Lethal Mutagenesis in Viruses and Bacteria

Abstract

In this work we study how mutations that change physical properties of cell proteins (stability) affect population survival and growth. We present a model in which the genotype is presented as a set folding free energies of cell proteins. Mutations occur upon replication, so stabilities of some proteins in daughter cells differ from those in the parent cell by amounts deduced from the distribution of mutational effects on protein stability. The genotype-phenotype relationship posits that the cell's fitness (replication rate) is proportional to the concentration of its folded proteins and that unstable essential proteins result in lethality. Simulations reveal that lethal mutagenesis occurs at a mutation rate close to seven mutations in each replication of the genome for RNA viruses and at about half that rate for DNA-based organisms, in accord with earlier predictions from analytical theory and experimental results. This number appears somewhat dependent on the number of genes in the organisms and the organism's natural death rate. Further, our model reproduces the distribution of stabilities of natural proteins, in excellent agreement with experiments. We find that species with high mutation rates tend to have less stable proteins compared to species with low mutation rates.

Contingency and entrenchment in protein evolution under purifying selection

“…Our model of purifying selection assumes that overstabilizing mutations are as deleterious as destabilizing mutations, so that only the wild-type stability has optimal fitness. However, several studies have shown that overstabilizing mutations can be neutral under stabilizing selection (75,77,80,88). Therefore, we also considered an alternative, semi-Gaussian fitness landscape in which argT sequences more stable than the wild type are just a fit as the wild type (Fig.…”

Relationship between protein thermodynamic constraints and variation of evolutionary rates among sites

“…A presumably more sophisticated model is based on posing a continuous dependence between fitness and Δ G (Tokuriki & Tawfik 2009, Chen &Shakhnovich 2009, Goldstein 2011, Wylie & Shakhnovich 2011). However, even though the continuous fitness models appear to be more realistic than the neutral stability-threshold models, in a recent study Arenas et al (2013) found that the neutral model leads to better predictions of site-specific amino-acid distributions.…”

“…Recent work has shown that describing protein evolution from the perspective of thermodynamic stability provides a wealth of insight into important aspects of protein evolution, such as the evolution of mutational robustness (Bloom et al 2007), the origin of epistatic interactions (Bershtein et al 2006, Gong et al 2013), lethal mutagenesis (Chen & Shakhnovich 2009), determinants of evolutionary rate at protein 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). Moreover, some studies suggest that ΔΔ G -based models are useful to study site-specific constraints.…”

The Influence of Selection for Protein Stability on dN/dS Estimations

“…The Fermi–Dirac like form of equation 1 suggests that fitness effects of mutations are more dramatic at lower stabilities (Chen and Shakhnovich 2009). The effect of mutations on folding stability is modeled as: $\Delta G_{\text{after}}=\Delta G_{\text{before}}+\Delta \Delta G_{\text{mutation}}$ An arising mutation would have a selection coefficient s defined as (Goldstein 2011; Wylie and Shakhnovich 2011): $s=\frac{F_{\text{after}}-F_{\text{before}}}{F_{\text{before}}}\sim e^{\text{β}\Delta G_{\text{before}}}true(1-e^{\text{β}\Delta \Delta G_{\text{mutation}}}true)$ which can be positive, negative, or nearly zero corresponding to mutations being beneficial, deleterious, or neutral.…”