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使用粗粒度模型的伞形采样快速计算蛋白质-蛋白质结合自由能

Fast Calculation of Protein-Protein Binding Free Energies Using Umbrella Sampling with a Coarse-Grained Model.

作者信息

Patel Jagdish Suresh, Ytreberg F Marty

机构信息

Center for Modeling Complex Interactions, University of Idaho , Moscow, Idaho 83844, United States.

Department of Physics, University of Idaho , Moscow, Idaho 83844, United States.

出版信息

J Chem Theory Comput. 2018 Feb 13;14(2):991-997. doi: 10.1021/acs.jctc.7b00660. Epub 2018 Jan 16.

Abstract

Determination of protein-protein binding affinity values is key to understanding various underlying biological phenomena, such as how missense variations change protein-protein binding. Most existing non-rigorous (fast) and rigorous (slow) methods that rely on all-atom representation of the proteins force the user to choose between speed and accuracy. In an attempt to achieve balance between speed and accuracy, we have combined rigorous umbrella sampling molecular dynamics simulation with a coarse-grained protein model. We predicted the effect of missense variations on binding affinity by selecting three protein-protein systems and comparing results to empirical relative binding affinity values and to non-rigorous modeling approaches. We obtained significant improvement both in our ability to discern stabilizing from destabilizing missense variations and in the correlation between predicted and experimental values compared to non-rigorous approaches. Overall our results suggest that using a rigorous affinity calculation method with coarse-grained protein models could offer fast and reliable predictions of protein-protein binding free energies.

摘要

确定蛋白质-蛋白质结合亲和力值是理解各种潜在生物学现象的关键,比如错义变异如何改变蛋白质-蛋白质结合。大多数现有的依赖蛋白质全原子表示的非严格(快速)和严格(慢速)方法,迫使用户在速度和准确性之间做出选择。为了在速度和准确性之间取得平衡,我们将严格的伞形采样分子动力学模拟与粗粒度蛋白质模型相结合。我们通过选择三个蛋白质-蛋白质系统,并将结果与经验相对结合亲和力值以及非严格建模方法进行比较,预测了错义变异对结合亲和力的影响。与非严格方法相比,我们在辨别稳定与不稳定错义变异的能力以及预测值与实验值之间的相关性方面都取得了显著改进。总体而言,我们的结果表明,使用带有粗粒度蛋白质模型的严格亲和力计算方法可以快速可靠地预测蛋白质-蛋白质结合自由能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af0/5813277/d8d42c5e5b64/ct-2017-00660m_0001.jpg

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