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寻找蛋白质系统中双因子相互作用的机制描述。

Searching for a mechanistic description of pairwise epistasis in protein systems.

机构信息

Department of Physics, University of Idaho, Moscow, Idaho, USA.

Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, USA.

出版信息

Proteins. 2022 Jul;90(7):1474-1485. doi: 10.1002/prot.26328. Epub 2022 Mar 11.

Abstract

When two or more amino acid mutations occur in protein systems, they can interact in a nonadditive fashion termed epistasis. One way to quantify epistasis between mutation pairs in protein systems is by using free energy differences: ϵ = ΔΔG  - (ΔΔG  + ΔΔG ) where ΔΔG refers to the change in the Gibbs free energy, subscripts 1 and 2 refer to single mutations in arbitrary order and 1,2 refers to the double mutant. In this study, we explore possible biophysical mechanisms that drive pairwise epistasis in both protein-protein binding affinity and protein folding stability. Using the largest available datasets containing experimental protein structures and free energy data, we derived statistical models for both binding and folding epistasis (ϵ) with similar explanatory power (R ) of .299 and .258, respectively. These models contain terms and interactions that are consistent with intuition. For example, increasing the Cartesian separation between mutation sites leads to a decrease in observed epistasis for both folding and binding. Our results provide insight into factors that contribute to pairwise epistasis in protein systems and their importance in explaining epistasis. However, the low explanatory power indicates that more study is needed to fully understand this phenomenon.

摘要

当蛋白质系统中发生两个或更多的氨基酸突变时,它们可能以非加性的方式相互作用,这种方式被称为上位性。一种量化蛋白质系统中突变对之间上位性的方法是使用自由能差异:ϵ=ΔΔG−(ΔΔG+ΔΔG),其中ΔΔG 表示吉布斯自由能的变化,下标 1 和 2 表示任意顺序的单个突变,1,2 表示双突变体。在这项研究中,我们探索了可能的生物物理机制,这些机制驱动蛋白质-蛋白质结合亲和力和蛋白质折叠稳定性的成对上位性。使用包含最大可用实验蛋白质结构和自由能数据的数据集,我们为结合和折叠上位性(ϵ)分别推导出了具有相似解释能力(R)的统计模型,分别为.299 和.258。这些模型包含与直觉一致的术语和相互作用。例如,增加突变位点的笛卡尔分离会导致折叠和结合中观察到的上位性降低。我们的研究结果提供了对蛋白质系统中成对上位性的因素及其在解释上位性中的重要性的深入了解。然而,低解释能力表明,需要进一步研究才能全面理解这一现象。

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