Protein biophysics in the cellular context: physics meets evolution.

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I will present recent theoretical and experimental developments aimed at understanding the two-way link between protein stability and protein evolution.  At the heart of these developments are models of evolutionary dynamics that merge molecular mechanisms of protein stability and folding with population genetics. Traditional population genetics models are agnostic to the physical-chemical nature of mutational effects. Rather they operate with an a’priori assumed distributions of fitness effects (DFE) of mutations from which evolutionary dynamics are derived. Alternatively some population genetics models aim to derive DFE from evolutionary observations. In departure with this tradition the novel multiscale models integrate the molecular effects of mutations on physical properties of proteins, most notably their stability, into physically intuitive yet detailed genotype-phenotype relationship (GPR) assumptions. I will present a range of models from simple analytical diffusion-based model on biophysical fitness landscapes to more sophisticated computational models of populations of model cells where genetic changes are mapped into molecular effects using biophysical modeling of proteins and ensuing fitness changes determine the fate of mutations in realistic population dynamics. Examples of insights derived from biophysics-based multiscale models include parameter-free prediction of distribution of protein stabilities in natural proteomes that explains the observation of “marginal stability” of proteins without resorting to unproven stability-activity tradeoffs,  the fundamental limit on mutation rates in living organisms, physics of thermal adaptation, co-evolution of protein interactions and abundances in cytoplasm and related results, some of which I will present and discuss. 

Next I will describe “bottom-up experimental efforts to establish the relationship between biophysical properties of proteins (stability, activity, interactions with other proteins) and fitness. The approach is based on introducing rational introducing genetic variation on the chromosome of E.coli using genome editing approaches with subsequent concurrent evaluation of biophysical effects of mutations in vitro and fitness effect of strains that have these genetic variants in their chromosome. Carrying out this program for two genes encoding essential metabolic enzymes in E. coli – folA and adk – we obtained deep insights into the relationship between protein stability and fitness. In particular we quantitatively determined how the changes in stability affect the protein turnover in cellular environment resulting in changes in the abundance of functional protein and through that affecting the phenotype (growth rates and lag times). We established fundamental role of protein quality control – chaperone GroEL and certain proteases that modulate the fitness effects of stability-changing mutations in a predictable way. We developed a dynamic steady state theory that describes stability-dependent protein turnover and established the relationship between protein abundance and stability that is different from simple equilibrium Boltzmann distribution. These advances allowed us to get a comprehensive biophysical fitness landscapes for metabolic enzymes. Further we applied these advances to the analysis of antibiotic resistance in DHFR encoded by folA. The theory allowed predicting fitness effect of escape mutations in DHFR and IC50 against antibiotic trimethoprim with very high accuracy based only on biophysical molecular properties of numerous DHFR mutants. Altogether these results provide a clear picture of interplay between biophysical traits and evolutionary dynamics on various time scales – from evolution of modern proteomes to evolution of antibiotic resistance and provides practical tools to address the problem of pathogen escape from stressors from a fundamental physical-chemical perspective.