蛋白质中的欠采样和共同进化推论

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Cell Systems Pub Date : 2023-03-15 Epub Date: 2023-01-23 DOI:10.1016/j.cels.2022.12.013
Yaakov Kleeorin, William P Russ, Olivier Rivoire, Rama Ranganathan
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引用次数: 0

摘要

蛋白质的结构、功能和进化取决于氨基酸之间局部和集体的表观相互作用。定义这些相互作用的一个有效方法是构建氨基酸之间的耦合模型,再现在组成蛋白质家族的序列中观察到的经验统计数据(频率和相关性)。然后对顶级耦合进行解释。在这里,我们展示了目前实施的这种推论不平等地代表了表观相互作用,这个问题从根本上说是由于在蛋白质中发生表观作用的不同尺度背景下序列采样有限而引起的。我们表明,这些问题解释了当前方法预测氨基酸间三级接触的能力,以及无法明显揭示功能相关的、集体进化的残基(称为扇区)的更大网络的原因。这项工作为更深入地理解和改进基于进化的蛋白质模型奠定了必要的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Undersampling and the inference of coevolution in proteins.

Protein structure, function, and evolution depend on local and collective epistatic interactions between amino acids. A powerful approach to defining these interactions is to construct models of couplings between amino acids that reproduce the empirical statistics (frequencies and correlations) observed in sequences comprising a protein family. The top couplings are then interpreted. Here, we show that as currently implemented, this inference unequally represents epistatic interactions, a problem that fundamentally arises from limited sampling of sequences in the context of distinct scales at which epistasis occurs in proteins. We show that these issues explain the ability of current approaches to predict tertiary contacts between amino acids and the inability to obviously expose larger networks of functionally relevant, collectively evolving residues called sectors. This work provides a necessary foundation for more deeply understanding and improving evolution-based models of proteins.

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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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