Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering

共同进化 上位性 计算机科学 背景(考古学) 蛋白质测序 蛋白质工程 人工智能 序列空间 合成生物学 定向进化 数据科学 机器学习 计算生物学 生物 理论计算机科学 生态学 遗传学 生物化学 肽序列 数学 基因 巴拿赫空间 突变体 古生物学 纯数学
作者
Marcel Wittmund,Frédéric Cadet,Mehdi D. Davari
出处
期刊:ACS Catalysis [American Chemical Society]
卷期号:12 (22): 14243-14263 被引量:33
标识
DOI:10.1021/acscatal.2c01426
摘要

Engineering proteins and enzymes with the desired functionality has broad applications in molecular biology, biotechnology, biomedical sciences, health, and medicine. The vastness of protein sequence space and all the possible proteins it represents can pose a considerable barrier for enzyme engineering campaigns through directed evolution and rational design. The nonlinear effects of coevolution between amino acids in protein sequences complicate this further. Data-driven models increasingly provide scientists with the computational tools to navigate through the largely undiscovered forest of protein variants and catch a glimpse of the rules and effects underlying the topology of sequence space. In this review, we outline a complete theoretical journey through the processes of protein engineering methods such as directed evolution and rational design and reflect on these strategies and data-driven hybrid strategies in the context of sequence space. We discuss crucial phenomena of residue coevolution, such as epistasis, and review the history of models created over the past decade, aiming to infer rules of protein evolution from data and use this knowledge to improve the prediction of the structure–function relationship of proteins. Data-driven models based on deep learning algorithms are among the most promising methods that can account for the nonlinear phenomena of sequence space to some degree. We also critically discuss the available models to predict evolutionary coupling and epistatic effects (classical and deep learning) in terms of their capabilities and limitations. Finally, we present our perspective on possible future directions for developing data-driven approaches and provide key orientation points and necessities for the future of the fast-evolving field of enzyme engineering.
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