (P.K.)
- Center for Advancing Human-Machine Partnerships, George Mason University, Fairfax, VA 22030, USA
- Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
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69
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Park Y, Casey D, Joshi I, Zhu J, Cheng F. Emergence of New Disease: How Can Artificial Intelligence Help? Trends Mol Med 2020; 26:627-629. [PMID: 32418724 PMCID: PMC7196393 DOI: 10.1016/j.molmed.2020.04.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 04/14/2020] [Accepted: 04/26/2020] [Indexed: 12/16/2022]
Abstract
Emergence of new disease remains a critical parameter in human health and society. Advances in artificial intelligence (AI) allow for rapid processing and analysis of massive and complex data. In this forum article, the recent applications across disease prediction and drug development in relation to the COVID-19 pandemic are reviewed.
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Affiliation(s)
- Yurim Park
- Radcliffe Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, UK.
| | - Daniel Casey
- Innovation Faculty, Celltrion Healthcare, Slough, UK
| | - Indra Joshi
- Digital Health and Artificial Intelligence Laboratory, National Health Service, London, UK
| | - Jiming Zhu
- Vanke School of Public Health and School of Medicine, Tsinghua University, Beijing, China; Institute for Hospital Management, Tsinghua Shenzhen International Graduate School, Shenzhen, China
| | - Feng Cheng
- Vanke School of Public Health and School of Medicine, Tsinghua University, Beijing, China
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70
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Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning. J Comput Biol 2020; 27:796-814. [DOI: 10.1089/cmb.2019.0193] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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71
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Chen MC, Li Y, Zhu YH, Ge F, Yu DJ. SSCpred: Single-Sequence-Based Protein Contact Prediction Using Deep Fully Convolutional Network. J Chem Inf Model 2020; 60:3295-3303. [DOI: 10.1021/acs.jcim.9b01207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ming-Cai Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, P. R. China
| | - Yang Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, P. R. China
- Department of Computational Medicine and Bioinformatics, University of Michigan, Washtenaw 100, Ann Arbor, Michigan 48109-2218, United States
| | - Yi-Heng Zhu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, P. R. China
| | - Fang Ge
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, P. R. China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, P. R. China
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72
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Protein conformations à la carte, a step further in de novo protein design. Proc Natl Acad Sci U S A 2020; 117:8674-8676. [PMID: 32291334 DOI: 10.1073/pnas.2004188117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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73
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Srivastava A, Tiwari SP, Miyashita O, Tama F. Integrative/Hybrid Modeling Approaches for Studying Biomolecules. J Mol Biol 2020; 432:2846-2860. [DOI: 10.1016/j.jmb.2020.01.039] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/20/2020] [Accepted: 01/24/2020] [Indexed: 12/12/2022]
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74
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Abriata LA, Dal Peraro M. Will Cryo-Electron Microscopy Shift the Current Paradigm in Protein Structure Prediction? J Chem Inf Model 2020; 60:2443-2447. [PMID: 32134661 DOI: 10.1021/acs.jcim.0c00177] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Protein dynamics is undoubtedly a pervasive ingredient in all biological functions. However, structural biology has been strongly driven by a static-centered view of protein architecture. We argue that the recent advances of cryo-electron microscopy (EM) have the potential to more broadly explore the conformational landscapes of protein complexes and therefore will enhance our ability to predict the diverse conformations of tertiary and quaternary protein structures that are functionally relevant in physiological conditions.
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Affiliation(s)
- Luciano A Abriata
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne, Switzerland
| | - Matteo Dal Peraro
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne, Switzerland
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75
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Tsuchiya Y, Tomii K. Neural networks for protein structure and function prediction and dynamic analysis. Biophys Rev 2020; 12:569-573. [PMID: 32166610 DOI: 10.1007/s12551-020-00685-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 03/02/2020] [Indexed: 12/18/2022] Open
Abstract
Hardware and software advancements along with the accumulation of large amounts of data in recent years have together spurred a remarkable growth in the application of neural networks to various scientific fields. Machine learning based on neural networks with multiple (hidden) layers is becoming an extremely powerful approach for analyzing data. With the accumulation of large amounts of protein data such as structural and functional assay data, the effects of such approaches within the field of protein informatics are increasing. Here, we introduce our recent studies based on applications of neural networks for protein structure and function prediction and dynamic analysis involving: (i) inter-residue contact prediction based on a multiple sequence alignment (MSA) of amino acid sequences, (ii) prediction of protein-compound interaction using assay data, and (iii) detection of protein allostery from trajectories of molecular dynamic (MD) simulation.
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Affiliation(s)
- Yuko Tsuchiya
- Artificial Intelligence Research Center (AIRC), Tokyo, Japan
- Biotechnology Research Institute for Drug Discovery, Tokyo, Japan
| | - Kentaro Tomii
- Artificial Intelligence Research Center (AIRC), Tokyo, Japan.
- Biotechnology Research Institute for Drug Discovery, Tokyo, Japan.
- Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.
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76
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Halim SA, Aziz S, Ilyas M, Wadood A, Khan A, Al-Harrasi A. In Silico Modeling of Crimean Congo Hemorrhagic Fever Virus Glycoprotein-N and Screening of Anti Viral Hits by Virtual Screening. Int J Pept Res Ther 2020; 26:2675-2688. [PMID: 32421093 PMCID: PMC7223756 DOI: 10.1007/s10989-020-10055-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 02/26/2020] [Accepted: 03/02/2020] [Indexed: 12/27/2022]
Abstract
Crimean-Congo hemorrhagic fever (CCHF) is a widespread zoonotic viral disease, caused by a tick-born virus Crimean-Congo hemorrhagic fever virus (CCHFV). This disease is endemic in Middle East, Asia, Africa and South-Eastern Europe with the mortality rate of 5–30%. CCHFV genome is composed of three segments: large, medium and small segments. M segment encodes a polyprotein (glycoprotein) so called glycoprotein N (Gn) which is considered as a potential druggable target for the effective therapy of CCHF. The complete structure of Gn is still not characterized. The aim of the current study is to predict the complete three-dimensional (3D-) structure of CCHFV Gn protein via threading-based modeling and investigate the residues crucial for binding with CCHFV envelop. The developed model displayed excellent stereo-chemical and geometrical properties. Subsequently structure based virtual screening (SBVS) was applied to discover novel inhibitors of Gn protein. A library of > 1300 anti-virals was selected from PubChem database and directed to the predicted binding site of Gn. The SBVS results led to the identification of thirty-seven compounds that inhibit the protein in computational analysis. Those 37 hits were subject to pharmacokinetic profiling which demonstrated that 30/37 compound possess safer pharmacokinetic properties. Thus, by specifically targeting Gn, less toxic and more potent inhibitors of CCHFV were identified in silico.
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Affiliation(s)
- Sobia Ahsan Halim
- 1Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al Mauz, Nizwa, Sultanate of Oman
| | - Sobia Aziz
- 2Department of Biochemistry, Kinnaird College for Women, Lahore, Pakistan
| | - Mohammad Ilyas
- 3Center for Omic Sciences, Islamia College Peshawar, Peshawar, Khyber Pakhtunkhwa Pakistan
| | - Abdul Wadood
- 4Department of Biochemistry, Abdul Wali Khan University Mardan, Shankar Campus, Mardan, Pakistan
| | - Ajmal Khan
- 1Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al Mauz, Nizwa, Sultanate of Oman
| | - Ahmed Al-Harrasi
- 1Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al Mauz, Nizwa, Sultanate of Oman
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77
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Epistatic contributions promote the unification of incompatible models of neutral molecular evolution. Proc Natl Acad Sci U S A 2020; 117:5873-5882. [PMID: 32123092 PMCID: PMC7084075 DOI: 10.1073/pnas.1913071117] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Mathematical models of evolution help us understand mechanisms driving protein-sequence change. Previous models recapitulate a disjoint subset of statistical features of natural sequences. We present a neutral evolution model that unifies features including extreme variance of the molecular clock’s tick rate and the observation of an evolutionary Stokes shift, an irreversible effect of mutations in the fitness landscape during sequence evolution. We show that interactions between amino acid sites, which inform our fitness metric, are required to observe these features. These interactions are inferred by using direct coupling analysis, which has been successfully utilized to predict protein structures, dynamics, and complexes from coevolutionary information. We anticipate our model will have applications in phylogenetics, ancestral reconstruction of sequences, and protein design. We introduce a model of amino acid sequence evolution that accounts for the statistical behavior of real sequences induced by epistatic interactions. We base the model dynamics on parameters derived from multiple sequence alignments analyzed by using direct coupling analysis methodology. Known statistical properties such as overdispersion, heterotachy, and gamma-distributed rate-across-sites are shown to be emergent properties of this model while being consistent with neutral evolution theory, thereby unifying observations from previously disjointed evolutionary models of sequences. The relationship between site restriction and heterotachy is characterized by tracking the effective alphabet dynamics of sites. We also observe an evolutionary Stokes shift in the fitness of sequences that have undergone evolution under our simulation. By analyzing the structural information of some proteins, we corroborate that the strongest Stokes shifts derive from sites that physically interact in networks near biochemically important regions. Perspectives on the implementation of our model in the context of the molecular clock are discussed.
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78
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Bhattacharya S, Bhattacharya D. Evaluating the significance of contact maps in low-homology protein modeling using contact-assisted threading. Sci Rep 2020; 10:2908. [PMID: 32076047 PMCID: PMC7031282 DOI: 10.1038/s41598-020-59834-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 02/04/2020] [Indexed: 12/02/2022] Open
Abstract
The development of improved threading algorithms for remote homology modeling is a critical step forward in template-based protein structure prediction. We have recently demonstrated the utility of contact information to boost protein threading by developing a new contact-assisted threading method. However, the nature and extent to which the quality of a predicted contact map impacts the performance of contact-assisted threading remains elusive. Here, we systematically analyze and explore this interdependence by employing our newly-developed contact-assisted threading method over a large-scale benchmark dataset using predicted contact maps from four complementary methods including direct coupling analysis (mfDCA), sparse inverse covariance estimation (PSICOV), classical neural network-based meta approach (MetaPSICOV), and state-of-the-art ultra-deep learning model (RaptorX). Experimental results demonstrate that contact-assisted threading using high-quality contacts having the Matthews Correlation Coefficient (MCC) ≥ 0.5 improves threading performance in nearly 30% cases, while low-quality contacts with MCC <0.35 degrades the performance for 50% cases. This holds true even in CASP13 dataset, where threading using high-quality contacts (MCC ≥ 0.5) significantly improves the performance of 22 instances out of 29. Collectively, our study uncovers the mutual association between the quality of predicted contacts and its possible utility in boosting threading performance for improving low-homology protein modeling.
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Affiliation(s)
- Sutanu Bhattacharya
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, 36849, USA
| | - Debswapna Bhattacharya
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, 36849, USA.
- Department of Biological Sciences, Auburn University, Auburn, AL, 36849, USA.
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79
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Malinverni D, Barducci A. Coevolutionary Analysis of Protein Subfamilies by Sequence Reweighting. ENTROPY (BASEL, SWITZERLAND) 2020; 21:1127. [PMID: 32002010 PMCID: PMC6992422 DOI: 10.3390/e21111127] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 11/14/2019] [Indexed: 01/07/2023]
Abstract
Extracting structural information from sequence co-variation has become a common computational biology practice in the recent years, mainly due to the availability of large sequence alignments of protein families. However, identifying features that are specific to sub-classes and not shared by all members of the family using sequence-based approaches has remained an elusive problem. We here present a coevolutionary-based method to differentially analyze subfamily specific structural features by a continuous sequence reweighting (SR) approach. We introduce the underlying principles and test its predictive capabilities on the Response Regulator family, whose subfamilies have been previously shown to display distinct, specific homo-dimerization patterns. Our results show that this reweighting scheme is effective in assigning structural features known a priori to subfamilies, even when sequence data is relatively scarce. Furthermore, sequence reweighting allows assessing if individual structural contacts pertain to specific subfamilies and it thus paves the way for the identification specificity-determining contacts from sequence variation data.
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Affiliation(s)
- Duccio Malinverni
- Medical Research Council (MRC) Laboratory of Molecular Biology, Cambridge CB20QH, UK
| | - Alessandro Barducci
- Centre de Biochimie Structurale (CBS), INSERM, CNRS, Université de Montpellier, 34090 Montpellier, France
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80
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Torrisi M, Pollastri G, Le Q. Deep learning methods in protein structure prediction. Comput Struct Biotechnol J 2020; 18:1301-1310. [PMID: 32612753 PMCID: PMC7305407 DOI: 10.1016/j.csbj.2019.12.011] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/19/2019] [Accepted: 12/20/2019] [Indexed: 01/01/2023] Open
Abstract
Protein Structure Prediction is a central topic in Structural Bioinformatics. Since the '60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural information at various levels of detail. In this review, we briefly introduce the problem of protein structure prediction and essential elements of Deep Learning (such as Convolutional Neural Networks, Recurrent Neural Networks and basic feed-forward Neural Networks they are founded on), after which we discuss the evolution of predictive methods for one-dimensional and two-dimensional Protein Structure Annotations, from the simple statistical methods of the early days, to the computationally intensive highly-sophisticated Deep Learning algorithms of the last decade. In the process, we review the growth of the databases these algorithms are based on, and how this has impacted our ability to leverage knowledge about evolution and co-evolution to achieve improved predictions. We conclude this review outlining the current role of Deep Learning techniques within the wider pipelines to predict protein structures and trying to anticipate what challenges and opportunities may arise next.
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Affiliation(s)
- Mirko Torrisi
- School of Computer Science, University College Dublin, Ireland
| | | | - Quan Le
- Centre for Applied Data Analytics Research, University College Dublin, Ireland
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81
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Mulnaes D, Porta N, Clemens R, Apanasenko I, Reiners J, Gremer L, Neudecker P, Smits SHJ, Gohlke H. TopModel: Template-Based Protein Structure Prediction at Low Sequence Identity Using Top-Down Consensus and Deep Neural Networks. J Chem Theory Comput 2020; 16:1953-1967. [DOI: 10.1021/acs.jctc.9b00825] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Daniel Mulnaes
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Nicola Porta
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Rebecca Clemens
- Institute für Biochemie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Irina Apanasenko
- Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) & JuStruct, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Jens Reiners
- Institute für Biochemie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
- Center for Structural Studies Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Lothar Gremer
- Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) & JuStruct, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Philipp Neudecker
- Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) & JuStruct, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Sander H. J. Smits
- Institute für Biochemie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
- Center for Structural Studies Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Holger Gohlke
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) & JuStruct, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- John von Neumann Institute for Computing (NIC) & Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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82
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Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D. Improved protein structure prediction using potentials from deep learning. Nature 2020; 577:706-710. [PMID: 31942072 DOI: 10.1038/s41586-019-1923-7] [Citation(s) in RCA: 1403] [Impact Index Per Article: 350.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 12/10/2019] [Indexed: 12/16/2022]
Abstract
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - David T Jones
- The Francis Crick Institute, London, UK.,University College London, London, UK
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83
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Fukuda H, Tomii K. DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment. BMC Bioinformatics 2020; 21:10. [PMID: 31918654 PMCID: PMC6953294 DOI: 10.1186/s12859-019-3190-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/04/2019] [Indexed: 12/30/2022] Open
Abstract
Background Recently developed methods of protein contact prediction, a crucially important step for protein structure prediction, depend heavily on deep neural networks (DNNs) and multiple sequence alignments (MSAs) of target proteins. Protein sequences are accumulating to an increasing degree such that abundant sequences to construct an MSA of a target protein are readily obtainable. Nevertheless, many cases present different ends of the number of sequences that can be included in an MSA used for contact prediction. The abundant sequences might degrade prediction results, but opportunities remain for a limited number of sequences to construct an MSA. To resolve these persistent issues, we strove to develop a novel framework using DNNs in an end-to-end manner for contact prediction. Results We developed neural network models to improve precision of both deep and shallow MSAs. Results show that higher prediction accuracy was achieved by assigning weights to sequences in a deep MSA. Moreover, for shallow MSAs, adding a few sequential features was useful to increase the prediction accuracy of long-range contacts in our model. Based on these models, we expanded our model to a multi-task model to achieve higher accuracy by incorporating predictions of secondary structures and solvent-accessible surface areas. Moreover, we demonstrated that ensemble averaging of our models can raise accuracy. Using past CASP target protein domains, we tested our models and demonstrated that our final model is superior to or equivalent to existing meta-predictors. Conclusions The end-to-end learning framework we built can use information derived from either deep or shallow MSAs for contact prediction. Recently, an increasing number of protein sequences have become accessible, including metagenomic sequences, which might degrade contact prediction results. Under such circumstances, our model can provide a means to reduce noise automatically. According to results of tertiary structure prediction based on contacts and secondary structures predicted by our model, more accurate three-dimensional models of a target protein are obtainable than those from existing ECA methods, starting from its MSA. DeepECA is available from https://github.com/tomiilab/DeepECA.
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Affiliation(s)
- Hiroyuki Fukuda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba-ken, 277-8562, Japan
| | - Kentaro Tomii
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba-ken, 277-8562, Japan. .,Artificial Intelligence Research Center (AIRC), Biotechnology Research Institute for Drug Discovery, Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.
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84
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Mao W, Ding W, Xing Y, Gong H. AmoebaContact and GDFold as a pipeline for rapid de novo protein structure prediction. NAT MACH INTELL 2019. [DOI: 10.1038/s42256-019-0130-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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85
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Shi Q, Chen W, Huang S, Wang Y, Xue Z. Deep learning for mining protein data. Brief Bioinform 2019; 22:194-218. [PMID: 31867611 DOI: 10.1093/bib/bbz156] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 10/21/2019] [Accepted: 11/07/2019] [Indexed: 01/16/2023] Open
Abstract
The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.
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Affiliation(s)
- Qiang Shi
- School of Software Engineering, Huazhong University of Science and Technology. His main interests cover machine learning especially deep learning, protein data analysis, and big data mining
| | - Weiya Chen
- School of Software Engineering, Huazhong University of Science & Technology, Wuhan, China. His research interests cover bioinformatics, virtual reality, and data visualization
| | - Siqi Huang
- Software Engineering at Huazhong University of science and technology, focusing on Machine learning and data mining
| | - Yan Wang
- School of life, University of Science & Technology; her main interests cover protein structure and function prediction and big data mining
| | - Zhidong Xue
- School of Software Engineering, Huazhong University of Science & Technology, Wuhan, China. His research interests cover bioinformatics, machine learning, and image processing
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86
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Revisiting the "satisfaction of spatial restraints" approach of MODELLER for protein homology modeling. PLoS Comput Biol 2019; 15:e1007219. [PMID: 31846452 PMCID: PMC6938380 DOI: 10.1371/journal.pcbi.1007219] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 12/31/2019] [Accepted: 11/13/2019] [Indexed: 01/02/2023] Open
Abstract
The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of σ values profoundly influences 3D modeling. We show that the σ values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the program’s predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLER’s objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance. Proteins are fundamental biological molecules that carry out countless activities in living beings. Since the function of proteins is dictated by their three-dimensional atomic structures, acquiring structural details of proteins provides deep insights into their function. Currently, the most frequently used computational approach for protein structure prediction is template-based modeling. In this approach, a target protein is modeled using the experimentally-derived structural information of a template protein assumed to have a similar structure to the target. MODELLER is the most frequently used program for template-based 3D model building. Despite its success, its predictions are not always accurate enough to be useful in Biomedical Research. Here, we show that it is possible to greatly increase the performance of MODELLER by modifying two aspects of its algorithm. First, we demonstrate that providing the program with accurate estimations of local target-template structural divergence greatly increases the quality of its predictions. Additionally, we show that modifying MODELLER’s scoring function with statistical potential energetic terms also helps to improve modeling quality. This work will be useful in future research, since it reports practical strategies to improve the performance of this core tool in Structural Bioinformatics.
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87
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Bittrich S, Schroeder M, Labudde D. StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map. Sci Rep 2019; 9:18517. [PMID: 31811259 PMCID: PMC6898053 DOI: 10.1038/s41598-019-55047-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 11/21/2019] [Indexed: 12/17/2022] Open
Abstract
Protein folding and structure prediction are two sides of the same coin. Contact maps and the related techniques of constraint-based structure reconstruction can be considered as unifying aspects of both processes. We present the Structural Relevance (SR) score which quantifies the information content of individual contacts and residues in the context of the whole native structure. The physical process of protein folding is commonly characterized with spatial and temporal resolution: some residues are Early Folding while others are Highly Stable with respect to unfolding events. We employ the proposed SR score to demonstrate that folding initiation and structure stabilization are subprocesses realized by distinct sets of residues. The example of cytochrome c is used to demonstrate how StructureDistiller identifies the most important contacts needed for correct protein folding. This shows that entries of a contact map are not equally relevant for structural integrity. The proposed StructureDistiller algorithm identifies contacts with the highest information content; these entries convey unique constraints not captured by other contacts. Identification of the most informative contacts effectively doubles resilience toward contacts which are not observed in the native contact map. Furthermore, this knowledge increases reconstruction fidelity on sparse contact maps significantly by 0.4 Å.
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Affiliation(s)
- Sebastian Bittrich
- University of Applied Sciences Mittweida, Mittweida, 09648, Germany. .,Biotechnology Center (BIOTEC), TU Dresden, Dresden, 01307, Germany. .,Research Collaboratory for Structural Bioinformatics Protein Data Bank, University of California, San Diego, La Jolla, CA, 92093, USA.
| | | | - Dirk Labudde
- University of Applied Sciences Mittweida, Mittweida, 09648, Germany
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88
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Rao R, Bhattacharya N, Thomas N, Duan Y, Chen X, Canny J, Abbeel P, Song YS. Evaluating Protein Transfer Learning with TAPE. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 2019; 32:9689-9701. [PMID: 33390682 PMCID: PMC7774645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Machine learning applied to protein sequences is an increasingly popular area of research. Semi-supervised learning for proteins has emerged as an important paradigm due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques. To facilitate progress in this field, we introduce the Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. We curate tasks into specific training, validation, and test splits to ensure that each task tests biologically relevant generalization that transfers to real-life scenarios. We benchmark a range of approaches to semi-supervised protein representation learning, which span recent work as well as canonical sequence learning techniques. We find that self-supervised pretraining is helpful for almost all models on all tasks, more than doubling performance in some cases. Despite this increase, in several cases features learned by self-supervised pretraining still lag behind features extracted by state-of-the-art non-neural techniques. This gap in performance suggests a huge opportunity for innovative architecture design and improved modeling paradigms that better capture the signal in biological sequences. TAPE will help the machine learning community focus effort on scientifically relevant problems. Toward this end, all data and code used to run these experiments are available at https://github.com/songlab-cal/tape.
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89
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Shrestha R, Fajardo E, Gil N, Fidelis K, Kryshtafovych A, Monastyrskyy B, Fiser A. Assessing the accuracy of contact predictions in CASP13. Proteins 2019; 87:1058-1068. [PMID: 31587357 PMCID: PMC6851495 DOI: 10.1002/prot.25819] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 09/17/2019] [Accepted: 09/17/2019] [Indexed: 01/07/2023]
Abstract
The accuracy of sequence-based tertiary contact predictions was assessed in a blind prediction experiment at the CASP13 meeting. After 4 years of significant improvements in prediction accuracy, another dramatic advance has taken place since CASP12 was held 2 years ago. The precision of predicting the top L/5 contacts in the free modeling category, where L is the corresponding length of the protein in residues, has exceeded 70%. As a comparison, the best-performing group at CASP12 with a 47% precision would have finished below the top 1/3 of the CASP13 groups. Extensively trained deep neural network approaches dominate the top performing algorithms, which appear to efficiently integrate information on coevolving residues and interacting fragments or possibly utilize memories of sequence similarities and sometimes can deliver accurate results even in the absence of virtually any target specific evolutionary information. If the current performance is evaluated by F-score on L contacts, it stands around 24% right now, which, despite the tremendous impact and advance in improving its utility for structure modeling, also suggests that there is much room left for further improvement.
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Affiliation(s)
- Rojan Shrestha
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Eduardo Fajardo
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Nelson Gil
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis CA 95616-8816, USA
| | - Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis CA 95616-8816, USA
| | - Bohdan Monastyrskyy
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis CA 95616-8816, USA
| | - Andras Fiser
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
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90
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Berman HM, Adams PD, Bonvin AA, Burley SK, Carragher B, Chiu W, DiMaio F, Ferrin TE, Gabanyi MJ, Goddard TD, Griffin PR, Haas J, Hanke CA, Hoch JC, Hummer G, Kurisu G, Lawson CL, Leitner A, Markley JL, Meiler J, Montelione GT, Phillips GN, Prisner T, Rappsilber J, Schriemer DC, Schwede T, Seidel CAM, Strutzenberg TS, Svergun DI, Tajkhorshid E, Trewhella J, Vallat B, Velankar S, Vuister GW, Webb B, Westbrook JD, White KL, Sali A. Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures. Structure 2019; 27:1745-1759. [PMID: 31780431 DOI: 10.1016/j.str.2019.11.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 10/31/2019] [Accepted: 11/06/2019] [Indexed: 12/23/2022]
Abstract
Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.
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Affiliation(s)
- Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Bridge Institute, Michelson Center, University of Southern California, Los Angeles, CA 90089, USA.
| | - Paul D Adams
- Physical Biosciences Division, Lawrence Berkeley Laboratory, Berkeley, CA 94720-8235, USA; Department of Bioengineering, University of California-Berkeley, Berkeley, CA 94720, USA
| | - Alexandre A Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences and San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA
| | - Bridget Carragher
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Wah Chiu
- Department of Bioengineering, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305-5447, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Thomas E Ferrin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Margaret J Gabanyi
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Thomas D Goddard
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Juergen Haas
- Swiss Institute of Bioinformatics and Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Christian A Hanke
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Jeffrey C Hoch
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030, USA
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany; Institute for Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Genji Kurisu
- Protein Data Bank Japan (PDBj), Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
| | - Catherine L Lawson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - John L Markley
- BioMagResBank (BMRB), Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, 465 21st Avenue South, Nashville, TN 37221, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytech Institute, Troy, NY 12180, USA
| | - George N Phillips
- BioSciences at Rice and Department of Chemistry, Rice University, Houston, TX 77251, USA
| | - Thomas Prisner
- Institute of Physical and Theoretical Chemistry and Center of Biomolecular Magnetic Resonance, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Juri Rappsilber
- Wellcome Trust Centre for Cell Biology, Edinburgh EH9 3JR, Scotland
| | - David C Schriemer
- Department of Biochemistry & Molecular Biology, Robson DNA Science Centre, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Torsten Schwede
- Swiss Institute of Bioinformatics and Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Claus A M Seidel
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | | | - Dmitri I Svergun
- European Molecular Biology Laboratory (EMBL), Hamburg Outstation, Notkestrasse 85, 22607 Hamburg, Germany
| | - Emad Tajkhorshid
- Department of Biochemistry, NIH Center for Macromolecular Modeling and Bioinformatics, Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia; Department of Chemistry, University of Utah, Salt Lake City, UT 84112, USA
| | - Brinda Vallat
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, UK
| | - Geerten W Vuister
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Leicester LE1 9HN, UK
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Kate L White
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Bridge Institute, Michelson Center, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrej Sali
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
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91
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Heo L, Feig M. High-accuracy protein structures by combining machine-learning with physics-based refinement. Proteins 2019; 88:637-642. [PMID: 31693199 DOI: 10.1002/prot.25847] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 10/05/2019] [Accepted: 11/03/2019] [Indexed: 12/16/2022]
Abstract
Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from coevolutionary analysis via machine learning to have significantly expanded the ability to predict structures for sequences without templates. One such method, AlphaFold, also performs well on sequences where templates are available but without using such information directly. Here we show that combining machine-learning based models from AlphaFold with state-of-the-art physics-based refinement via molecular dynamics simulations further improves predictions to outperform any other prediction method tested during the latest round of CASP. The resulting models have highly accurate global and local structures, including high accuracy at functionally important interface residues, and they are highly suitable as initial models for crystal structure determination via molecular replacement.
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Affiliation(s)
- Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan
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92
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Wang S, Fei S, Wang Z, Li Y, Xu J, Zhao F, Gao X. PredMP: a web server for de novo prediction and visualization of membrane proteins. Bioinformatics 2019; 35:691-693. [PMID: 30084960 DOI: 10.1093/bioinformatics/bty684] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 06/29/2018] [Accepted: 08/02/2018] [Indexed: 01/21/2023] Open
Abstract
MOTIVATION PredMP is the first web service, to our knowledge, that aims at de novo prediction of the membrane protein (MP) 3D structure followed by the embedding of the MP into the lipid bilayer for visualization. Our approach is based on a high-throughput Deep Transfer Learning (DTL) method that first predicts MP contacts by learning from non-MPs and then predicts the 3D model of the MP using the predicted contacts as distance restraints. This algorithm is derived from our previous Deep Learning (DL) method originally developed for soluble protein contact prediction, which has been officially ranked No. 1 in CASP12. The DTL framework in our approach overcomes the challenge that there are only a limited number of solved MP structures for training the deep learning model. There are three modules in the PredMP server: (i) The DTL framework followed by the contact-assisted folding protocol has already been implemented in RaptorX-Contact, which serves as the key module for 3D model generation; (ii) The 1D annotation module, implemented in RaptorX-Property, is used to predict the secondary structure and disordered regions; and (iii) the visualization module to display the predicted MPs embedded in the lipid bilayer guided by the predicted transmembrane topology. RESULTS Tested on 510 non-redundant MPs, our server predicts correct folds for ∼290 MPs, which significantly outperforms existing methods. Tested on a blind and live benchmark CAMEO from September 2016 to January 2018, PredMP can successfully model all 10 MPs belonging to the hard category. AVAILABILITY AND IMPLEMENTATION PredMP is freely accessed on the web at http://www.predmp.com. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sheng Wang
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | | | - Zongan Wang
- Department of Chemistry, James Franck Institute, University of Chicago, Chicago, IL, USA
| | - Yu Li
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, IL, USA
| | - Feng Zhao
- Prospect Institute of Fatty Acids and Health, Qingdao University, Ningxia, China
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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93
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Chen Q, Xiao Y, Zhang W, Mu W. Current methods and applications in computational protein design for food industry. Crit Rev Food Sci Nutr 2019; 60:3259-3270. [DOI: 10.1080/10408398.2019.1682513] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Qiuming Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Yaqin Xiao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Wenli Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Wanmeng Mu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- International Joint Laboratory on Food Safety, Jiangnan University, Wuxi, China
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94
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AlQuraishi M. AlphaFold at CASP13. Bioinformatics 2019; 35:4862-4865. [PMID: 31116374 PMCID: PMC6907002 DOI: 10.1093/bioinformatics/btz422] [Citation(s) in RCA: 156] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 03/26/2019] [Accepted: 05/15/2019] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Computational prediction of protein structure from sequence is broadly viewed as a foundational problem of biochemistry and one of the most difficult challenges in bioinformatics. Once every two years the Critical Assessment of protein Structure Prediction (CASP) experiments are held to assess the state of the art in the field in a blind fashion, by presenting predictor groups with protein sequences whose structures have been solved but have not yet been made publicly available. The first CASP was organized in 1994, and the latest, CASP13, took place last December, when for the first time the industrial laboratory DeepMind entered the competition. DeepMind's entry, AlphaFold, placed first in the Free Modeling (FM) category, which assesses methods on their ability to predict novel protein folds (the Zhang group placed first in the Template-Based Modeling (TBM) category, which assess methods on predicting proteins whose folds are related to ones already in the Protein Data Bank.) DeepMind's success generated significant public interest. Their approach builds on two ideas developed in the academic community during the preceding decade: (i) the use of co-evolutionary analysis to map residue co-variation in protein sequence to physical contact in protein structure, and (ii) the application of deep neural networks to robustly identify patterns in protein sequence and co-evolutionary couplings and convert them into contact maps. In this Letter, we contextualize the significance of DeepMind's entry within the broader history of CASP, relate AlphaFold's methodological advances to prior work, and speculate on the future of this important problem.
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Affiliation(s)
- Mohammed AlQuraishi
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
- Lab of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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95
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Hanson J, Paliwal K, Litfin T, Yang Y, Zhou Y. Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks. Bioinformatics 2019; 34:4039-4045. [PMID: 29931279 DOI: 10.1093/bioinformatics/bty481] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 06/13/2018] [Indexed: 11/12/2022] Open
Abstract
Motivation Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here, we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information. Results We show that the proposed method achieves a robust performance over validation and independent test sets with the Area Under the receiver operating characteristic Curve (AUC) > 0.95 in all tests. When compared to several state-of-the-art methods for independent testing of 228 proteins, the method yields an AUC value of 0.958, whereas the next-best method obtains an AUC of 0.909. More importantly, the improvement is over contacts at all sequence-position separations. Specifically, a 8.95%, 5.65% and 2.84% increase in precision were observed for the top L∕10 predictions over the next best for short, medium and long-range contacts, respectively. This confirms the usefulness of ResNets to congregate the short-range relations and 2D-BRLSTM to propagate the long-range dependencies throughout the entire protein contact map 'image'. Availability and implementation SPOT-Contact server url: http://sparks-lab.org/jack/server/SPOT-Contact/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jack Hanson
- Signal Processing Laboratory, Griffith University, Brisbane, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, Griffith University, Brisbane, Australia
| | - Thomas Litfin
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Australia
| | - Yuedong Yang
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Australia
- School of Data and Computer Science, Sun-Yat Sen University, Guangzhou, Guangdong, China
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Australia
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96
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Guterres H, Lee HS, Im W. Ligand-Binding-Site Structure Refinement Using Molecular Dynamics with Restraints Derived from Predicted Binding Site Templates. J Chem Theory Comput 2019; 15:6524-6535. [PMID: 31557013 DOI: 10.1021/acs.jctc.9b00751] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Accurate modeling of ligand-binding-site structures plays a critical role in structure-based virtual screening. However, the structures of the ligand-binding site in most predicted protein models are generally of low quality and need refinements. In this work, we present a ligand-binding-site structure refinement protocol using molecular dynamics simulation with restraints derived from predicted binding site templates. Our benchmark validation shows great performance for 40 diverse sets of proteins from the Astex list. The ligand-binding sites on modeled protein structures are consistently refined using our method with an average Cα RMSD improvement of 0.90 Å. Comparison of ligand binding modes from ligand docking to initial unrefined and refined structures shows an average of 1.97 Å RMSD improvement in the refined structures. These results demonstrate a promising new method of structure refinement for protein ligand-binding-site structures.
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Affiliation(s)
- Hugo Guterres
- Department of Biological Sciences , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Hui Sun Lee
- Department of Biological Sciences , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Wonpil Im
- Department of Biological Sciences , Lehigh University , Bethlehem , Pennsylvania 18015 , United States.,School of Computational Sciences , Korea Institute for Advanced Study , Seoul 02455 , Republic of Korea
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97
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Sala D, Huang YJ, Cole CA, Snyder DA, Liu G, Ishida Y, Swapna GVT, Brock KP, Sander C, Fidelis K, Kryshtafovych A, Inouye M, Tejero R, Valafar H, Rosato A, Montelione GT. Protein structure prediction assisted with sparse NMR data in CASP13. Proteins 2019; 87:1315-1332. [PMID: 31603581 DOI: 10.1002/prot.25837] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 01/05/2023]
Abstract
CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.
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Affiliation(s)
- Davide Sala
- Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York
| | - Casey A Cole
- Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina
| | - David A Snyder
- Department of Chemistry, College of Science and Health, William Paterson University, Wayne, New Jersey
| | - Gaohua Liu
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Nexomics Biosciences, Bordentown, New Jersey
| | - Yojiro Ishida
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - G V T Swapna
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts.,cBio Center, Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | | | - Masayori Inouye
- Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Roberto Tejero
- Departamento de Quimica Fisica, Universidad de Valencia, Valencia, Spain
| | - Homayoun Valafar
- Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina
| | - Antonio Rosato
- Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York.,Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
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98
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Guzenko D, Lafita A, Monastyrskyy B, Kryshtafovych A, Duarte JM. Assessment of protein assembly prediction in CASP13. Proteins 2019; 87:1190-1199. [PMID: 31374138 DOI: 10.1002/prot.25795] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 07/11/2019] [Accepted: 07/27/2019] [Indexed: 01/08/2023]
Abstract
We present the assembly category assessment in the 13th edition of the CASP community-wide experiment. For the second time, protein assemblies constitute an independent assessment category. Compared to the last edition we see a clear uptake in participation, more oligomeric targets released, and consistent, albeit modest, improvement of the predictions quality. Looking at the tertiary structure predictions, we observe that ignoring the oligomeric state of the targets hinders modeling success. We also note that some contact prediction groups successfully predicted homomeric interfacial contacts, though it appears that these predictions were not used for assembly modeling. Homology modeling with sizeable human intervention appears to form the basis of the assembly prediction techniques in this round of CASP. Future developments should see more integrated approaches where subunits are modeled in the context of the assemblies they form.
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Affiliation(s)
- Dmytro Guzenko
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, California
| | - Aleix Lafita
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Bohdan Monastyrskyy
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California, USA
| | - Andriy Kryshtafovych
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California, USA
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, California
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99
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Shin K, Tran DP, Takemura K, Kitao A, Terayama K, Tsuda K. Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method. ACS OMEGA 2019; 4:13853-13862. [PMID: 31497702 PMCID: PMC6714528 DOI: 10.1021/acsomega.9b01480] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 08/01/2019] [Indexed: 05/13/2023]
Abstract
This paper proposes a novel molecular simulation method, called tree search molecular dynamics (TS-MD), to accelerate the sampling of conformational transition pathways, which require considerable computation. In TS-MD, a tree search algorithm, called upper confidence bounds for trees, which is a type of reinforcement learning algorithm, is applied to sample the transition pathway. By learning from the results of the previous simulations, TS-MD efficiently searches conformational space and avoids being trapped in local stable structures. TS-MD exhibits better performance than parallel cascade selection molecular dynamics, which is one of the state-of-the-art methods, for the folding of miniproteins, Chignolin and Trp-cage, in explicit water.
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Affiliation(s)
- Kento Shin
- Graduate School
of Frontier Sciences, The University of
Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan
| | - Duy Phuoc Tran
- Graduate School
of Frontier Sciences, The University of
Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan
| | - Kazuhiro Takemura
- School
of Life Sciences and Technology, Tokyo Institute
of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Akio Kitao
- School
of Life Sciences and Technology, Tokyo Institute
of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Kei Terayama
- RIKEN Center for
Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Medical Sciences
Innovation Hub Program, RIKEN Cluster for Science, Technology and
Innovation Hub, Kanagawa 230-0045, Japan
- Department
of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
- E-mail: (Kei Terayama)
| | - Koji Tsuda
- Graduate School
of Frontier Sciences, The University of
Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan
- RIKEN Center for
Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Ibaraki 305-0047, Japan
- E-mail: . Phone: +81(4)-7136-3983. Fax: +81(4)-7136-3975 (Koji Tsuda)
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100
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Torrisi M, Kaleel M, Pollastri G. Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction. Sci Rep 2019; 9:12374. [PMID: 31451723 PMCID: PMC6710256 DOI: 10.1038/s41598-019-48786-x] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 08/12/2019] [Indexed: 01/10/2023] Open
Abstract
Protein Secondary Structure prediction has been a central topic of research in Bioinformatics for decades. In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction accuracy (88–90%), while only a few predict more than the 3 traditional Helix, Strand and Coil classes. In this study we present tests on different models trained both on single sequence and evolutionary profile-based inputs and develop a new state-of-the-art system with Porter 5. Porter 5 is composed of ensembles of cascaded Bidirectional Recurrent Neural Networks and Convolutional Neural Networks, incorporates new input encoding techniques and is trained on a large set of protein structures. Porter 5 achieves 84% accuracy (81% SOV) when tested on 3 classes and 73% accuracy (70% SOV) on 8 classes on a large independent set. In our tests Porter 5 is 2% more accurate than its previous version and outperforms or matches the most recent predictors of secondary structure we tested. When Porter 5 is retrained on SCOPe based sets that eliminate homology between training/testing samples we obtain similar results. Porter is available as a web server and standalone program at http://distilldeep.ucd.ie/porter/ alongside all the datasets and alignments.
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Affiliation(s)
- Mirko Torrisi
- School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Manaz Kaleel
- School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Gianluca Pollastri
- School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland.
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