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蛋白质结构预测:通过添加进化约束使AWSEM成为AWSEM-ER

Protein structure prediction: making AWSEM AWSEM-ER by adding evolutionary restraints.

作者信息

Sirovetz Brian J, Schafer Nicholas P, Wolynes Peter G

机构信息

Center for Theoretical Biological Physics, Rice University, Houston, Texas.

Department of Chemistry, Rice University, Houston, Texas.

出版信息

Proteins. 2017 Nov;85(11):2127-2142. doi: 10.1002/prot.25367. Epub 2017 Aug 27.

Abstract

Protein sequences have evolved to fold into functional structures, resulting in families of diverse protein sequences that all share the same overall fold. One can harness protein family sequence data to infer likely contacts between pairs of residues. In the current study, we combine this kind of inference from coevolutionary information with a coarse-grained protein force field ordinarily used with single sequence input, the Associative memory, Water mediated, Structure and Energy Model (AWSEM), to achieve improved structure prediction. The resulting Associative memory, Water mediated, Structure and Energy Model with Evolutionary Restraints (AWSEM-ER) yields a significant improvement in the quality of protein structure prediction over the single sequence prediction from AWSEM when a sufficiently large number of homologous sequences are available. Free energy landscape analysis shows that the addition of the evolutionary term shifts the free energy minimum to more native-like structures, which explains the improvement in the quality of structures when performing predictions using simulated annealing. Simulations using AWSEM without coevolutionary information have proved useful in elucidating not only protein folding behavior, but also mechanisms of protein function. The success of AWSEM-ER in de novo structure prediction suggests that the enhanced model opens the door to functional studies of proteins even when no experimentally solved structures are available.

摘要

蛋白质序列已经进化到能够折叠成功能结构,从而形成了各种蛋白质序列家族,这些家族都具有相同的整体折叠结构。人们可以利用蛋白质家族序列数据来推断残基对之间可能的接触。在当前的研究中,我们将这种从协同进化信息中得出的推断与通常用于单序列输入的粗粒度蛋白质力场——关联记忆、水介导、结构与能量模型(AWSEM)相结合,以实现改进的结构预测。当有足够数量的同源序列时,由此产生的带有进化约束的关联记忆、水介导、结构与能量模型(AWSEM-ER)在蛋白质结构预测质量方面比仅基于AWSEM的单序列预测有显著提高。自由能景观分析表明,进化项的加入将自由能最小值转移到更类似天然的结构上,这解释了在使用模拟退火进行预测时结构质量的提高。使用没有协同进化信息的AWSEM进行的模拟已被证明不仅有助于阐明蛋白质折叠行为,还能揭示蛋白质功能机制。AWSEM-ER在从头结构预测方面的成功表明,即使没有实验解析的结构,这种增强模型也为蛋白质功能研究打开了大门。

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