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

机译:生物物理蛋白质折叠模型可解释病毒中大多数突变适应性效应

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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 pop ulation 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 (AG). Most mutations in our model affect fitness by altering AG, whereas based on simple estimates, ~10% abolish activity and are unconditionally lethal. Mutations pushing AG > 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 (A/) 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 de pending 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.
机译:突变的适应性影响范围从致命到有害再到有益。适应性效应(DFE)在随机突变之间的分布是每个进化模型的基本组成部分,也是鲁棒性的数学描述。最近对五个病毒物种的实验都揭示了特征性的双峰形DFE,其特征在于在中性和致死性上具有峰值。然而,潜在的观察到的适应性影响的表型原因仍然未知,并且据推测,从一个突变到另一个突变,其变化不可预测。通过将种群遗传学模拟与简单的生物物理蛋白质折叠模型相结合,我们表明蛋白质热力学稳定性占观察到的突变效应的很大一部分。我们假设中度不稳定的突变会造成与折叠蛋白减少成正比的适应性损失,折叠蛋白的减少持续取决于折叠自由能(AG)。我们模型中的大多数突变都会通过改变AG来影响适应性,而根据简单的估计,约10%的活性会被废除并且无条件地致命。促使AG> 0的突变也被认为具有致命性。与中性网络理论相反,我们发现,在突变/选择/漂移稳态下,高突变率(m)导致稳定性较差的蛋白质和更分散的DFE(即,较低的突变稳健性)。人口规模小(A /)也降低了稳定性和鲁棒性。在我们的模型中,连续的非致死突变平均使适应性降低约2%,而约10-35%的突变在N和m上具有致命性。补偿性突变在突变率高的小人群中很常见。更广泛地说,我们得出结论,生物物理和种群遗传力之间的相互作用影响了DFE。

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