A biophysical protein folding model accounts for most mutational fitness effects in viruses

By C. Scott Wylie, Eugene I. Shakhnovich. 2011

193 citations recorded by the source corpus.

Fitness effects of mutations fall on a continuum ranging from lethal to deleterious to beneficial. The distribution of fitness effects (DFE) among random mutations is an essential component of every evolutionary model and a mathematical portrait of robustness. Recent experiments on five viral species all revealed a characteristic bimodal-shaped DFE featuring peaks at neutrality and lethality. However, the phenotypic causes underlying observed fitness effects are still unknown and presumably, are thought to vary unpredictably from one mutation to another. By combining population genetics simulations with a simple biophysical protein folding model, we show that protein thermodynamic stability accounts for a large fraction of observed mutational effects. We assume that moderately destabilizing mutations inflict a fitness penalty proportional to the reduction in folded protein, which depends continuously on folding free energy (ΔG). Most mutations in our model affect fitness by altering ΔG, whereas based on simple estimates, ∼10% abolish activity and are unconditionally lethal. Mutations pushing ΔG > 0 are also considered lethal. Contrary to neutral network theory, we find that, in mutation/selection/drift steady state, high mutation rates (m) lead to less stable proteins and a more dispersed DFE (i.e., less mutational robustness). Small population size (N) also decreases stability and robustness. In our model, a continuum of nonlethal mutations reduces fitness by ∼2% on average, whereas ∼10–35% of mutations are lethal depending on N and m. Compensatory mutations are common in small populations with high mutation rates. More broadly, we conclude that interplay between biophysical and population genetic forces shapes the DFE.

Subfields: Genetics, Public Health, Environmental and Occupational Health, Sociology and Political Science, Computational Biology & Bioinformatics, AI for Physical Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Public Health, Environmental and Occupational Health

In Sociology and Political Science

In Computational Biology & Bioinformatics

In AI for Physical Sciences

Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, background): “For instance, protein thermal stability, measured through folding free energy, is approximately additive [8, 13].”
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “[31] C Scott Wylie and Eugene I Shakhnovich.”
Evolvability as a function of purifying selection in TEM-1 β-lactamase. (2015, unknown): “The idea is that the ''extra'' stability beyond that required to asymptotically populate the native state provides a thermodynamic basis for robustness by buffering the slightly destabilizing effects of most mutations (Bloom et al., 2005; Tokuriki and Tawfik, 2009; Wylie and Shakhnovich, 2011).”
Capturing the mutational landscape of the beta-lactamase TEM-1 (2013, methodology): “First μ and σ were extrapolated from pools of mutants with bioph ysical informations extracted from the literature (3), and the assumption that the distrib u ion of mutation effect on ∆∆G may be conserved across all proteins may be wrong.”
Latent Effects of Hsp90 Mutants Revealed at Reduced Expression Levels (2013, background): “Bi-modal fitness distributions are predicted based on a model where fitness effects are dominated by the impact of mutations on protein stability [18].”
Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics (2014, methodology): “com As noted in [11,25 ,45] population size may affect the cellular distribution of important signatures of folding stabilities such that organisms with small effective population sizes (e.”
Inferring the shape of global epistasis (2018, background): “, thermodynamic stability for a protein’s native conformation is almost always and important phenotype, and there is a consistent thread in the literature suggesting that the phenotypic effects of most mutations are mediated via their effects on thermodynamic stability and the non-linear relationshi”
Selection for Protein Stability Enriches for Epistatic Interactions (2018, background): “However, this extremely small stability margin is a well-known artifact of our decision 250 to model fitness as a step function in stability [8] rather than a more realistic logistic function [11,30], 251 and the fact that our simulations do not include any of the other factors that would tend to in”
Relationship between protein thermodynamic constraints and variation of evolutionary rates among sites (2014, methodology): “…level (Drummond & Wilke 2008, Serohijos et al. 2012), the evolution of novel function (Bloom et al. 2006, Tokuriki et al. 2008), and the expected equilibrium distributions of stability and the explanation of marginal stability (Taverna & Goldstein 2002, Goldstein 2011, Wylie & Shakhnovich 2011).”