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Computational prediction of protein–protein binding affinities
Wiley Interdisciplinary Reviews: Computational Molecular Science ( IF 16.8 ) Pub Date : 2019-10-29 , DOI: 10.1002/wcms.1448
Till Siebenmorgen 1 , Martin Zacharias 1
Affiliation  

Protein–protein interactions form central elements of almost all cellular processes. Knowledge of the structure of protein–protein complexes but also of the binding affinity is of major importance to understand the biological function of protein–protein interactions. Even weak transient protein–protein interactions can be of functional relevance for the cell during signal transduction or regulation of metabolism. The structure of a growing number of protein–protein complexes has been solved in recent years. Combined with docking approaches or template‐based methods, it is possible to generate structural models of many putative protein–protein complexes or to design new protein–protein interactions. In order to evaluate the functional relevance of putative or predicted protein–protein complexes, realistic binding affinity prediction is of increasing importance. Several computational tools ranging from simple force‐field or knowledge‐based scoring of single protein–protein complexes to ensemble‐based approaches and rigorous binding free energy simulations are available to predict relative and absolute binding affinities of complexes. With a focus on molecular mechanics force‐field approaches the present review aims at presenting an overview on available methods and discussing advantages, approximations, and limitations of the various methods.

中文翻译:

蛋白质-蛋白质结合亲和力的计算预测

蛋白质间相互作用是几乎所有细胞过程的核心要素。了解蛋白质-蛋白质复合物的结构以及结合亲和力对于了解蛋白质-蛋白质相互作用的生物学功能至关重要。在信号转导或代谢调节过程中,即使短暂的短暂蛋白质间相互作用也可能与细胞具有功能相关性。近年来,越来越多的蛋白质-蛋白质复合物的结构已得到解决。结合对接方法或基于模板的方法,可以生成许多推定的蛋白质-蛋白质复合物的结构模型或设计新的蛋白质-蛋白质相互作用。为了评估推定或预测的蛋白质-蛋白质复合物的功能相关性,现实的结合亲和力预测越来越重要。可以使用多种计算工具来预测复合物的相对和绝对结合亲和力,这些方法从简单的力场或基于知识的单一蛋白质-蛋白质复合物评分到基于整体的方法以及严格的结合自由能模拟。本文以分子力学力场方法为重点,旨在对可用方法进行概述,并讨论各种方法的优点,近似值和局限性。
更新日期:2019-10-29
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