Data from: Coevolution-based inference of amino acid interactions underlying protein function

Protein function arises from a poorly understood pattern of energetic interactions between amino acid residues. Sequence-based strategies for deducing this pattern have been proposed, but lack of benchmark data has limited experimental verification. Here, we extend deep-mutation technologies to enable measurement of many thousands of pairwise amino acid couplings in several homologs of a protein family - a deep coupling scan (DCS). The data show that cooperative interactions between residues are loaded in a sparse, evolutionarily conserved, spatially contiguous network of amino acids. The pattern of amino acid coupling is quantitatively captured in the coevolution of amino acid positions, especially as indicated by the statistical coupling analysis (SCA), providing experimental confirmation of the key tenets of this method. This work exposes the collective nature of physical constraints on protein function and clarifies its link with sequence analysis, enabling a general practical approach for understanding the structural basis for protein function.蛋白质功能源于氨基酸残基间一种尚未被充分阐明的能量相互作用模式。学界已提出基于序列推导该模式的策略，但因缺乏基准数据集（benchmark data），其实验验证受到了限制。本研究拓展了深度突变技术，得以对某一蛋白质家族的多个同源蛋白中的数千对氨基酸残基配对耦合作用进行测量——即深度耦合扫描（deep coupling scan, DCS）。实验数据表明，残基间的协同相互作用富集于一个稀疏、进化保守且空间连续的氨基酸网络中。氨基酸残基配对耦合的模式可通过氨基酸位点的共进化进行定量捕捉，尤其是通过统计耦合分析（statistical coupling analysis, SCA）所揭示的结果，这为该方法的核心原理提供了实验验证。本研究揭示了蛋白质功能所受物理约束的集体性本质，阐明了其与序列分析的内在关联，为解析蛋白质功能的结构基础提供了通用且实用的研究路径。