Sun Mengyi, Stoltzfus Arlin, McCandlish David M
Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Office of Data and Informatics, Material Measurement Laboratory, NIST, Gaithersburg, MD.
bioRxiv. 2024 Oct 15:2024.10.11.617952. doi: 10.1101/2024.10.11.617952.
The effect of replacing the amino acid at a given site in a protein is difficult to predict. Yet, evolutionary comparisons have revealed highly regular patterns of interchangeability between pairs of amino acids, and such patterns have proved enormously useful in a range of applications in bioinformatics, evolutionary inference, and protein design. Here we reconcile these apparently contradictory observations using fitness data from over 350,000 experimental amino acid replacements. Almost one-quarter of the 20 × 19 = 380 types of replacements have broad distributions of fitness effects (DFEs) that closely resemble the background DFE for random changes, indicating an overwhelming influence of protein context in determining mutational effects. However, we also observe that the 380 pair-specific DFEs closely follow a maximum entropy distribution, specifically a truncated exponential distribution. The shape of this distribution is determined entirely by its mean, which is equivalent to the chance that a replacement of the given type is fitter than a random replacement. In this type of distribution, modest deviations in the mean correspond to much larger changes in the probability of falling in the far right tail, so that modest differences in mean exchangeability may result in much larger differences in the chance of a highly fit mutation. Indeed, we show that under the assumption that purifying selection filters out the vast majority of mutations, the maximum entropy distributions of fitness effects inferred from deep mutational scanning experiments predict the characteristic patterns of amino acid change observed in molecular evolution. These maximum entropy distributions of mutational effects not only provide a tuneable model for molecular evolution, but also have implications for mutational effect prediction and protein engineering.
预测蛋白质中特定位点氨基酸替换的效果颇具难度。然而，进化比较揭示了氨基酸对之间高度规则的互换模式，并且这些模式在生物信息学、进化推断和蛋白质设计的一系列应用中已证明极为有用。在此，我们利用超过350,000次实验性氨基酸替换的适应性数据，调和了这些明显相互矛盾的观察结果。在20×19 = 380种替换类型中，近四分之一具有广泛的适应性效应分布（DFE），与随机变化的背景DFE极为相似，这表明蛋白质背景在决定突变效应方面具有压倒性影响。然而，我们也观察到这380种特定对的DFE紧密遵循最大熵分布，具体而言是截断指数分布。这种分布的形状完全由其均值决定，该均值等同于给定类型的替换比随机替换更具适应性的概率。在这种分布类型中，均值的适度偏差对应于落入最右侧尾部概率的大得多的变化，因此平均互换性的适度差异可能导致高度适应性突变概率的大得多的差异。实际上，我们表明，在净化选择滤除绝大多数突变这一假设下，从深度突变扫描实验推断出的适应性效应的最大熵分布预测了分子进化中观察到的氨基酸变化的特征模式。这些突变效应的最大熵分布不仅为分子进化提供了一个可调节的模型，而且对突变效应预测和蛋白质工程也具有启示意义。
