1
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Musil M, Jezik A, Horackova J, Borko S, Kabourek P, Damborsky J, Bednar D. FireProt 2.0: web-based platform for the fully automated design of thermostable proteins. Brief Bioinform 2023; 25:bbad425. [PMID: 38018911 PMCID: PMC10685400 DOI: 10.1093/bib/bbad425] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/25/2023] [Accepted: 11/01/2023] [Indexed: 11/30/2023] Open
Abstract
Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron-Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool's availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.
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Affiliation(s)
- Milos Musil
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic
- Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Centre, St. Anne’s University Hospital Brno, Brno, Czech Republic
| | - Andrej Jezik
- Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
| | - Jana Horackova
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic
| | - Simeon Borko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic
- Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Centre, St. Anne’s University Hospital Brno, Brno, Czech Republic
| | - Petr Kabourek
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic
- International Clinical Research Centre, St. Anne’s University Hospital Brno, Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic
- International Clinical Research Centre, St. Anne’s University Hospital Brno, Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic
- International Clinical Research Centre, St. Anne’s University Hospital Brno, Brno, Czech Republic
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2
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Zhou L, Feng T, Xu S, Gao F, Lam TT, Wang Q, Wu T, Huang H, Zhan L, Li L, Guan Y, Dai Z, Yu G. ggmsa: a visual exploration tool for multiple sequence alignment and associated data. Brief Bioinform 2022; 23:6603927. [PMID: 35671504 DOI: 10.1093/bib/bbac222] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/07/2022] [Accepted: 05/11/2022] [Indexed: 12/25/2022] Open
Abstract
The identification of the conserved and variable regions in the multiple sequence alignment (MSA) is critical to accelerating the process of understanding the function of genes. MSA visualizations allow us to transform sequence features into understandable visual representations. As the sequence-structure-function relationship gains increasing attention in molecular biology studies, the simple display of nucleotide or protein sequence alignment is not satisfied. A more scalable visualization is required to broaden the scope of sequence investigation. Here we present ggmsa, an R package for mining comprehensive sequence features and integrating the associated data of MSA by a variety of display methods. To uncover sequence conservation patterns, variations and recombination at the site level, sequence bundles, sequence logos, stacked sequence alignment and comparative plots are implemented. ggmsa supports integrating the correlation of MSA sequences and their phenotypes, as well as other traits such as ancestral sequences, molecular structures, molecular functions and expression levels. We also design a new visualization method for genome alignments in multiple alignment format to explore the pattern of within and between species variation. Combining these visual representations with prime knowledge, ggmsa assists researchers in discovering MSA and making decisions. The ggmsa package is open-source software released under the Artistic-2.0 license, and it is freely available on Bioconductor (https://bioconductor.org/packages/ggmsa) and Github (https://github.com/YuLab-SMU/ggmsa).
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Affiliation(s)
- Lang Zhou
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Division of Laboratory Medicine, Microbiome Medicine Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Tingze Feng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Fangluan Gao
- Institute of Plant Virology, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Tommy T Lam
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, China.,Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong SAR, China
| | - Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Tianzhi Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Huina Huang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Zhuhai International Travel Healthcare Center, Zhuhai, Guangdong, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lin Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yi Guan
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, China.,Joint Institute of Virology (Shantou University - The University of Hong Kong), Shantou University, Shantou, China
| | - Zehan Dai
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Division of Laboratory Medicine, Microbiome Medicine Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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3
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Braberg H, Echeverria I, Kaake RM, Sali A, Krogan NJ. From systems to structure - using genetic data to model protein structures. Nat Rev Genet 2022; 23:342-354. [PMID: 35013567 PMCID: PMC8744059 DOI: 10.1038/s41576-021-00441-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2021] [Indexed: 12/11/2022]
Abstract
Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.
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Affiliation(s)
- Hannes Braberg
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Robyn M Kaake
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Gladstone Institutes, San Francisco, CA, USA
| | - Andrej Sali
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Gladstone Institutes, San Francisco, CA, USA.
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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4
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Abstract
Compensatory substitutions happen when one mutation is advantageously selected because it restores the loss of fitness induced by a previous deleterious mutation. How frequent such mutations occur in evolution and what is the structural and functional context permitting their emergence remain open questions. We built an atlas of intra-protein compensatory substitutions using a phylogenetic approach and a dataset of 1,630 bacterial protein families for which high-quality sequence alignments and experimentally derived protein structures were available. We identified more than 51,000 positions coevolving by the mean of predicted compensatory mutations. Using the evolutionary and structural properties of the analyzed positions, we demonstrate that compensatory mutations are scarce (typically only a few in the protein history) but widespread (the majority of proteins experienced at least one). Typical coevolving residues are evolving slowly, are located in the protein core outside secondary structure motifs, and are more often in contact than expected by chance, even after accounting for their evolutionary rate and solvent exposure. An exception to this general scheme is residues coevolving for charge compensation, which are evolving faster than noncoevolving sites, in contradiction with predictions from simple coevolutionary models, but similar to stem pairs in RNA. While sites with a significant pattern of coevolution by compensatory mutations are rare, the comparative analysis of hundreds of structures ultimately permits a better understanding of the link between the three-dimensional structure of a protein and its fitness landscape.
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Affiliation(s)
- Shilpi Chaurasia
- RG Molecular Systems Evolution, Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, August-Thienemann-Straße 2, 24306 Plön, Germany.,Excelra Knowledge Solutions Pvt Ltd, Hyderabad, India
| | - Julien Y Dutheil
- RG Molecular Systems Evolution, Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, August-Thienemann-Straße 2, 24306 Plön, Germany.,Institute of Evolution Sciences of Montpellier (ISEM), CNRS, University of Montpellier, IRD, EPHE, 34095 Montpellier, France
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5
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Wang Y, Lei R, Nourmohammad A, Wu NC. Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing. eLife 2021; 10:e72516. [PMID: 34878407 PMCID: PMC8683081 DOI: 10.7554/elife.72516] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/07/2021] [Indexed: 11/13/2022] Open
Abstract
As one of the main influenza antigens, neuraminidase (NA) in H3N2 virus has evolved extensively for more than 50 years due to continuous immune pressure. While NA has recently emerged as an effective vaccine target, biophysical constraints on the antigenic evolution of NA remain largely elusive. Here, we apply combinatorial mutagenesis and next-generation sequencing to characterize the local fitness landscape in an antigenic region of NA in six different human H3N2 strains that were isolated around 10 years apart. The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved. Our analysis further demonstrates that local net charge governs the pairwise epistasis in this antigenic region. In addition, we show that residue coevolution in this antigenic region is correlated with the pairwise epistasis between charge states. Overall, this study demonstrates the importance of quantifying epistasis and the underlying biophysical constraint for building a model of influenza evolution.
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Affiliation(s)
- Yiquan Wang
- Department of Biochemistry, University of Illinois at Urbana-ChampaignUrbanaUnited States
| | - Ruipeng Lei
- Department of Biochemistry, University of Illinois at Urbana-ChampaignUrbanaUnited States
| | - Armita Nourmohammad
- Department of Physics, University of WashingtonSeattleUnited States
- Max Planck Institute for Dynamics and Self-OrganizationGöttingenGermany
- Fred Hutchinson Cancer Research CenterSeattleUnited States
| | - Nicholas C Wu
- Department of Biochemistry, University of Illinois at Urbana-ChampaignUrbanaUnited States
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-ChampaignUrbanaUnited States
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-ChampaignUrbanaUnited States
- Carle Illinois College of Medicine, University of Illinois at Urbana-ChampaignUrbanaUnited States
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6
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Lindorff-Larsen K, Kragelund BB. On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins. J Mol Biol 2021; 433:167196. [PMID: 34390736 DOI: 10.1016/j.jmb.2021.167196] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 11/29/2022]
Abstract
Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.
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Affiliation(s)
- Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory & Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen. Ole Maaløes Vej 5, DK-2200 Copenhagen N, Denmark.
| | - Birthe B Kragelund
- Structural Biology and NMR Laboratory & Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen. Ole Maaløes Vej 5, DK-2200 Copenhagen N, Denmark.
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7
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Tian P, Best RB. Exploring the sequence fitness landscape of a bridge between protein folds. PLoS Comput Biol 2020; 16:e1008285. [PMID: 33048928 PMCID: PMC7553338 DOI: 10.1371/journal.pcbi.1008285] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 08/24/2020] [Indexed: 12/15/2022] Open
Abstract
Most foldable protein sequences adopt only a single native fold. Recent protein design studies have, however, created protein sequences which fold into different structures apon changes of environment, or single point mutation, the best characterized example being the switch between the folds of the GA and GB binding domains of streptococcal protein G. To obtain further insight into the design of sequences which can switch folds, we have used a computational model for the fitness landscape of a single fold, built from the observed sequence variation of protein homologues. We have recently shown that such coevolutionary models can be used to design novel foldable sequences. By appropriately combining two of these models to describe the joint fitness landscape of GA and GB, we are able to describe the propensity of a given sequence for each of the two folds. We have successfully tested the combined model against the known series of designed GA/GB hybrids. Using Monte Carlo simulations on this landscape, we are able to identify pathways of mutations connecting the two folds. In the absence of a requirement for domain stability, the most frequent paths go via sequences in which neither domain is stably folded, reminiscent of the propensity for certain intrinsically disordered proteins to fold into different structures according to context. Even if the folded state is required to be stable, we find that there is nonetheless still a wide range of sequences which are close to the transition region and therefore likely fold switches, consistent with recent estimates that fold switching may be more widespread than had been thought.
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Affiliation(s)
- Pengfei Tian
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, U.S.A
| | - Robert B. Best
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, U.S.A
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8
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Zhang GJ, Ma LF, Wang XQ, Zhou XG. Secondary Structure and Contact Guided Differential Evolution for Protein Structure Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1068-1081. [PMID: 30295627 DOI: 10.1109/tcbb.2018.2873691] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Ab initio protein tertiary structure prediction is one of the long-standing problems in structural bioinformatics. With the help of residue-residue contact and secondary structure prediction information, the accuracy of ab initio structure prediction can be enhanced. In this study, an improved differential evolution with secondary structure and residue-residue contact information referred to as SCDE is proposed for protein structure prediction. In SCDE, two score models based on secondary structure and contact information are proposed, and two selection strategies, namely, secondary structure-based selection strategy and contact-based selection strategy, are designed to guide conformation space search. A probability distribution function is designed to balance these two selection strategies. Experimental results on a benchmark dataset with 28 proteins and four free model targets in CASP12 demonstrate that the proposed SCDE is effective and efficient.
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9
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Wang X, Jing X, Deng Y, Nie Y, Xu F, Xu Y, Zhao YL, Hunt JF, Montelione GT, Szyperski T. Evolutionary coupling saturation mutagenesis: Coevolution-guided identification of distant sites influencing Bacillus naganoensis pullulanase activity. FEBS Lett 2019; 594:799-812. [PMID: 31665817 DOI: 10.1002/1873-3468.13652] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/15/2019] [Accepted: 10/25/2019] [Indexed: 01/20/2023]
Abstract
Pullulanases are well-known debranching enzymes hydrolyzing α-1,6-glycosidic linkages. To date, engineering of pullulanase is mainly focused on catalytic pocket or domain tailoring based on structure/sequence information. Saturation mutagenesis-involved directed evolution is, however, limited by the low number of mutational sites compatible with combinatorial libraries of feasible size. Using Bacillus naganoensis pullulanase as a target protein, here we introduce the 'evolutionary coupling saturation mutagenesis' (ECSM) approach: residue pair covariances are calculated to identify residues for saturation mutagenesis, focusing directed evolution on residue pairs playing important roles in natural evolution. Evolutionary coupling (EC) analysis identified seven residue pairs as evolutionary mutational hotspots. Subsequent saturation mutagenesis yielded variants with enhanced catalytic activity. The functional pairs apparently represent distant sites affecting enzyme activity.
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Affiliation(s)
- Xinye Wang
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Xiaoran Jing
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yi Deng
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yao Nie
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Fei Xu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yan Xu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Yi-Lei Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - John F Hunt
- Department of Biological Sciences, Columbia University, New York, NY, 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, USA.,Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.,Department of Chemistry and Chemical Biology, and Center for Biotechnology and Integrative Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Thomas Szyperski
- Department of Chemistry, The State University of New York at Buffalo, NY, USA
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10
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Wozniak PP, Pelc J, Skrzypecki M, Vriend G, Kotulska M. Bio-knowledge-based filters improve residue-residue contact prediction accuracy. Bioinformatics 2019; 34:3675-3683. [PMID: 29850768 DOI: 10.1093/bioinformatics/bty416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 05/19/2018] [Indexed: 11/13/2022] Open
Abstract
Motivation Residue-residue contact prediction through direct coupling analysis has reached impressive accuracy, but yet higher accuracy will be needed to allow for routine modelling of protein structures. One way to improve the prediction accuracy is to filter predicted contacts using knowledge about the particular protein of interest or knowledge about protein structures in general. Results We focus on the latter and discuss a set of filters that can be used to remove false positive contact predictions. Each filter depends on one or a few cut-off parameters for which the filter performance was investigated. Combining all filters while using default parameters resulted for a test set of 851 protein domains in the removal of 29% of the predictions of which 92% were indeed false positives. Availability and implementation All data and scripts are available at http://comprec-lin.iiar.pwr.edu.pl/FPfilter/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- P P Wozniak
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
| | - J Pelc
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
| | - M Skrzypecki
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
| | - G Vriend
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - M Kotulska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
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11
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Marchi J, Galpern EA, Espada R, Ferreiro DU, Walczak AM, Mora T. Size and structure of the sequence space of repeat proteins. PLoS Comput Biol 2019; 15:e1007282. [PMID: 31415557 PMCID: PMC6733475 DOI: 10.1371/journal.pcbi.1007282] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Revised: 09/09/2019] [Accepted: 07/24/2019] [Indexed: 11/18/2022] Open
Abstract
The coding space of protein sequences is shaped by evolutionary constraints set by requirements of function and stability. We show that the coding space of a given protein family—the total number of sequences in that family—can be estimated using models of maximum entropy trained on multiple sequence alignments of naturally occuring amino acid sequences. We analyzed and calculated the size of three abundant repeat proteins families, whose members are large proteins made of many repetitions of conserved portions of ∼30 amino acids. While amino acid conservation at each position of the alignment explains most of the reduction of diversity relative to completely random sequences, we found that correlations between amino acid usage at different positions significantly impact that diversity. We quantified the impact of different types of correlations, functional and evolutionary, on sequence diversity. Analysis of the detailed structure of the coding space of the families revealed a rugged landscape, with many local energy minima of varying sizes with a hierarchical structure, reminiscent of fustrated energy landscapes of spin glass in physics. This clustered structure indicates a multiplicity of subtypes within each family, and suggests new strategies for protein design. Natural protein molecules are only a small subset of the possible strings of amino acids. This naturally calls the question of how many protein sequences theoretically exist that are functional, and how many have already been explored by nature. To help answer this question, we developed a statistical method to calculate the total potential number of protein sequences of a given family, focusing on three families of repeat proteins, which play important roles in a variety of cellular processes. The number of sequences that we compute is limited by functional interactions between the residues of the protein, as well as its evolutionary history. Applying techniques from the physics of disordered systems, we show that the space of sequences has a rugged structure, which could hinder their evolution. Individual proteins can be organised into distinct clusters corresponding to basins of attraction of the landscape, suggesting the existence of subfamilies within each family.
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Affiliation(s)
- Jacopo Marchi
- Laboratoire de physique de l’École normale supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France
| | - Ezequiel A. Galpern
- Protein Physiology Lab, Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica, Buenos Aires, Argentina
- CONICET - Universidad de Buenos Aires, Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Buenos Aires, Argentina
| | - Rocio Espada
- Laboratoire Gulliver, Ecole supérieure de physique et chimie industrielles (PSL University) and CNRS, 75005, Paris, France
| | - Diego U. Ferreiro
- Protein Physiology Lab, Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica, Buenos Aires, Argentina
- CONICET - Universidad de Buenos Aires, Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Buenos Aires, Argentina
| | - Aleksandra M. Walczak
- Laboratoire de physique de l’École normale supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France
- * E-mail: (AMW); (TM)
| | - Thierry Mora
- Laboratoire de physique de l’École normale supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France
- * E-mail: (AMW); (TM)
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12
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Musil M, Stourac J, Bendl J, Brezovsky J, Prokop Z, Zendulka J, Martinek T, Bednar D, Damborsky J. FireProt: web server for automated design of thermostable proteins. Nucleic Acids Res 2019; 45:W393-W399. [PMID: 28449074 PMCID: PMC5570187 DOI: 10.1093/nar/gkx285] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Accepted: 04/11/2017] [Indexed: 01/07/2023] Open
Abstract
There is a continuous interest in increasing proteins stability to enhance their usability in numerous biomedical and biotechnological applications. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. Here, we present FireProt, a web server for the automated design of multiple-point thermostable mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen tools and three protein engineering strategies for making reliable protein designs. The server is complemented with interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable mutants. FireProt is freely available at http://loschmidt.chemi.muni.cz/fireprot.
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Affiliation(s)
- Milos Musil
- Loschmidt Laboratories, Department of Experimental Biology, Masaryk University, Brno, Czech Republic.,Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology, Masaryk University, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Jaroslav Bendl
- Loschmidt Laboratories, Department of Experimental Biology, Masaryk University, Brno, Czech Republic.,Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Jan Brezovsky
- Loschmidt Laboratories, Department of Experimental Biology, Masaryk University, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology, Masaryk University, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Jaroslav Zendulka
- Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic.,Centre of Excellence IT4Innovations, Technical University Ostrava, Ostrava
| | - Tomas Martinek
- Loschmidt Laboratories, Department of Experimental Biology, Masaryk University, Brno, Czech Republic.,Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic.,Centre of Excellence IT4Innovations, Technical University Ostrava, Ostrava
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology, Masaryk University, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology, Masaryk University, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czech Republic
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13
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Thirumalai D, Hyeon C, Zhuravlev PI, Lorimer GH. Symmetry, Rigidity, and Allosteric Signaling: From Monomeric Proteins to Molecular Machines. Chem Rev 2019; 119:6788-6821. [DOI: 10.1021/acs.chemrev.8b00760] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- D. Thirumalai
- Department of Chemistry, The University of Texas, Austin, Texas 78712, United States
| | - Changbong Hyeon
- Korea Institute for Advanced Study, Seoul 02455, Republic of Korea
| | - Pavel I. Zhuravlev
- Biophysics Program, Institute for Physical Science and Technology and Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, United States
| | - George H. Lorimer
- Biophysics Program, Institute for Physical Science and Technology and Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, United States
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14
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Jing X, Dong Q, Lu R, Dong Q. Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications. Curr Bioinform 2019. [DOI: 10.2174/1574893613666181109130430] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:Protein inter-residue contacts prediction play an important role in the field of protein structure and function research. As a low-dimensional representation of protein tertiary structure, protein inter-residue contacts could greatly help de novo protein structure prediction methods to reduce the conformational search space. Over the past two decades, various methods have been developed for protein inter-residue contacts prediction.Objective:We provide a comprehensive and systematic review of protein inter-residue contacts prediction methods.Results:Protein inter-residue contacts prediction methods are roughly classified into five categories: correlated mutations methods, machine-learning methods, fusion methods, templatebased methods and 3D model-based methods. In this paper, firstly we describe the common definition of protein inter-residue contacts and show the typical application of protein inter-residue contacts. Then, we present a comprehensive review of the three main categories for protein interresidue contacts prediction: correlated mutations methods, machine-learning methods and fusion methods. Besides, we analyze the constraints for each category. Furthermore, we compare several representative methods on the CASP11 dataset and discuss performances of these methods in detail.Conclusion:Correlated mutations methods achieve better performances for long-range contacts, while the machine-learning method performs well for short-range contacts. Fusion methods could take advantage of the machine-learning and correlated mutations methods. Employing more effective fusion strategy could be helpful to further improve the performances of fusion methods.
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Affiliation(s)
- Xiaoyang Jing
- School of Computer Science, Fudan University, Shanghai, China
| | - Qimin Dong
- Vocational and Technical Education Center of Linxi County, Chifeng, Inner Mongolia, China
| | - Ruqian Lu
- School of Computer Science, Fudan University, Shanghai, China
| | - Qiwen Dong
- Faculty of Education, East China Normal University, Shanghai, China
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15
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Wuyun Q, Zheng W, Peng Z, Yang J. A large-scale comparative assessment of methods for residue-residue contact prediction. Brief Bioinform 2019; 19:219-230. [PMID: 27802931 DOI: 10.1093/bib/bbw106] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Indexed: 11/14/2022] Open
Abstract
Sequence-based prediction of residue-residue contact in proteins becomes increasingly more important for improving protein structure prediction in the big data era. In this study, we performed a large-scale comparative assessment of 15 locally installed contact predictors. To assess these methods, we collected a big data set consisting of 680 nonredundant proteins covering different structural classes and target difficulties. We investigated a wide range of factors that may influence the precision of contact prediction, including target difficulty, structural class, the alignment depth and distribution of contact pairs in a protein structure. We found that: (1) the machine learning-based methods outperform the direct-coupling-based methods for short-range contact prediction, while the latter are significantly better for long-range contact prediction. The consensus-based methods, which combine machine learning and direct-coupling methods, perform the best. (2) The target difficulty does not have clear influence on the machine learning-based methods, while it does affect the direct-coupling and consensus-based methods significantly. (3) The alignment depth has relatively weak effect on the machine learning-based methods. However, for the direct-coupling-based methods and consensus-based methods, the predicted contacts for targets with deeper alignment tend to be more accurate. (4) All methods perform relatively better on β and α + β proteins than on α proteins. (5) Residues buried in the core of protein structure are more prone to be in contact than residues on the surface (22 versus 6%). We believe these are useful results for guiding future development of new approach to contact prediction.
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Affiliation(s)
- Qiqige Wuyun
- School of Mathematical Sciences, Nankai University, Tianjin, China
| | - Wei Zheng
- School of Mathematical Sciences, Nankai University, Tianjin, China
| | - Zhenling Peng
- Center for Applied Mathematics, Tianjin University, Tianjin, China
| | - Jianyi Yang
- School of Mathematical Sciences, Nankai University, Tianjin, China
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16
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Abstract
The comparative study of homologous proteins can provide abundant information about the functional and structural constraints on protein evolution. For example, an amino acid substitution that is deleterious may become permissive in the presence of another substitution at a second site of the protein. A popular approach for detecting coevolving residues is by looking for correlated substitution events on branches of the molecular phylogeny relating the protein-coding sequences. Here we describe a machine learning method (Bayesian graphical models) implemented in the open-source phylogenetic software package HyPhy, http://hyphy.org , for extracting a network of coevolving residues from a sequence alignment.
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17
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Huang YJ, Brock KP, Ishida Y, Swapna GVT, Inouye M, Marks DS, Sander C, Montelione GT. Combining Evolutionary Covariance and NMR Data for Protein Structure Determination. Methods Enzymol 2018; 614:363-392. [PMID: 30611430 PMCID: PMC6640129 DOI: 10.1016/bs.mie.2018.11.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.
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Affiliation(s)
- Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Yojiro Ishida
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Gurla V T Swapna
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Masayori Inouye
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School and cBio Center, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States.
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18
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Vorberg S, Seemayer S, Söding J. Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction. PLoS Comput Biol 2018; 14:e1006526. [PMID: 30395601 PMCID: PMC6237422 DOI: 10.1371/journal.pcbi.1006526] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 11/15/2018] [Accepted: 09/24/2018] [Indexed: 12/01/2022] Open
Abstract
Compensatory mutations between protein residues in physical contact can manifest themselves as statistical couplings between the corresponding columns in a multiple sequence alignment (MSA) of the protein family. Conversely, large coupling coefficients predict residue contacts. Methods for de-novo protein structure prediction based on this approach are becoming increasingly reliable. Their main limitation is the strong systematic and statistical noise in the estimation of coupling coefficients, which has so far limited their application to very large protein families. While most research has focused on improving predictions by adding external information, little progress has been made to improve the statistical procedure at the core, because our lack of understanding of the sources of noise poses a major obstacle. First, we show theoretically that the expectation value of the coupling score assuming no coupling is proportional to the product of the square roots of the column entropies, and we propose a simple entropy bias correction (EntC) that subtracts out this expectation value. Second, we show that the average product correction (APC) includes the correction of the entropy bias, partly explaining its success. Third, we have developed CCMgen, the first method for simulating protein evolution and generating realistic synthetic MSAs with pairwise statistical residue couplings. Fourth, to learn exact statistical models that reliably reproduce observed alignment statistics, we developed CCMpredPy, an implementation of the persistent contrastive divergence (PCD) method for exact inference. Fifth, we demonstrate how CCMgen and CCMpredPy can facilitate the development of contact prediction methods by analysing the systematic noise contributions from phylogeny and entropy. Using the entropy bias correction, we can disentangle both sources of noise and find that entropy contributes roughly twice as much noise as phylogeny. Knowledge about the three-dimensional structure of proteins is key to understanding their function and role in biological processes and diseases. The experimental structure determination techniques, such as X-ray crystallography or electron cryo-microscopy, are labour intensive, time-consuming and expensive. Therefore, complementary computational methods to predict a protein’s structure have become indispensable. Over the last years, immense progress has been made in predicting protein structures from their amino acid sequence by utilizing highly accurate predictions of spatial contacts between amino acid residues as constraints in folding simulations. However, contact prediction methods require large numbers of homologous protein sequences in order to discriminate between signal and noise. A major obstacle preventing progress on the statistical methodology is our limited understanding of the different components of noise that are known to affect the predictions. We provide two tools, CCMpredPy and CCMgen, that can be used to learn highly accurate statistical models for contact prediction and to simulate protein evolution according to the statistical constraints between positions of residues as specified by these models, respectively. We showcase their usefulness by quantifying the relative contribution of noise arising from entropy and phylogeny on the predicted contacts, which will facilitate the improvement of the statistical methodology.
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Affiliation(s)
- Susann Vorberg
- Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Stefan Seemayer
- Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Johannes Söding
- Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, Göttingen, Germany
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19
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Szurmant H, Weigt M. Inter-residue, inter-protein and inter-family coevolution: bridging the scales. Curr Opin Struct Biol 2018; 50:26-32. [PMID: 29101847 PMCID: PMC5940578 DOI: 10.1016/j.sbi.2017.10.014] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 10/12/2017] [Accepted: 10/13/2017] [Indexed: 10/18/2022]
Abstract
Interacting proteins coevolve at multiple but interconnected scales, from the residue-residue over the protein-protein up to the family-family level. The recent accumulation of enormous amounts of sequence data allows for the development of novel, data-driven computational approaches. Notably, these approaches can bridge scales within a single statistical framework. Although being currently applied mostly to isolated problems on single scales, their immense potential for an evolutionary informed, structural systems biology is steadily emerging.
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Affiliation(s)
- Hendrik Szurmant
- Department of Basic Medical Sciences, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, CA 91766, USA.
| | - Martin Weigt
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Biologie Computationnelle et Quantitative - Institut de Biologie Paris Seine, 75005 Paris, France.
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20
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Filip R, Leluk J. Comparative studies on variability, phylogenesis, and correlated mutations of neuraminidases from influenza virus type A. BIO-ALGORITHMS AND MED-SYSTEMS 2018. [DOI: 10.1515/bams-2017-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Neuraminidase (NA) is an important protein for the replication cycle of influenza A viruses. NA is an enzyme that cleaves the sialic acid receptors; this process plays a significant role in viral life cycle. Blocking NA with a specific inhibitor is an effective way to treat the flu. However, some strains show resistance to current drugs. Therefore, NA is the focus for the intense research for new antiviral drugs and also for the explanation of the functions of new mutations. This research focuses on determining the profile of variability and phylogenetic analysis and finding the correlated mutations within a set of 149 sequences of NA belonging to various strains of influenza A virus. In this study, we have used the original programs (Corm, Consensus Constructor, and SSSSg) and also other bioinformatics software. NA proteins are characterized by various levels of variability in different regions, which was presented in detail with the aid of ConSurf. The use of four independent methods to create the phylogenetic trees gave some new data on the evolutionary relationship within the NA family proteins. The search for correlated mutations shows several potentially important correlated positions that were not reported previously to be significant. The use of such an approach can be potentially important and gives new information regarding NA proteins of influenza A virus.
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21
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Cocco S, Feinauer C, Figliuzzi M, Monasson R, Weigt M. Inverse statistical physics of protein sequences: a key issues review. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2018; 81:032601. [PMID: 29120346 DOI: 10.1088/1361-6633/aa9965] [Citation(s) in RCA: 110] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e. evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.
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Affiliation(s)
- Simona Cocco
- Laboratoire de Physique Statistique de l'Ecole Normale Supérieure-UMR 8549, CNRS and PSL Research, Sorbonne Universités UPMC, Paris, France
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22
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Barrat-Charlaix P, Weigt M. [From sequence variability to structural and functional prediction: modeling of homologous protein families]. Biol Aujourdhui 2018; 211:239-244. [PMID: 29412135 DOI: 10.1051/jbio/2017030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Indexed: 06/08/2023]
Abstract
Thanks to next-generation sequencing, the number of sequenced genomes grows rapidly, providing in particular ample examples for the sequence variability between homologous proteins. This article discusses data-driven probabilistic sequence models, which are able to extract a multitude of information from sequence data alone, including (i) structural features like residue-residue contacts, which are formed in the folded protein, (ii) protein-protein interaction interfaces and (iii) phenotypic effects of amino-acid substitutions in proteins.
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Affiliation(s)
- Pierre Barrat-Charlaix
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Biologie Computationnelle et Quantitative, Institut de Biologie Paris Seine, 75005 Paris, France
| | - Martin Weigt
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Biologie Computationnelle et Quantitative, Institut de Biologie Paris Seine, 75005 Paris, France
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23
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Li B, Fooksa M, Heinze S, Meiler J. Finding the needle in the haystack: towards solving the protein-folding problem computationally. Crit Rev Biochem Mol Biol 2018; 53:1-28. [PMID: 28976219 PMCID: PMC6790072 DOI: 10.1080/10409238.2017.1380596] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 08/22/2017] [Accepted: 09/13/2017] [Indexed: 12/22/2022]
Abstract
Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.
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Affiliation(s)
- Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Michaela Fooksa
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, USA
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
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24
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Salinas VH, Ranganathan R. Coevolution-based inference of amino acid interactions underlying protein function. eLife 2018; 7:34300. [PMID: 30024376 PMCID: PMC6117156 DOI: 10.7554/elife.34300] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 07/18/2018] [Indexed: 02/02/2023] Open
Abstract
Protein function arises from a poorly understood pattern of energetic interactions between amino acid residues. Sequence-based strategies for deducing this pattern have been proposed, but lack of benchmark data has limited experimental verification. Here, we extend deep-mutation technologies to enable measurement of many thousands of pairwise amino acid couplings in several homologs of a protein family - a deep coupling scan (DCS). The data show that cooperative interactions between residues are loaded in a sparse, evolutionarily conserved, spatially contiguous network of amino acids. The pattern of amino acid coupling is quantitatively captured in the coevolution of amino acid positions, especially as indicated by the statistical coupling analysis (SCA), providing experimental confirmation of the key tenets of this method. This work exposes the collective nature of physical constraints on protein function and clarifies its link with sequence analysis, enabling a general practical approach for understanding the structural basis for protein function.
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Affiliation(s)
- Victor H Salinas
- Green Center for Systems BiologyUT Southwestern Medical CenterDallasUnited States
| | - Rama Ranganathan
- Center for Physics of Evolving Systems, Biochemistry and Molecular BiologyThe University of ChicagoChicagoUnited States,Institute for Molecular EngineeringThe University of ChicagoChicagoUnited States
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25
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Huang YJ, Brock KP, Sander C, Marks DS, Montelione GT. A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1105:153-169. [PMID: 30617828 DOI: 10.1007/978-981-13-2200-6_10] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
While 3D structure determination of small (<15 kDa) proteins by solution NMR is largely automated and routine, structural analysis of larger proteins is more challenging. An emerging hybrid strategy for modeling protein structures combines sparse NMR data that can be obtained for larger proteins with sequence co-variation data, called evolutionary couplings (ECs), obtained from multiple sequence alignments of protein families. This hybrid "EC-NMR" method can be used to accurately model larger (15-60 kDa) proteins, and more rapidly determine structures of smaller (5-15 kDa) proteins using only backbone NMR data. The resulting structures have accuracies relative to reference structures comparable to those obtained with full backbone and sidechain NMR resonance assignments. The requirement that evolutionary couplings (ECs) are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, potentially also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.
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Affiliation(s)
- Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Kelly P Brock
- cBio Center, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- cBio Center, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, 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, USA.
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26
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Biomolecular coevolution and its applications: Going from structure prediction toward signaling, epistasis, and function. Biochem Soc Trans 2017; 45:1253-1261. [DOI: 10.1042/bst20170063] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 08/30/2017] [Accepted: 09/04/2017] [Indexed: 01/01/2023]
Abstract
Evolution leads to considerable changes in the sequence of biomolecules, while their overall structure and function remain quite conserved. The wealth of genomic sequences, the ‘Biological Big Data’, modern sequencing techniques provide allows us to investigate biomolecular evolution with unprecedented detail. Sophisticated statistical models can infer residue pair mutations resulting from spatial proximity. The introduction of predicted spatial adjacencies as constraints in biomolecular structure prediction workflows has transformed the field of protein and RNA structure prediction toward accuracies approaching the experimental resolution limit. Going beyond structure prediction, the same mathematical framework allows mimicking evolutionary fitness landscapes to infer signaling interactions, epistasis, or mutational landscapes.
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27
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Jing X, Dong Q, Lu R. RRCRank: a fusion method using rank strategy for residue-residue contact prediction. BMC Bioinformatics 2017; 18:390. [PMID: 28865433 PMCID: PMC5581475 DOI: 10.1186/s12859-017-1811-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 08/28/2017] [Indexed: 11/10/2022] Open
Abstract
Background In structural biology area, protein residue-residue contacts play a crucial role in protein structure prediction. Some researchers have found that the predicted residue-residue contacts could effectively constrain the conformational search space, which is significant for de novo protein structure prediction. In the last few decades, related researchers have developed various methods to predict residue-residue contacts, especially, significant performance has been achieved by using fusion methods in recent years. In this work, a novel fusion method based on rank strategy has been proposed to predict contacts. Unlike the traditional regression or classification strategies, the contact prediction task is regarded as a ranking task. First, two kinds of features are extracted from correlated mutations methods and ensemble machine-learning classifiers, and then the proposed method uses the learning-to-rank algorithm to predict contact probability of each residue pair. Results First, we perform two benchmark tests for the proposed fusion method (RRCRank) on CASP11 dataset and CASP12 dataset respectively. The test results show that the RRCRank method outperforms other well-developed methods, especially for medium and short range contacts. Second, in order to verify the superiority of ranking strategy, we predict contacts by using the traditional regression and classification strategies based on the same features as ranking strategy. Compared with these two traditional strategies, the proposed ranking strategy shows better performance for three contact types, in particular for long range contacts. Third, the proposed RRCRank has been compared with several state-of-the-art methods in CASP11 and CASP12. The results show that the RRCRank could achieve comparable prediction precisions and is better than three methods in most assessment metrics. Conclusions The learning-to-rank algorithm is introduced to develop a novel rank-based method for the residue-residue contact prediction of proteins, which achieves state-of-the-art performance based on the extensive assessment. Electronic supplementary material The online version of this article (10.1186/s12859-017-1811-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiaoyang Jing
- School of Computer Science, Fudan University, Shanghai, 200433, People's Republic of China
| | - Qiwen Dong
- School of Data Science and Engineering, East China Normal University, Shanghai, 200062, People's Republic of China.
| | - Ruqian Lu
- School of Computer Science, Fudan University, Shanghai, 200433, People's Republic of China
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28
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Skwark MJ, Croucher NJ, Puranen S, Chewapreecha C, Pesonen M, Xu YY, Turner P, Harris SR, Beres SB, Musser JM, Parkhill J, Bentley SD, Aurell E, Corander J. Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis. PLoS Genet 2017; 13:e1006508. [PMID: 28207813 PMCID: PMC5312804 DOI: 10.1371/journal.pgen.1006508] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 11/24/2016] [Indexed: 12/05/2022] Open
Abstract
Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae (pneumococcus) and Streptococcus pyogenes (group A Streptococcus). For pneumococcus we identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these antibiotic resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. The group A Streptococcus (GAS) data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome. Despite this, we were able to pinpoint two RNA pseudouridine synthases, which were each strongly linked to a separate set of loci across the chromosome, representing biologically plausible targets of co-selection. The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs, potentially arising from fitness selection interdependence reflecting underlying protein-protein interactions, or genes whose product activities contribute to the same phenotype. This discovery approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work. Epistatic interactions between polymorphisms in DNA are recognized as important drivers of evolution in numerous organisms. Study of epistasis in bacteria has been hampered by the lack of densely sampled population genomic data, suitable statistical models and inference algorithms sufficiently powered for extremely high-dimensional parameter spaces. We introduce the first model-based method for genome-wide epistasis analysis and use two of the largest available bacterial population genome data sets on Streptococcus pneumoniae (the pneumococcus) and Streptococcus pyogenes (group A Streptococcus) to demonstrate its potential for biological discovery. Our approach reveals interacting networks of resistance, virulence and core machinery genes in the pneumococcus, which highlights putative candidates for novel drug targets. We also discover a number of plausible targets of co-selection in S. pyogenes linked to RNA pseudouridine synthases. Our method significantly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work.
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Affiliation(s)
- Marcin J Skwark
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States of America
| | - Nicholas J Croucher
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Santeri Puranen
- Department of Computer Science, Aalto University, Espoo, Finland
| | | | - Maiju Pesonen
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Ying Ying Xu
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Paul Turner
- Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand.,Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Simon R Harris
- Pathogen Genomics, Wellcome Trust Sanger Institute, Cambridge, United Kingdom
| | - Stephen B Beres
- Center for Molecular and Translational Human Infectious Diseases Research, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute, and Houston Methodist Hospital, Houston, Texas, United States of America
| | - James M Musser
- Center for Molecular and Translational Human Infectious Diseases Research, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute, and Houston Methodist Hospital, Houston, Texas, United States of America.,Departments of Pathology and Laboratory Medicine and Microbiology and Immunology, Weill Cornell Medical College, New York, New York, United States of America
| | - Julian Parkhill
- Pathogen Genomics, Wellcome Trust Sanger Institute, Cambridge, United Kingdom
| | - Stephen D Bentley
- Pathogen Genomics, Wellcome Trust Sanger Institute, Cambridge, United Kingdom
| | - Erik Aurell
- Department of Computational Biology, KTH-Royal Institute of Technology, Stockholm, Sweden.,Departments of Applied Physics and Computer Science, Aalto University, Espoo, Finland.,Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China
| | - Jukka Corander
- Pathogen Genomics, Wellcome Trust Sanger Institute, Cambridge, United Kingdom.,Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.,Department of Biostatistics, University of Oslo, Oslo, Norway.,Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
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29
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Várnai C, Burkoff NS, Wild DL. Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs. PLoS One 2017; 12:e0169356. [PMID: 28166227 PMCID: PMC5293240 DOI: 10.1371/journal.pone.0169356] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 12/15/2016] [Indexed: 01/05/2023] Open
Abstract
Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.
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Affiliation(s)
- Csilla Várnai
- Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Nikolas S. Burkoff
- Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - David L. Wild
- Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, United Kingdom
- * E-mail:
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30
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Nshogozabahizi JC, Dench J, Aris-Brosou S. Widespread Historical Contingency in Influenza Viruses. Genetics 2017; 205:409-420. [PMID: 28049709 PMCID: PMC5223518 DOI: 10.1534/genetics.116.193979] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Accepted: 11/04/2016] [Indexed: 11/18/2022] Open
Abstract
In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular "disease-causing" mutations do not have the same outcome in all individuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, especially in fast-evolving organisms. Here, we present a novel statistical approach that replies on a model developed in ecology and that we adapt to analyze genetic data in fast-evolving systems such as the influenza A virus. We validate the approach using a two-pronged strategy: extensive simulations demonstrate a low-to-moderate sensitivity with excellent specificity and precision, while analyses of experimentally validated data recover known interactions, including in a eukaryotic system. We further evaluate the ability of our approach to detect correlated evolution during antigenic shifts or at the emergence of drug resistance. We show that in all cases, correlated evolution is prevalent in influenza A viruses, involving many pairs of sites linked together in chains; a hallmark of historical contingency. Strikingly, interacting sites are separated by large physical distances, which entails either long-range conformational changes or functional tradeoffs, for which we find support with the emergence of drug resistance. Our work paves a new way for the unbiased detection of epistasis in a wide range of organisms by performing whole-genome scans.
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Affiliation(s)
| | - Jonathan Dench
- Department of Biology, University of Ottawa, Ontario K1N 6N5, Canada
| | - Stéphane Aris-Brosou
- Department of Biology, University of Ottawa, Ontario K1N 6N5, Canada
- Department of Mathematics and Statistics, University of Ottawa, Ontario K1N 6N5, Canada
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31
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Cheng RR, Nordesjö O, Hayes RL, Levine H, Flores SC, Onuchic JN, Morcos F. Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes. Mol Biol Evol 2016; 33:3054-3064. [PMID: 27604223 PMCID: PMC5100047 DOI: 10.1093/molbev/msw188] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Two-component signaling (TCS) is the primary means by which bacteria sense and respond to the environment. TCS involves two partner proteins working in tandem, which interact to perform cellular functions whereas limiting interactions with non-partners (i.e., cross-talk). We construct a Potts model for TCS that can quantitatively predict how mutating amino acid identities affect the interaction between TCS partners and non-partners. The parameters of this model are inferred directly from protein sequence data. This approach drastically reduces the computational complexity of exploring the sequence-space of TCS proteins. As a stringent test, we compare its predictions to a recent comprehensive mutational study, which characterized the functionality of 204 mutational variants of the PhoQ kinase in Escherichia coli We find that our best predictions accurately reproduce the amino acid combinations found in experiment, which enable functional signaling with its partner PhoP. These predictions demonstrate the evolutionary pressure to preserve the interaction between TCS partners as well as prevent unwanted cross-talk. Further, we calculate the mutational change in the binding affinity between PhoQ and PhoP, providing an estimate to the amount of destabilization needed to disrupt TCS.
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Affiliation(s)
- R R Cheng
- Center for Theoretical Biological Physics, Rice University, Houston, TX
| | - O Nordesjö
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - R L Hayes
- Department of Biophysics, University of Michigan, Ann Arbor, MI
| | - H Levine
- Center for Theoretical Biological Physics, Rice University, Houston, TX.,Department of Bioengineering, Rice University, Houston, TX
| | - S C Flores
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - J N Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, TX .,Department of Physics and Astronomy, Rice University, Houston, TX.,Department of Chemistry, and Biosciences, Rice University, Houston, TX
| | - F Morcos
- Department of Biological Sciences and Center for Systems Biology, University of Texas at Dallas, Dallas, TX
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32
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Abstract
Structural domains are believed to be modules within proteins that can fold and function independently. Some proteins show tandem repetitions of apparent modular structure that do not fold independently, but rather co-operate in stabilizing structural forms that comprise several repeat-units. For many natural repeat-proteins, it has been shown that weak energetic links between repeats lead to the breakdown of co-operativity and the appearance of folding sub-domains within an apparently regular repeat array. The quasi-1D architecture of repeat-proteins is crucial in detailing how the local energetic balances can modulate the folding dynamics of these proteins, which can be related to the physiological behaviour of these ubiquitous biological systems.
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33
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Franceus J, Verhaeghe T, Desmet T. Correlated positions in protein evolution and engineering. J Ind Microbiol Biotechnol 2016; 44:687-695. [PMID: 27514664 DOI: 10.1007/s10295-016-1811-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Accepted: 07/30/2016] [Indexed: 12/22/2022]
Abstract
Statistical analysis of a protein multiple sequence alignment can reveal groups of positions that undergo interdependent mutations throughout evolution. At these so-called correlated positions, only certain combinations of amino acids appear to be viable for maintaining proper folding, stability, catalytic activity or specificity. Therefore, it is often speculated that they could be interesting guides for semi-rational protein engineering purposes. Because they are a fingerprint from protein evolution, their analysis may provide valuable insight into a protein's structure or function and furthermore, they may also be suitable target positions for mutagenesis. Unfortunately, little is currently known about the properties of these correlation networks and how they should be used in practice. This review summarises the recent findings, opportunities and pitfalls of the concept.
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Affiliation(s)
- Jorick Franceus
- Department of Biochemical and Microbial Technology, Centre for Industrial Biotechnology and Biocatalysis, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
| | - Tom Verhaeghe
- Department of Biochemical and Microbial Technology, Centre for Industrial Biotechnology and Biocatalysis, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
| | - Tom Desmet
- Department of Biochemical and Microbial Technology, Centre for Industrial Biotechnology and Biocatalysis, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.
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34
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A novel algorithm for detecting multiple covariance and clustering of biological sequences. Sci Rep 2016; 6:30425. [PMID: 27451921 PMCID: PMC4958985 DOI: 10.1038/srep30425] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Accepted: 07/05/2016] [Indexed: 12/14/2022] Open
Abstract
Single genetic mutations are always followed by a set of compensatory mutations. Thus, multiple changes commonly occur in biological sequences and play crucial roles in maintaining conformational and functional stability. Although many methods are available to detect single mutations or covariant pairs, detecting non-synchronous multiple changes at different sites in sequences remains challenging. Here, we develop a novel algorithm, named Fastcov, to identify multiple correlated changes in biological sequences using an independent pair model followed by a tandem model of site-residue elements based on inter-restriction thinking. Fastcov performed exceptionally well at harvesting co-pairs and detecting multiple covariant patterns. By 10-fold cross-validation using datasets of different scales, the characteristic patterns successfully classified the sequences into target groups with an accuracy of greater than 98%. Moreover, we demonstrated that the multiple covariant patterns represent co-evolutionary modes corresponding to the phylogenetic tree, and provide a new understanding of protein structural stability. In contrast to other methods, Fastcov provides not only a reliable and effective approach to identify covariant pairs but also more powerful functions, including multiple covariance detection and sequence classification, that are most useful for studying the point and compensatory mutations caused by natural selection, drug induction, environmental pressure, etc.
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35
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Shepherd GM, Singer MS, Greer CA. ■ REVIEW : Olfactory Receptors: A Large Gene Family with Broad Affinities and Multiple Functions. Neuroscientist 2016. [DOI: 10.1177/107385849600200512] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Five years have passed since the first cloning and sequencing of a large family of G protein-coupled receptors from the olfactory epithelium. These receptors are believed to be the initial sites of odor transduction. Although direct experimental evidence concerning the properties of these molecules is still limited, a variety of studies has provided fascinating insights into a range of possible functions, extending beyond olfactory transduction to include functions as diverse as sperm navigation and neural and cardiac development. To serve these functions, the olfactory receptors appear to express interesting adaptations of the basic seven transmembrane domain structures found in the neurotransmitter members of the G protein-coupled receptor superfamily. We review here this evidence and propose hypotheses for the molecular mechanisms underlying several distinct functions for this receptor family as guides for future experimental testing. NEUROSCIENTIST 2:262-271, 1996
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Affiliation(s)
- Gordon M. Shepherd
- Sections of Neurobiology and Neurosurgery Yale University
School of Medicine New Haven, Connecticut
| | - Michael S. Singer
- Sections of Neurobiology and Neurosurgery Yale University
School of Medicine New Haven, Connecticut
| | - Charles A. Greer
- Sections of Neurobiology and Neurosurgery Yale University
School of Medicine New Haven, Connecticut
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36
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Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models. PLoS Comput Biol 2016; 12:e1004889. [PMID: 27177270 PMCID: PMC4866778 DOI: 10.1371/journal.pcbi.1004889] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2015] [Accepted: 03/30/2016] [Indexed: 12/05/2022] Open
Abstract
Inverse statistical approaches to determine protein structure and function from Multiple Sequence Alignments (MSA) are emerging as powerful tools in computational biology. However the underlying assumptions of the relationship between the inferred effective Potts Hamiltonian and real protein structure and energetics remain untested so far. Here we use lattice protein model (LP) to benchmark those inverse statistical approaches. We build MSA of highly stable sequences in target LP structures, and infer the effective pairwise Potts Hamiltonians from those MSA. We find that inferred Potts Hamiltonians reproduce many important aspects of ‘true’ LP structures and energetics. Careful analysis reveals that effective pairwise couplings in inferred Potts Hamiltonians depend not only on the energetics of the native structure but also on competing folds; in particular, the coupling values reflect both positive design (stabilization of native conformation) and negative design (destabilization of competing folds). In addition to providing detailed structural information, the inferred Potts models used as protein Hamiltonian for design of new sequences are able to generate with high probability completely new sequences with the desired folds, which is not possible using independent-site models. Those are remarkable results as the effective LP Hamiltonians used to generate MSA are not simple pairwise models due to the competition between the folds. Our findings elucidate the reasons for the success of inverse approaches to the modelling of proteins from sequence data, and their limitations. Inverse statistical approaches, modeling pairwise correlations between amino acids in the sequences of homologous proteins across many different organisms, can successfully extract protein structure (contact) information. Here, we benchmark those statistical approaches on exactly solvable models of proteins, folding on a 3D lattice, to assess the reasons underlying their success and their limitations. We show that the inferred parameters (effective pairwise interactions) of the statistical models have clear and quantitative interpretations in terms of positive (favoring the native fold) and negative (disfavoring competing folds) protein sequence design. New sequences randomly drawn from the statistical models are likely to fold into the native structures when effective pairwise interactions are accurately inferred, a performance which cannot be achieved with independent-site models.
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37
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Baker FN, Porollo A. CoeViz: a web-based tool for coevolution analysis of protein residues. BMC Bioinformatics 2016; 17:119. [PMID: 26956673 PMCID: PMC4782369 DOI: 10.1186/s12859-016-0975-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 03/01/2016] [Indexed: 11/30/2022] Open
Abstract
Background Proteins generally perform their function in a folded state. Residues forming an active site, whether it is a catalytic center or interaction interface, are frequently distant in a protein sequence. Hence, traditional sequence-based prediction methods focusing on a single residue (or a short window of residues) at a time may have difficulties in identifying and clustering the residues constituting a functional site, especially when a protein has multiple functions. Evolutionary information encoded in multiple sequence alignments is known to greatly improve sequence-based predictions. Identification of coevolving residues further advances the protein structure and function annotation by revealing cooperative pairs and higher order groupings of residues. Results We present a new web-based tool (CoeViz) that provides a versatile analysis and visualization of pairwise coevolution of amino acid residues. The tool computes three covariance metrics: mutual information, chi-square statistic, Pearson correlation, and one conservation metric: joint Shannon entropy. Implemented adjustments of covariance scores include phylogeny correction, corrections for sequence dissimilarity and alignment gaps, and the average product correction. Visualization of residue relationships is enhanced by hierarchical cluster trees, heat maps, circular diagrams, and the residue highlighting in protein sequence and 3D structure. Unlike other existing tools, CoeViz is not limited to analyzing conserved domains or protein families and can process long, unstructured and multi-domain proteins thousands of residues long. Two examples are provided to illustrate the use of the tool for identification of residues (1) involved in enzymatic function, (2) forming short linear functional motifs, and (3) constituting a structural domain. Conclusions CoeViz represents a practical resource for a quick sequence-based protein annotation for molecular biologists, e.g., for identifying putative functional clusters of residues and structural domains. CoeViz also can serve computational biologists as a resource of coevolution matrices, e.g., for developing machine learning-based prediction models. The presented tool is integrated in the POLYVIEW-2D server (http://polyview.cchmc.org/) and available from resulting pages of POLYVIEW-2D.
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Affiliation(s)
- Frazier N Baker
- Department of Electrical Engineering and Computing Systems, University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH, 45221, USA. .,Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
| | - Aleksey Porollo
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA. .,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
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38
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Slama P. Identification of family determining residues in Jumonji-C lysine demethylases: A sequence-based, family wide classification. Proteins 2016; 84:397-407. [PMID: 26757344 PMCID: PMC4755873 DOI: 10.1002/prot.24986] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Revised: 12/31/2015] [Accepted: 01/04/2016] [Indexed: 12/12/2022]
Abstract
Histone post-translational modifications play a critical role in the regulation of gene expression. Methylation of lysines at N-terminal tails of histones has been shown to be involved in such regulation. While this modification was long considered to be irreversible, two different classes of enzymes capable of carrying out the demethylation of histone lysines were recently identified: the oxidases, such as LSD1, and the oxygenases (JmjC-containing). Here, a family-wide analysis of the second of these classes is proposed, with over 300 proteins studied at the sequence level. We show that a correlated evolution analysis yields some position/residue pairs which are critical at comparing JmjC sequences and enables the classification of JmjC domains into five families. A few positions appear more frequently among conditions, such as positions 23 (directly C-terminal to the second iron ligand), 24, 252 and 253 (directly N-terminal to a conserved Asn). Implications of family conditions are studied in detail on PHF2, revealing the meaningfulness of the sequence-derived conditions at the structural level. These results should help obtain insights on the diversity of JmjC-containing proteins solely by considering some of the amino acids present in their JmjC domain.
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Affiliation(s)
- Patrick Slama
- Center for Imaging Science, Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland
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39
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Baker MAB, Hynson RMG, Ganuelas LA, Mohammadi NS, Liew CW, Rey AA, Duff AP, Whitten AE, Jeffries CM, Delalez NJ, Morimoto YV, Stock D, Armitage JP, Turberfield AJ, Namba K, Berry RM, Lee LK. Domain-swap polymerization drives the self-assembly of the bacterial flagellar motor. Nat Struct Mol Biol 2016; 23:197-203. [PMID: 26854663 DOI: 10.1038/nsmb.3172] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 01/13/2016] [Indexed: 01/02/2023]
Abstract
Large protein complexes assemble spontaneously, yet their subunits do not prematurely form unwanted aggregates. This paradox is epitomized in the bacterial flagellar motor, a sophisticated rotary motor and sensory switch consisting of hundreds of subunits. Here we demonstrate that Escherichia coli FliG, one of the earliest-assembling flagellar motor proteins, forms ordered ring structures via domain-swap polymerization, which in other proteins has been associated with uncontrolled and deleterious protein aggregation. Solution structural data, in combination with in vivo biochemical cross-linking experiments and evolutionary covariance analysis, revealed that FliG exists predominantly as a monomer in solution but only as domain-swapped polymers in assembled flagellar motors. We propose a general structural and thermodynamic model for self-assembly, in which a structural template controls assembly and shapes polymer formation into rings.
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Affiliation(s)
- Matthew A B Baker
- Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia.,European Molecular Biology Laboratory Australia Node for Single Molecule Science, School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Robert M G Hynson
- Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
| | - Lorraine A Ganuelas
- Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
| | - Nasim Shah Mohammadi
- Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
| | - Chu Wai Liew
- Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
| | - Anthony A Rey
- Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
| | - Anthony P Duff
- Australian Nuclear and Science Technology Organisation, Lucas Heights, New South Wales, Australia
| | - Andrew E Whitten
- Australian Nuclear and Science Technology Organisation, Lucas Heights, New South Wales, Australia
| | - Cy M Jeffries
- Australian Nuclear and Science Technology Organisation, Lucas Heights, New South Wales, Australia
| | | | - Yusuke V Morimoto
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
| | - Daniela Stock
- Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
| | | | | | - Keiichi Namba
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
| | | | - Lawrence K Lee
- Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia.,European Molecular Biology Laboratory Australia Node for Single Molecule Science, School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
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40
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Schwarz RF, Tamuri AU, Kultys M, King J, Godwin J, Florescu AM, Schultz J, Goldman N. ALVIS: interactive non-aggregative visualization and explorative analysis of multiple sequence alignments. Nucleic Acids Res 2016; 44:e77. [PMID: 26819408 PMCID: PMC4856975 DOI: 10.1093/nar/gkw022] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Accepted: 01/08/2016] [Indexed: 12/19/2022] Open
Abstract
Sequence Logos and its variants are the most commonly used method for visualization of multiple sequence alignments (MSAs) and sequence motifs. They provide consensus-based summaries of the sequences in the alignment. Consequently, individual sequences cannot be identified in the visualization and covariant sites are not easily discernible. We recently proposed Sequence Bundles, a motif visualization technique that maintains a one-to-one relationship between sequences and their graphical representation and visualizes covariant sites. We here present Alvis, an open-source platform for the joint explorative analysis of MSAs and phylogenetic trees, employing Sequence Bundles as its main visualization method. Alvis combines the power of the visualization method with an interactive toolkit allowing detection of covariant sites, annotation of trees with synapomorphies and homoplasies, and motif detection. It also offers numerical analysis functionality, such as dimension reduction and classification. Alvis is user-friendly, highly customizable and can export results in publication-quality figures. It is available as a full-featured standalone version (http://www.bitbucket.org/rfs/alvis) and its Sequence Bundles visualization module is further available as a web application (http://science-practice.com/projects/sequence-bundles).
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Affiliation(s)
- Roland F Schwarz
- European Molecular Biology Laboratory-European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Asif U Tamuri
- European Molecular Biology Laboratory-European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Marek Kultys
- Science Practice, 83-85 Paul Street, London, EC2A 4NQ, UK
| | - James King
- Science Practice, 83-85 Paul Street, London, EC2A 4NQ, UK
| | - James Godwin
- Science Practice, 83-85 Paul Street, London, EC2A 4NQ, UK
| | - Ana M Florescu
- Science Practice, 83-85 Paul Street, London, EC2A 4NQ, UK
| | - Jörg Schultz
- Center for Computational and Theoretical Biology and Department of Bioinformatics, University of Würzburg, Biocenter, Am Hubland, 97074 Würzburg, Germany
| | - Nick Goldman
- European Molecular Biology Laboratory-European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
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41
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Braun T, Koehler Leman J, Lange OF. Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction. PLoS Comput Biol 2015; 11:e1004661. [PMID: 26713437 PMCID: PMC4694711 DOI: 10.1371/journal.pcbi.1004661] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2015] [Accepted: 11/17/2015] [Indexed: 12/18/2022] Open
Abstract
Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs. Recently, a breakthrough has been achieved in modeling the atomic 3D structures of proteins from their sequence alone without requiring any experimental work on the protein itself. To achieve this goal, a database of evolutionary related sequences is analyzed to find co-evolving residues, giving insight into which residues are in close proximity to each other. These residue-residue contacts can help to drive a computer simulation with an atomic-scale physical model of the protein structure from a random starting conformation to a native-like 3D conformation. Although much effort is being put into the improvement of residue-residue contact predictions, their accuracy will always be limited. Therefore, structure prediction protocols with a high tolerance against incorrect distance restraints are needed. Here, we present a structure prediction protocol that combines evolutionary information with the iterative sampling approach of the molecular modeling suite Rosetta, called RASREC. RASREC has been shown to converge faster to near-native models and to be more robust against incorrect distance restraints than standard prediction protocols. It is therefore perfectly suited for restraints obtained from predicted residue-residue contacts with limited accuracy. We show that our protocol outperforms other currently published structure prediction methods and is able to achieve accurate structures, even if the accuracy of predicted contacts is low.
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Affiliation(s)
- Tatjana Braun
- Biomolecular NMR and Munich Center for Integrated Protein Science, Department Chemie, Technische Universität München, Garching, Germany
- * E-mail:
| | - Julia Koehler Leman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Oliver F. Lange
- Biomolecular NMR and Munich Center for Integrated Protein Science, Department Chemie, Technische Universität München, Garching, Germany
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42
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From residue coevolution to protein conformational ensembles and functional dynamics. Proc Natl Acad Sci U S A 2015; 112:13567-72. [PMID: 26487681 DOI: 10.1073/pnas.1508584112] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The analysis of evolutionary amino acid correlations has recently attracted a surge of renewed interest, also due to their successful use in de novo protein native structure prediction. However, many aspects of protein function, such as substrate binding and product release in enzymatic activity, can be fully understood only in terms of an equilibrium ensemble of alternative structures, rather than a single static structure. In this paper we combine coevolutionary data and molecular dynamics simulations to study protein conformational heterogeneity. To that end, we adapt the Boltzmann-learning algorithm to the analysis of homologous protein sequences and develop a coarse-grained protein model specifically tailored to convert the resulting contact predictions to a protein structural ensemble. By means of exhaustive sampling simulations, we analyze the set of conformations that are consistent with the observed residue correlations for a set of representative protein domains, showing that (i) the most representative structure is consistent with the experimental fold and (ii) the various regions of the sequence display different stability, related to multiple biologically relevant conformations and to the cooperativity of the coevolving pairs. Moreover, we show that the proposed protocol is able to reproduce the essential features of a protein folding mechanism as well as to account for regions involved in conformational transitions through the correct sampling of the involved conformers.
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43
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Stein RR, Marks DS, Sander C. Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models. PLoS Comput Biol 2015. [PMID: 26225866 PMCID: PMC4520494 DOI: 10.1371/journal.pcbi.1004182] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene–gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.
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Affiliation(s)
- Richard R. Stein
- Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
- * E-mail: (RRS); (CS)
| | - Debora S. Marks
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Chris Sander
- Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
- * E-mail: (RRS); (CS)
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44
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Espada R, Parra RG, Mora T, Walczak AM, Ferreiro DU. Capturing coevolutionary signals inrepeat proteins. BMC Bioinformatics 2015; 16:207. [PMID: 26134293 PMCID: PMC4489039 DOI: 10.1186/s12859-015-0648-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Accepted: 06/16/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The analysis of correlations of amino acid occurrences in globular domains has led to the development of statistical tools that can identify native contacts - portions of the chains that come to close distance in folded structural ensembles. Here we introduce a direct coupling analysis for repeat proteins - natural systems for which the identification of folding domains remains challenging. RESULTS We show that the inherent translational symmetry of repeat protein sequences introduces a strong bias in the pair correlations at precisely the length scale of the repeat-unit. Equalizing for this bias in an objective way reveals true co-evolutionary signals from which local native contacts can be identified. Importantly, parameter values obtained for all other interactions are not significantly affected by the equalization. We quantify the robustness of the procedure and assign confidence levels to the interactions, identifying the minimum number of sequences needed to extract evolutionary information in several repeat protein families. CONCLUSIONS The overall procedure can be used to reconstruct the interactions at distances larger than repeat-pairs, identifying the characteristics of the strongest couplings in each family, and can be applied to any system that appears translationally symmetric.
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Affiliation(s)
- Rocío Espada
- Protein Physiology Lab, Dep de Química Biológica, Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN, Buenos Aires, Argentina.,Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - R Gonzalo Parra
- Protein Physiology Lab, Dep de Química Biológica, Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN, Buenos Aires, Argentina
| | - Thierry Mora
- Laboratoire de physique statistique, CNRS, UPMC and École normale supérieure, 24 rue Lhomond, Paris, 75005, France
| | | | - Diego U Ferreiro
- Protein Physiology Lab, Dep de Química Biológica, Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN, Buenos Aires, Argentina
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45
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Malinverni D, Marsili S, Barducci A, De Los Rios P. Large-Scale Conformational Transitions and Dimerization Are Encoded in the Amino-Acid Sequences of Hsp70 Chaperones. PLoS Comput Biol 2015; 11:e1004262. [PMID: 26046683 PMCID: PMC4457872 DOI: 10.1371/journal.pcbi.1004262] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Accepted: 04/01/2015] [Indexed: 12/30/2022] Open
Abstract
Hsp70s are a class of ubiquitous and highly conserved molecular chaperones playing a central role in the regulation of proteostasis in the cell. Hsp70s assist a myriad of cellular processes by binding unfolded or misfolded substrates during a complex biochemical cycle involving large-scale structural rearrangements. Here we show that an analysis of coevolution at the residue level fully captures the characteristic large-scale conformational transitions of this protein family, and predicts an evolutionary conserved–and thus functional–homo-dimeric arrangement. Furthermore, we highlight that the features encoding the Hsp70 dimer are more conserved in bacterial than in eukaryotic sequences, suggesting that the known Hsp70/Hsp110 hetero-dimer is a eukaryotic specialization built on a pre-existing template. Molecular chaperones are a class of proteins that are crucial for the correct functioning of cells. They play central housekeeping roles in the normal cell cycle, and are major actors of the protection system of the cell against cell stress conditions. In this study, we apply statistical inference methods to analyse the structure and function of the Hsp70 molecular chaperone, one of the main members of chaperones. We use the correlated amino acid coevolutions in protein sequences to identify directly interacting amino acids. Our results show that coevolutions capture an appreciable fraction of native contacts throughout the protein. Furthermore, amino acid coevolution predicts previously hypothesized functional dimer interactions between Hsp70s, thus giving a theoretical contribution to this debate.
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Affiliation(s)
- Duccio Malinverni
- Laboratoire de Biophysique Statistique, École Polytechnique Fédérale de Lausanne, Faculté de Sciences de Base, Lausanne, Switzerland
| | - Simone Marsili
- Structural Computational Biology Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Alessandro Barducci
- Laboratoire de Biophysique Statistique, École Polytechnique Fédérale de Lausanne, Faculté de Sciences de Base, Lausanne, Switzerland
| | - Paolo De Los Rios
- Laboratoire de Biophysique Statistique, École Polytechnique Fédérale de Lausanne, Faculté de Sciences de Base, Lausanne, Switzerland
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46
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All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences. Proc Natl Acad Sci U S A 2015; 112:5413-8. [PMID: 25858953 DOI: 10.1073/pnas.1419956112] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Transmembrane β-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting β-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent β-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand-strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of β-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.
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47
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Bywater RP. Prediction of protein structural features from sequence data based on Shannon entropy and Kolmogorov complexity. PLoS One 2015; 10:e0119306. [PMID: 25856073 PMCID: PMC4391790 DOI: 10.1371/journal.pone.0119306] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2013] [Accepted: 01/29/2015] [Indexed: 11/21/2022] Open
Abstract
While the genome for a given organism stores the information necessary for the organism to function and flourish it is the proteins that are encoded by the genome that perhaps more than anything else characterize the phenotype for that organism. It is therefore not surprising that one of the many approaches to understanding and predicting protein folding and properties has come from genomics and more specifically from multiple sequence alignments. In this work I explore ways in which data derived from sequence alignment data can be used to investigate in a predictive way three different aspects of protein structure: secondary structures, inter-residue contacts and the dynamics of switching between different states of the protein. In particular the use of Kolmogorov complexity has identified a novel pathway towards achieving these goals.
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48
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Zhang J, Ruhlman TA, Sabir J, Blazier JC, Jansen RK. Coordinated rates of evolution between interacting plastid and nuclear genes in Geraniaceae. THE PLANT CELL 2015; 27:563-73. [PMID: 25724640 PMCID: PMC4558654 DOI: 10.1105/tpc.114.134353] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Revised: 01/28/2015] [Accepted: 02/12/2015] [Indexed: 05/08/2023]
Abstract
Although gene coevolution has been widely observed within individuals and between different organisms, rarely has this phenomenon been investigated within a phylogenetic framework. The Geraniaceae is an attractive system in which to study plastid-nuclear genome coevolution due to the highly elevated evolutionary rates in plastid genomes. In plants, the plastid-encoded RNA polymerase (PEP) is a protein complex composed of subunits encoded by both plastid (rpoA, rpoB, rpoC1, and rpoC2) and nuclear genes (sig1-6). We used transcriptome and genomic data for 27 species of Geraniales in a systematic evaluation of coevolution between genes encoding subunits of the PEP holoenzyme. We detected strong correlations of dN (nonsynonymous substitutions) but not dS (synonymous substitutions) within rpoB/sig1 and rpoC2/sig2, but not for other plastid/nuclear gene pairs, and identified the correlation of dN/dS ratio between rpoB/C1/C2 and sig1/5/6, rpoC1/C2 and sig2, and rpoB/C2 and sig3 genes. Correlated rates between interacting plastid and nuclear sequences across the Geraniales could result from plastid-nuclear genome coevolution. Analyses of coevolved amino acid positions suggest that structurally mediated coevolution is not the major driver of plastid-nuclear coevolution. The detection of strong correlation of evolutionary rates between SIG and RNAP genes suggests a plausible explanation for plastome-genome incompatibility in Geraniaceae.
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Affiliation(s)
- Jin Zhang
- Department of Integrative Biology, University of Texas, Austin, Texas 78712
| | - Tracey A Ruhlman
- Department of Integrative Biology, University of Texas, Austin, Texas 78712
| | - Jamal Sabir
- Department of Biological Sciences, Biotechnology Research Group, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - J Chris Blazier
- Department of Integrative Biology, University of Texas, Austin, Texas 78712
| | - Robert K Jansen
- Department of Integrative Biology, University of Texas, Austin, Texas 78712 Department of Biological Sciences, Biotechnology Research Group, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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49
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Singh S, Mandlik V, Shinde S. Molecular dynamics simulations and statistical coupling analysis of GPI12 in L. major: functional co-evolution and conservedness reveals potential drug–target sites. MOLECULAR BIOSYSTEMS 2015; 11:958-68. [DOI: 10.1039/c4mb00649f] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
GPI12 represents an important enzyme in the GPI biosynthetic pathway of several parasites like ‘Leishmania’.
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Affiliation(s)
- Shailza Singh
- National Centre for Cell Science
- NCCS Complex
- Pune 411007
- India
| | | | - Sonali Shinde
- National Centre for Cell Science
- NCCS Complex
- Pune 411007
- India
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50
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Tian P, Boomsma W, Wang Y, Otzen DE, Jensen MH, Lindorff-Larsen K. Structure of a Functional Amyloid Protein Subunit Computed Using Sequence Variation. J Am Chem Soc 2014; 137:22-5. [DOI: 10.1021/ja5093634] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Pengfei Tian
- Niels
Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
| | - Wouter Boomsma
- Structural
Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5 DK-2200 Copenhagen N, Denmark
| | - Yong Wang
- Structural
Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5 DK-2200 Copenhagen N, Denmark
| | - Daniel E. Otzen
- Interdisciplinary
Nanoscience Center (iNANO), Centre for Insoluble Protein Structures
(inSPIN), Department of Molecular Biology and Genetics, Aarhus University, Gustav Wieds Vej 14, 8000 Aarhus C, Denmark
| | - Mogens H. Jensen
- Niels
Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Structural
Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5 DK-2200 Copenhagen N, Denmark
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