1
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Lian X, Praljak N, Subramanian SK, Wasinger S, Ranganathan R, Ferguson AL. Deep-learning-based design of synthetic orthologs of SH3 signaling domains. Cell Syst 2024:S2405-4712(24)00204-7. [PMID: 39106868 DOI: 10.1016/j.cels.2024.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/12/2023] [Accepted: 07/22/2024] [Indexed: 08/09/2024]
Abstract
Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can learn specialized functional constraints that control fitness in specific biological contexts. Here, we examine the ability of generative models to produce synthetic versions of Src-homology 3 (SH3) domains that mediate signaling in the Sho1 osmotic stress response pathway of yeast. We show that a variational autoencoder (VAE) model produces artificial sequences that experimentally recapitulate the function of natural SH3 domains. More generally, the model organizes all fungal SH3 domains such that locality in the model latent space (but not simply locality in sequence space) enriches the design of synthetic orthologs and exposes non-obvious amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of generative models to design ortholog-like functions in vivo opens new avenues for engineering protein function in specific cellular contexts and environments.
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Affiliation(s)
- Xinran Lian
- Department of Chemistry, University of Chicago, Chicago, IL 60637, USA
| | - Nikša Praljak
- Graduate Program in Biophysical Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Subu K Subramanian
- Department of Molecular and Cell Biology, California Institute for Quantitative Biosciences (QB3), and Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Sarah Wasinger
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Rama Ranganathan
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA; Center for Physics of Evolving Systems and Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL 60637, USA.
| | - Andrew L Ferguson
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
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2
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Zhang H, Bull RA, Quadeer AA, McKay MR. HCV E1 influences the fitness landscape of E2 and may enhance escape from E2-specific antibodies. Virus Evol 2023; 9:vead068. [PMID: 38107333 PMCID: PMC10722114 DOI: 10.1093/ve/vead068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/27/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023] Open
Abstract
The Hepatitis C virus (HCV) envelope glycoprotein E1 forms a non-covalent heterodimer with E2, the main target of neutralizing antibodies. How E1-E2 interactions influence viral fitness and contribute to resistance to E2-specific antibodies remain largely unknown. We investigate this problem using a combination of fitness landscape and evolutionary modeling. Our analysis indicates that E1 and E2 proteins collectively mediate viral fitness and suggests that fitness-compensating E1 mutations may accelerate escape from E2-targeting antibodies. Our analysis also identifies a set of E2-specific human monoclonal antibodies that are predicted to be especially resilient to escape via genetic variation in both E1 and E2, providing directions for robust HCV vaccine development.
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Affiliation(s)
- Hang Zhang
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, SAR, China
| | - Rowena A Bull
- School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- The Kirby Institute for Infection and Immunity, Sydney, NSW 2052, Australia
| | - Ahmed Abdul Quadeer
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, SAR, China
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia
| | - Matthew R McKay
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
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3
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Zhang H, Quadeer AA, McKay MR. Direct-acting antiviral resistance of Hepatitis C virus is promoted by epistasis. Nat Commun 2023; 14:7457. [PMID: 37978179 PMCID: PMC10656532 DOI: 10.1038/s41467-023-42550-6] [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: 01/17/2023] [Accepted: 10/13/2023] [Indexed: 11/19/2023] Open
Abstract
Direct-acting antiviral agents (DAAs) provide efficacious therapeutic treatments for chronic Hepatitis C virus (HCV) infection. However, emergence of drug resistance mutations (DRMs) can greatly affect treatment outcomes and impede virological cure. While multiple DRMs have been observed for all currently used DAAs, the evolutionary determinants of such mutations are not currently well understood. Here, by considering DAAs targeting the nonstructural 3 (NS3) protein of HCV, we present results suggesting that epistasis plays an important role in the evolution of DRMs. Employing a sequence-based fitness landscape model whose predictions correlate highly with experimental data, we identify specific DRMs that are associated with strong epistatic interactions, and these are found to be enriched in multiple NS3-specific DAAs. Evolutionary modelling further supports that the identified DRMs involve compensatory mutational interactions that facilitate relatively easy escape from drug-induced selection pressures. Our results indicate that accounting for epistasis is important for designing future HCV NS3-targeting DAAs.
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Affiliation(s)
- Hang Zhang
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
| | - Ahmed Abdul Quadeer
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China.
| | - Matthew R McKay
- Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, Australia.
- Department of Microbiology and Immunology, University of Melbourne, at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
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4
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Evolutionary modeling reveals enhanced mutational flexibility of HCV subtype 1b compared with 1a. iScience 2022; 25:103569. [PMID: 34988406 PMCID: PMC8704487 DOI: 10.1016/j.isci.2021.103569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 11/19/2021] [Accepted: 12/02/2021] [Indexed: 11/24/2022] Open
Abstract
Hepatitis C virus (HCV) is a leading cause of liver-associated disease and liver cancer. Of the major HCV subtypes, patients infected with subtype 1b have been associated with having a higher risk of developing chronic infection and hepatocellular carcinoma. However, underlying reasons for this increased disease severity remain unknown. Here, we provide an evolutionary rationale, based on a comparative study of fitness landscape and in-host evolutionary models of the E2 glycoprotein of HCV subtypes 1a and 1b. Our analysis demonstrates that a higher chronicity rate of 1b may be attributed to lower fitness constraints, enabling 1b viruses to more easily escape antibody responses. More generally, our results suggest that differences in evolutionary constraints between HCV subtypes may be an important factor in mediating distinct disease outcomes. Our analysis also identifies antibodies that appear escape-resistant against both subtypes 1a and 1b, providing directions for designing HCV vaccines having cross-subtype protection. Comparative analysis of the fitness landscapes of HCV subtypes 1a and 1b Subtype 1b evolution is subject to less constraints than 1a Subtype 1b appears to evade antibodies more easily compared with 1a Antibodies are identified that are difficult to escape for both subtypes 1a and 1b
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5
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Echeverría N, Comas V, Aldunate F, Perbolianachis P, Moreno P, Cristina J. In the era of rapid mRNA-based vaccines: Why is there no effective hepatitis C virus vaccine yet? World J Hepatol 2021; 13:1234-1268. [PMID: 34786164 PMCID: PMC8568586 DOI: 10.4254/wjh.v13.i10.1234] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/14/2021] [Accepted: 09/10/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatitis C virus (HCV) is responsible for no less than 71 million people chronically infected and is one of the most frequent indications for liver transplantation worldwide. Despite direct-acting antiviral therapies fuel optimism in controlling HCV infections, there are several obstacles regarding treatment accessibility and reinfection continues to remain a possibility. Indeed, the majority of new HCV infections in developed countries occur in people who inject drugs and are more plausible to get reinfected. To achieve global epidemic control of this virus the development of an effective prophylactic or therapeutic vaccine becomes a must. The coronavirus disease 19 (COVID-19) pandemic led to auspicious vaccine development against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus, which has renewed interest on fighting HCV epidemic with vaccination. The aim of this review is to highlight the current situation of HCV vaccine candidates designed to prevent and/or to reduce HCV infectious cases and their complications. We will emphasize on some of the crossroads encountered during vaccine development against this insidious virus, together with some key aspects of HCV immunology which have, so far, hampered the progress in this area. The main focus will be on nucleic acid-based as well as recombinant viral vector-based vaccine candidates as the most novel vaccine approaches, some of which have been recently and successfully employed for SARS-CoV-2 vaccines. Finally, some ideas will be presented on which methods to explore for the design of live-attenuated vaccines against HCV.
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Affiliation(s)
- Natalia Echeverría
- Laboratorio de Virología Molecular, Centro de Investigaciones Nucleares, Facultad de Ciencias, Universidad de la República, Montevideo 11400, Uruguay
| | - Victoria Comas
- Departamento de Desarrollo Biotecnológico, Instituto de Higiene, Facultad de Medicina, Universidad de la República, Montevideo 11600, Uruguay
| | - Fabián Aldunate
- Laboratorio de Virología Molecular, Centro de Investigaciones Nucleares, Facultad de Ciencias, Universidad de la República, Montevideo 11400, Uruguay
| | - Paula Perbolianachis
- Laboratorio de Virología Molecular, Centro de Investigaciones Nucleares, Facultad de Ciencias, Universidad de la República, Montevideo 11400, Uruguay
| | - Pilar Moreno
- Laboratorio de Virología Molecular, Centro de Investigaciones Nucleares, Facultad de Ciencias, Universidad de la República, Montevideo 11400, Uruguay
| | - Juan Cristina
- Laboratorio de Virología Molecular, Centro de Investigaciones Nucleares, Facultad de Ciencias, Universidad de la República, Montevideo 11400, Uruguay.
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6
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Ferguson AL, Ranganathan R. 100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design. ACS Macro Lett 2021; 10:327-340. [PMID: 35549066 DOI: 10.1021/acsmacrolett.0c00885] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The design of synthetic proteins with the desired function is a long-standing goal in biomolecular science, with broad applications in biochemical engineering, agriculture, medicine, and public health. Rational de novo design and experimental directed evolution have achieved remarkable successes but are challenged by the requirement to find functional "needles" in the vast "haystack" of protein sequence space. Data-driven models for fitness landscapes provide a predictive map between protein sequence and function and can prospectively identify functional candidates for experimental testing to greatly improve the efficiency of this search. This Viewpoint reviews the applications of machine learning and, in particular, deep learning as part of data-driven protein engineering platforms. We highlight recent successes, review promising computational methodologies, and provide an outlook on future challenges and opportunities. The article is written for a broad audience comprising both polymer and protein scientists and computer and data scientists interested in an up-to-date review of recent innovations and opportunities in this rapidly evolving field.
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Affiliation(s)
- Andrew L. Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Rama Ranganathan
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
- Center for Physics of Evolving Systems, University of Chicago, Chicago, Illinois 60637, United States
- Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois 60637, United States
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7
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Ballal A, Laurendon C, Salmon M, Vardakou M, Cheema J, Defernez M, O'Maille PE, Morozov AV. Sparse Epistatic Patterns in the Evolution of Terpene Synthases. Mol Biol Evol 2020; 37:1907-1924. [PMID: 32119077 DOI: 10.1093/molbev/msaa052] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
We explore sequence determinants of enzyme activity and specificity in a major enzyme family of terpene synthases. Most enzymes in this family catalyze reactions that produce cyclic terpenes-complex hydrocarbons widely used by plants and insects in diverse biological processes such as defense, communication, and symbiosis. To analyze the molecular mechanisms of emergence of terpene cyclization, we have carried out in-depth examination of mutational space around (E)-β-farnesene synthase, an Artemisia annua enzyme which catalyzes production of a linear hydrocarbon chain. Each mutant enzyme in our synthetic libraries was characterized biochemically, and the resulting reaction rate data were used as input to the Michaelis-Menten model of enzyme kinetics, in which free energies were represented as sums of one-amino-acid contributions and two-amino-acid couplings. Our model predicts measured reaction rates with high accuracy and yields free energy landscapes characterized by relatively few coupling terms. As a result, the Michaelis-Menten free energy landscapes have simple, interpretable structure and exhibit little epistasis. We have also developed biophysical fitness models based on the assumption that highly fit enzymes have evolved to maximize the output of correct products, such as cyclic products or a specific product of interest, while minimizing the output of byproducts. This approach results in nonlinear fitness landscapes that are considerably more epistatic. Overall, our experimental and computational framework provides focused characterization of evolutionary emergence of novel enzymatic functions in the context of microevolutionary exploration of sequence space around naturally occurring enzymes.
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Affiliation(s)
- Aditya Ballal
- Department of Physics & Astronomy and Center for Quantitative Biology, Rutgers University, Piscataway, NJ
| | - Caroline Laurendon
- John Innes Centre, Department of Metabolic Biology, Norwich Research Park, Norwich, United Kingdom.,Food & Health Programme, Institute of Food Research, Norwich Research Park, Norwich, United Kingdom
| | - Melissa Salmon
- John Innes Centre, Department of Metabolic Biology, Norwich Research Park, Norwich, United Kingdom.,Food & Health Programme, Institute of Food Research, Norwich Research Park, Norwich, United Kingdom.,Earlham Institute, Norwich Research Park, Norwich, United Kingdom
| | - Maria Vardakou
- John Innes Centre, Department of Metabolic Biology, Norwich Research Park, Norwich, United Kingdom.,Food & Health Programme, Institute of Food Research, Norwich Research Park, Norwich, United Kingdom.,School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Jitender Cheema
- John Innes Centre, Department of Computational and Systems Biology, Norwich Research Park, Norwich, United Kingdom
| | - Marianne Defernez
- Core Science Resources, Quadram Institute, Norwich Research Park, Norwich, United Kingdom
| | - Paul E O'Maille
- John Innes Centre, Department of Metabolic Biology, Norwich Research Park, Norwich, United Kingdom.,Food & Health Programme, Institute of Food Research, Norwich Research Park, Norwich, United Kingdom.,SRI International, Menlo Park, CA
| | - Alexandre V Morozov
- Department of Physics & Astronomy and Center for Quantitative Biology, Rutgers University, Piscataway, NJ
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8
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Deconvolving mutational patterns of poliovirus outbreaks reveals its intrinsic fitness landscape. Nat Commun 2020; 11:377. [PMID: 31953427 PMCID: PMC6969152 DOI: 10.1038/s41467-019-14174-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Accepted: 12/16/2019] [Indexed: 01/08/2023] Open
Abstract
Vaccination has essentially eradicated poliovirus. Yet, its mutation rate is higher than that of viruses like HIV, for which no effective vaccine exists. To investigate this, we infer a fitness model for the poliovirus viral protein 1 (vp1), which successfully predicts in vitro fitness measurements. This is achieved by first developing a probabilistic model for the prevalence of vp1 sequences that enables us to isolate and remove data that are subject to strong vaccine-derived biases. The intrinsic fitness constraints derived for vp1, a capsid protein subject to antibody responses, are compared with those of analogous HIV proteins. We find that vp1 evolution is subject to tighter constraints, limiting its ability to evade vaccine-induced immune responses. Our analysis also indicates that circulating poliovirus strains in unimmunized populations serve as a reservoir that can seed outbreaks in spatio-temporally localized sub-optimally immunized populations. Poliovirus has a higher mutation rate than HIV, yet has been almost eradicated by vaccination while an effective vaccine against HIV does not exist. Here, the authors develop a fitness model for poliovirus viral protein 1 to show that it is subject to stringent evolutionary constraints that limit its ability to avoid vaccine-induced immune responses.
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9
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Laine E, Karami Y, Carbone A. GEMME: a simple and fast global epistatic model predicting mutational effects. Mol Biol Evol 2019; 36:2604-2619. [PMID: 31406981 PMCID: PMC6805226 DOI: 10.1093/molbev/msz179] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 06/03/2019] [Accepted: 08/02/2019] [Indexed: 12/15/2022] Open
Abstract
The systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering, and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modeling intersite dependencies within biological sequences. However, state-of-the-art methods remain time consuming. Here, we present Global Epistatic Model for predicting Mutational Effects (GEMME) (www.lcqb.upmc.fr/GEMME), an original and fast method that predicts mutational outcomes by explicitly modeling the evolutionary history of natural sequences. This allows accounting for all positions in a sequence when estimating the effect of a given mutation. GEMME uses only a few biologically meaningful and interpretable parameters. Assessed against 50 high- and low-throughput mutational experiments, it overall performs similarly or better than existing methods. It accurately predicts the mutational landscapes of a wide range of protein families, including viral ones and, more generally, of much conserved families. Given an input alignment, it generates the full mutational landscape of a protein in a matter of minutes. It is freely available as a package and a webserver at www.lcqb.upmc.fr/GEMME/.
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Affiliation(s)
- Elodie Laine
- Sorbonne Université, UPMC University Paris 06, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France
| | - Yasaman Karami
- Sorbonne Université, UPMC University Paris 06, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France.,Sorbonne Université, UPMC-Univ P6, Institut du Calcul et de la Simulation
| | - Alessandra Carbone
- Sorbonne Université, UPMC University Paris 06, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France.,Institut Universitaire de France
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10
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Identifying immunologically-vulnerable regions of the HCV E2 glycoprotein and broadly neutralizing antibodies that target them. Nat Commun 2019; 10:2073. [PMID: 31061402 PMCID: PMC6502829 DOI: 10.1038/s41467-019-09819-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 04/02/2019] [Indexed: 02/06/2023] Open
Abstract
Isolation of broadly neutralizing human monoclonal antibodies (HmAbs) targeting the E2 glycoprotein of Hepatitis C virus (HCV) has sparked hope for effective vaccine development. Nonetheless, escape mutations have been reported. Ideally, a potent vaccine should elicit HmAbs that target regions of E2 that are most difficult to escape. Here, aimed at addressing this challenge, we develop a predictive in-silico evolutionary model for E2 that identifies one such region, a specific antigenic domain, making it an attractive target for a robust antibody response. Specific broadly neutralizing HmAbs that appear difficult to escape from are also identified. By providing a framework for identifying vulnerable regions of E2 and for assessing the potency of specific antibodies, our results can aid the rational design of an effective prophylactic HCV vaccine. A good vaccine should direct the immune response to virus regions that are most difficult to escape. Here, Quadeer et al. develop a predictive in-silico evolutionary model for HCV E2 which identifies one such antigenic region and identifies multiple broadly neutralizing human antibodies that appear difficult to escape from.
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11
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Raja R, Pareek A, Newar K, Dixit NM. Mutational pathway maps and founder effects define the within-host spectrum of hepatitis C virus mutants resistant to drugs. PLoS Pathog 2019; 15:e1007701. [PMID: 30934020 PMCID: PMC6459561 DOI: 10.1371/journal.ppat.1007701] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Revised: 04/11/2019] [Accepted: 03/13/2019] [Indexed: 12/11/2022] Open
Abstract
Knowledge of the within-host frequencies of resistance-associated amino acid variants (RAVs) is important to the identification of optimal drug combinations for the treatment of hepatitis C virus (HCV) infection. Multiple RAVs may exist in infected individuals, often below detection limits, at any resistance locus, defining the diversity of accessible resistance pathways. We developed a multiscale mathematical model to estimate the pre-treatment frequencies of the entire spectrum of mutants at chosen loci. Using a codon-level description of amino acids, we performed stochastic simulations of intracellular dynamics with every possible nucleotide variant as the infecting strain and estimated the relative infectivity of each variant and the resulting distribution of variants produced. We employed these quantities in a deterministic multi-strain model of extracellular dynamics and estimated mutant frequencies. Our predictions captured database frequencies of the RAV R155K, resistant to NS3/4A protease inhibitors, presenting a successful test of our formalism. We found that mutational pathway maps, interconnecting all viable mutants, and strong founder effects determined the mutant spectrum. The spectra were vastly different for HCV genotypes 1a and 1b, underlying their differential responses to drugs. Using a fitness landscape determined recently, we estimated that 13 amino acid variants, encoded by 44 codons, exist at the residue 93 of the NS5A protein, illustrating the massive diversity of accessible resistance pathways at specific loci. Accounting for this diversity, which our model enables, would help optimize drug combinations. Our model may be applied to describe the within-host evolution of other flaviviruses and inform vaccine design strategies.
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Affiliation(s)
- Rubesh Raja
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Aditya Pareek
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Kapil Newar
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Narendra M. Dixit
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
- * E-mail:
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12
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Co-evolution networks of HIV/HCV are modular with direct association to structure and function. PLoS Comput Biol 2018; 14:e1006409. [PMID: 30192744 PMCID: PMC6145588 DOI: 10.1371/journal.pcbi.1006409] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 09/19/2018] [Accepted: 07/31/2018] [Indexed: 01/09/2023] Open
Abstract
Mutational correlation patterns found in population-level sequence data for the Human Immunodeficiency Virus (HIV) and the Hepatitis C Virus (HCV) have been demonstrated to be informative of viral fitness. Such patterns can be seen as footprints of the intrinsic functional constraints placed on viral evolution under diverse selective pressures. Here, considering multiple HIV and HCV proteins, we demonstrate that these mutational correlations encode a modular co-evolutionary structure that is tightly linked to the structural and functional properties of the respective proteins. Specifically, by introducing a robust statistical method based on sparse principal component analysis, we identify near-disjoint sets of collectively-correlated residues (sectors) having mostly a one-to-one association to largely distinct structural or functional domains. This suggests that the distinct phenotypic properties of HIV/HCV proteins often give rise to quasi-independent modes of evolution, with each mode involving a sparse and localized network of mutational interactions. Moreover, individual inferred sectors of HIV are shown to carry immunological significance, providing insight for guiding targeted vaccine strategies.
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