The overarching goal of the proposed research is to study evolution of structure, Biophysical properties and function of proteins in the context of Darwinian evolution of their carrier organisms. In the past budget period we significantly advanced towards that goal by introducing rich, powerful yet tractable multiscale models of protein evolution which derive organismal fitness from genomic sequences via intuitive yet realistic Genotype- Phenotype relationships which relate biophysical and functional properties of proteins to important phenotypic traits such cell division. Here we will further develop this approach by adding more realism to the models including treatment of proteins in sequence-based multiscale model at atomic resolution, prototypical E.coli glycolytic pathway, effect of misfolding on fitness and effect of protein abundances. Specific problems that will be addressed include the effect of ecology of the population on key Biophysical properties of proteins such as distribution of their stabilities, abundances and strengths of protein-protein interactions in the proteome;reconstruction of ancestor sequences and molecular phylogenies from genomics data within physically realistic models, relationship between protein stability, abundance in cytoplasm, and rate of their evolution. Predictions and insights from multiscale models will be vigorously tested against high-throughput Bioinfiomatic analyses and experiment. Experimental studies proposed here include measurements of stabilities of statistically representative sets of proteins in several proteomes, of bacteria having various ecological parameters (e.g population sizes and/or mutation rates). Successful completion of these studies will advance our ability to extract functionally relevant signals from genomic sequences by putting their analysis on firm evolutionary and Biophysical ground.

Public Health Relevance

This research aims to establish the relation between ecological properties of bacterial populations and molecular properties of their proteomes. It will provide foundation to determine effective tools to fight adverse effect of infection and address such unmet medical needs as development of novel medicines and treatment protocols to overcome bacterial resistance.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Macromolecular Structure and Function D Study Section (MSFD)
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Edmonds, Charles G
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Harvard University
Schools of Arts and Sciences
United States
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Manhart, Michael; Adkar, Bharat V; Shakhnovich, Eugene I (2018) Trade-offs between microbial growth phases lead to frequency-dependent and non-transitive selection. Proc Biol Sci 285:
Rotem, Assaf; Serohijos, Adrian W R; Chang, Connie B et al. (2018) Evolution on the Biophysical Fitness Landscape of an RNA Virus. Mol Biol Evol 35:2390-2400
Manhart, Michael; Shakhnovich, Eugene I (2018) Growth tradeoffs produce complex microbial communities on a single limiting resource. Nat Commun 9:3214
Jacobs, William M; Shakhnovich, Eugene I (2018) Accurate Protein-Folding Transition-Path Statistics from a Simple Free-Energy Landscape. J Phys Chem B :
Razban, Rostam M; Gilson, Amy I; Durfee, Niamh et al. (2018) ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms' proteomes. Bioinformatics 34:3557-3565
Bershtein, Shimon; Serohijos, Adrian Wr; Shakhnovich, Eugene I (2017) Bridging the physical scales in evolutionary biology: from protein sequence space to fitness of organisms and populations. Curr Opin Struct Biol 42:31-40
Gilson, Amy I; Marshall-Christensen, Ahmee; Choi, Jeong-Mo et al. (2017) The Role of Evolutionary Selection in the Dynamics of Protein Structure Evolution. Biophys J 112:1350-1365
Choi, Jeong-Mo; Gilson, Amy I; Shakhnovich, Eugene I (2017) Graph's Topology and Free Energy of a Spin Model on the Graph. Phys Rev Lett 118:088302
Adkar, Bharat V; Manhart, Michael; Bhattacharyya, Sanchari et al. (2017) Optimization of lag phase shapes the evolution of a bacterial enzyme. Nat Ecol Evol 1:149
Jacquin, Hugo; Gilson, Amy; Shakhnovich, Eugene et al. (2016) Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models. PLoS Comput Biol 12:e1004889

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