Computational modelling of evolutionary change

Lead Research Organisation: MRC National Inst for Medical Research

Abstract

Evolution is important for three reasons. Firstly, understanding how evolution occurs is inherently critical to many processes that involve living objects. For example, some pathogens (e.g. RNA viruses) evolve in timescales of weeks to years; combating these public health threats require understanding how these threats change and shift due to the evolutonary process. Secondly, the evolutionary record provies a wealth of information about structure, function, and physiological roles. Decyphering this record requires an understanding of the process that resulted in the observed variety. Thirdly, evolution is the essence behind all biological phenomenon. If we wish to understand why things are the way they are, how the process of evolution determines the resulting properties of biological components, systems, and organisms, and how these properties affect the biological system, we need to understand this evolutionary process. We are modelling a number of different evolutionary processes, including evolution of viruses (influenza, papillomavirus), protein structures, and biological networks. We are in particular interested in how these processes can change, for instance, when a virus encounters a new host, or when the needs of the network are different. It is hoped that these models will allow us to understand the evolutionary process better, providing important understanding of existent biological systems. The work with viruses, in particular, is important in understanding and monitoring emerging health threats.

Technical Summary

We are working in a variety of areas in understanding the evolutionary process, modelling this process, and applying these models to specific biological and medical problems. 1) Evolution of pathogens We are modelling the molecular evolution of influenza, both at the DNA and protein levels, a collaboration with Alan Hay (virology) and other members of the EU FLUPOL project. Most evolutionary models assume that the evolutionary process is similar for all locations in these biomolecules at all time. We are interested in moving beyond these models, including temporal and spatial heterogeneity. In particular, we are interested in looking at how the process changes when influenza switches hosts. This has allowed us, for instance, to reconstruct the history of the 1918 Spanish flu epidemic, as well as identifying protein locations throughout the influenza genome where the selective constraints are different in avian and human viruses, work that will be extended to include swine viruses. These analyses can provide important information to better direct the widespread virus surveillance projects, as well as allowing us to better interpret the results of these efforts. We have also looked at the evolution of papillomavirus, a collaboration with John Doorbar (virology), developing methods to determine how this virus spread over the wide range of hosts. These projects have involved the construction of new, generally useful techniques to understand and model molecular evolution. 2) Models for better phylogenetics Most models of sequence change only consider changes in DNA or amino acid, neglecting the process of insertions and deletions. This is because there are no good models of this process that have been included in phylogenetic reconstruction methods. In collaboration with Simon Wheelan (Manchester), we are developing reconstruction models that include insertions and deletions, modelled using a Tree-Based Hidden Markov Model (T-HMM) formalism. 3) Evolution of chemotaxis In collaboration with Orkun Soyer (CoSBI, Trento) we are working to understand how biochemical networks can evolve to mediate simple behaviour, in our case chemotaxis. We are looking at the relationship between the evolutionary context (including the environment, the available biochemistry, and the mechanics of evolutionary change) and the chemotaxis process that results. This work is directed towards understanding both how evolution is able to evolve networks, as well as understanding the chemotaxis process itself, especially the variety of possible strategies that might exist beyond the well-studied E. coli model system. 4) Evolution of protein structure Most models of protein evolution consider the amino acid or DNA sequence. Much less is known about the process of structural change. In collaboration with Willie Taylor (Mathematical Biology) and Jotun Hein (Oxford) we are developing models of this structural change. It is hoped that this work can look deeper into the past, as this structural change occurs at a much slower rate than sequence change. It can also provide insight into the process that gave us the observed universe of protein structures.

Publications

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Williams PD (2006) SELECTIVE ADVANTAGE OF RECOMBINATION IN EVOLVING PROTEIN POPULATIONS: A LATTICE MODEL STUDY. in International journal of modern physics. C, Physics and computers

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Williams PD (2006) Assessing the accuracy of ancestral protein reconstruction methods. in PLoS computational biology

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Tamuri AU (2009) Identifying changes in selective constraints: host shifts in influenza. in PLoS computational biology

 
Description BBSRC Response Mode: Mechanistic models of protein sequence evolution
Amount £407,027 (GBP)
Funding ID BB/P007562/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 04/2017 
End 03/2020
 
Description Collaborative grant
Amount £2,825,452 (GBP)
Funding ID 203268/Z/16/Z 
Organisation Wellcome Trust 
Sector Charity/Non Profit
Country United Kingdom
Start 01/2017 
End 12/2020
 
Description EPSRC Project Grant
Amount £126,291 (GBP)
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 08/2010 
End 08/2013
 
Title New methods for phylogenetic analysis 
Description 1) Methods for identifying changes in selective constraints when the particular context of a protein (or other biomacromolecule) changes, such as in the process of a pathogen changing host or changing antigenic properties. 
Type Of Material Technology assay or reagent 
Year Produced 2012 
Provided To Others? Yes  
Impact Identification of changes of selective constraints on the various proteins in influenza when it shifts from avian to human hosts. 
URL https://github.com/tamuri/swmutsel
 
Description EPSRC Grant 
Organisation University of Manchester
Department School of Physics and Astronomy Manchester
Country United Kingdom 
Sector Academic/University 
PI Contribution Agent based modelling of evolutionary processes
Collaborator Contribution Collaboration looking at multi-level selection involved in horizontal gene transfer in bacteria. Grant has finished in 2013 but collaboration is continuing.
Impact PLoS Comput Biol. 2013 Apr;9(4):e1003036. doi: 10.1371/journal.pcbi.1003036. Epub 2013 Apr 18. The evolution of collective restraint: policing and obedience among non-conjugative plasmids. Kentzoglanakis K1, García López D, Brown SP, Goldstein RA. Multidisciplinary, involving microbiology, computer modelling, theoretical physics, and bioinformatics
Start Year 2010
 
Description Evolution of Chemotaxis 
Organisation University of Trento
Department Centre for Computational and Systems Biology
Country Italy 
Sector Academic/University 
PI Contribution Analysing evolution of chemotactic strategies using in silico simulations
Collaborator Contribution Active collaboration with Orkun Soyer
Impact Optimal chemotactic responses in stochastic environments. Godány M, Khatri BS, Goldstein RA. PLoS One. 2017 Jun 23;12(6):e0179111. doi: 10.1371/journal.pone.0179111. eCollection 2017. PMID: 28644830 Evolution of response dynamics underlying bacterial chemotaxis. Soyer OS, Goldstein RA. BMC Evol Biol. 2011 Aug 16;11:240. doi: 10.1186/1471-2148-11-240. PMID: 21846396 Evolution of taxis responses in virtual bacteria: non-adaptive dynamics. Goldstein RA, Soyer OS. PLoS Comput Biol. 2008 May 23;4(5):e1000084. doi: 10.1371/journal.pcbi.1000084.
Start Year 2007
 
Description Evolution of ERV 
Organisation University College London
Country United Kingdom 
Sector Academic/University 
PI Contribution Research into evolutionary dynamics of endogenous retroviruses
Collaborator Contribution Understanding of the biology
Impact Just started
Start Year 2013
 
Description Evolution of ERV 
Organisation University of Glasgow
Country United Kingdom 
Sector Academic/University 
PI Contribution Research into evolutionary dynamics of endogenous retroviruses
Collaborator Contribution Understanding of the biology
Impact Just started
Start Year 2013
 
Description Evolutionary analysis of papillomavirus 
Organisation Medical Research Council (MRC)
Department MRC National Institute for Medical Research (NIMR)
Country United Kingdom 
Sector Academic/University 
PI Contribution We performed evolutoinary analysis of papillomavirus, including identifying host shift events and other events responsible for incongruence between virus and host phylogenetics
Collaborator Contribution Active collaboration with virologist on interesting and important problem
Impact 20093429
Start Year 2007
 
Description FLUPOL Grant 
Organisation European Molecular Biology Laboratory
Department European Molecular Biology Laboratory Grenoble outstation
Country France 
Sector Academic/University 
PI Contribution Analysing process of host shifts in influenza, determining history of 1918 'Spanish flu' pandemic, identifying locations of host-specific factors through phylogenetic analysis
Collaborator Contribution Active collaboration on EU-funded project analysing influenza polymerase structure and evolution
Impact 19911053 19787384
Start Year 2006
 
Description HIV Evolution 
Organisation University College London
Country United Kingdom 
Sector Academic/University 
PI Contribution Analysing evolution of HIV with focus on public health issues
Collaborator Contribution Partners provide expertise in HIV
Impact Sci Rep. 2016 Nov 30;6:38153. doi: 10.1038/srep38153. Wide variation in susceptibility of transmitted/founder HIV-1 subtype C Isolates to protease inhibitors and association with in vitro replication efficiency. Sutherland KA, Collier DA, Claiborne DT, Prince JL, Deymier MJ, Goldstein RA, Hunter E, Gupta RK.
Start Year 2012
 
Description Protein evolution 
Organisation University of Colorado
Country United States 
Sector Academic/University 
PI Contribution Modellilng of evolution of a simple model of proteins
Collaborator Contribution Active joint collaboration
Impact Yanlong O. Xu, Randall W. Hall, Richard A. Goldstein, and David D. Pollock (2005), Divergence, recombination, and retention of functionality during protein evolution, Human Genomics, 2:158-167 Paul D. Williams, David D. Pollock and Richard A. Goldstein (2006), Functionality and the evolution of marginal stability in proteins: Inferences from lattice simulations, Evol. Bioinform. Online, 2:1-11. Paul D. Williams, David D. Pollock and Richard A. Goldstein (2006), Selective advantage of recombination in evolving protein populations: A lattice model study, Int. J. Mod. Phys. C, 17:75-90. Paul D. Williams, David D. Pollock, Benjamin P. Blackburne, and Richard A. Goldstein (2006), Accessing the accuracy of ancestral protein reconstruction methods, PLoS Computational Biology, 2:e69, PMID: 16789817. Richard A. Goldstein and David D. Pollock (2006), Observations of amino acid gain and loss during protein evolution are explained by statistical bias, Mol. Biol. Evol., 23: 1444, PMID: 16698770. Richard A. Goldstein (2007), Amino-acid interactions in psychrophiles, mesophiles, thermophiles, and hyperthermophiles: Insights from the quasi-chemical approximation. Protein Sci. 16, 1887-1895, PMID: 17766385. Richard A. Goldstein (2008), The structure of protein evolution and the evolution of protein structure, Curr. Opinion Struct. Biol., 18, 170-177. Richard A. Goldstein (2011), The evolution and evolutionary consequences of marginal thermostability in proteins, Proteins, 79:1396-1407. Richard A. Goldstein and David D. Pollock (2012), Modeling protein evolution, in Computational Modeling of Biological Systems (Nikolay Dokholyan, ed.), Springer, pps. 426-431. Ivan Coluzza, James T. MacDonald, Michael I. Sadowski, William R. Taylor, and Richard A Goldstein (2012), Analytic Markovian rates for generalized protein structure evolution, PLoS One, 7:e34228. David A. Liberles et al. (2012), The Interface of Protein Structure, Protein Biophysics, and Molecular Evolution, Protein Science, 21:769-785. David D. Pollock, Grant Thiltgen, and Richard A. Goldstein (2012), Relaxation of amino acid propensities: An evolutionary Stokes shift, Proceedings of the National Academy of Sciences U.S.A., 109:E1352-1359, PMID: 22547823. Grant Thiltgen and Richard A. Goldstein (2012), Assessing predictors of changes in protein stability upon mutation without using experimental data, PLoS One, 7:e46084. Richard A. Goldstein (2013), Population size dependence of fitness effect distribution and substitution rate probed by biophysical model of protein thermostability. Genome Biol Evol., 5:1584-1593, PMID: 23884461. David D. Pollock and Richard A. Goldstein (2014), Strong evidence for protein epistasis, weak evidence against it. Proceedings of the National Academy of Sciences U.S.A., 111:E1450. Richard A. Goldstein, Stephen T. Pollard, Seena D. Shah, David D. Pollock (2015), Non-adaptive amino acid convergence rates decrease over time. Mol Biol Evol, 32:1373-81. Bhavin S. Khatri and Richard A. Goldstein (2015), A coarse-grained biophysical model of sequence evolution and the population size dependence of the speciation rate, J Theor Biol, 378:56-64. Bhavin S. Khatri and Richard A. Goldstein (2015), Simple Biophysical Model Predicts Faster Accumulation of Hybrid Incompatibilities in Small Populations Under Stabilizing Selection. Genetics. 201:1525-1537. Richard A. Goldstein, David D. Pollock (2016) The tangled bank of amino acids. Protein Science 25:1354-1362. Grant Thiltgen, Mario dos Reis, Richard A. Goldstein (2017) Finding Direction in the Search for Selection. Journal of Molecular Evolution, doi:10.1007/s00239-016-9765-5. Richard A. Goldstein and David D. Pollock (2017), Sequence entropy of folding and the absolute rate of amino acid substitutions, Nature Ecology & Evolution 1:1923-1930. David D. Pollock, Stephen T. Pollard, Jonathan A. Shortt, Richard A. Goldstein (2017) Mechanistic Models of Protein Evolution in Evolutionary Biology: Self/Nonself Evolution, Species and Complex Traits Evolution, Methods and Concepts, P. Pontarotti (ed.), Springer, Cham, Switzerland, pages 277-296.