Free energies for coarse-grained proteins by integrating multibody statistical contact potentials with entropies from elastic network models.

Michael T Zimmermann, Sumudu P Leelananda, Pawel Gniewek, Yaping Feng, Robert L Jernigan, Andrzej Kloczkowski
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引用次数: 19

Abstract

We propose a novel method of calculation of free energy for coarse grained models of proteins by combining our newly developed multibody potentials with entropies computed from elastic network models of proteins. Multi-body potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Combining four-body non-sequential, four-body sequential and pairwise short range potentials with optimized weights for each term, our coarse-grained potential improved recognition of native structure among misfolded decoys, outperforming all other contact potentials for CASP8 decoy sets and performance comparable to the fully atomic empirical DFIRE potentials. By combing statistical contact potentials with entropies from elastic network models of the same structures we can compute free energy changes and improve coarse-grained modeling of protein structure and dynamics. The consideration of protein flexibility and dynamics should improve protein structure prediction and refinement of computational models. This work is the first to combine coarse-grained multibody potentials with an entropic model that takes into account contributions of the entire structure, investigating native-like decoy selection.

利用弹性网络模型的熵与多体统计接触势的积分计算粗粒蛋白质的自由能。
我们提出了一种计算粗粒度蛋白质模型自由能的新方法,该方法将我们新开发的多体势与蛋白质弹性网络模型计算的熵相结合。多体势最近引起了很大的兴趣,因为它们考虑了与残基包装相关的三维相互作用,并捕获了这些相互作用在蛋白质结构中的协同性。将四体非序列、四体序列和配对短程势与每个项的优化权值相结合,我们的粗粒度势提高了对错误折叠诱饵的天然结构的识别,优于CASP8诱饵集的所有其他接触势,性能可与全原子经验DFIRE势相媲美。通过将统计接触势与相同结构的弹性网络模型的熵相结合,可以计算自由能的变化,从而改进蛋白质结构和动力学的粗粒度建模。考虑到蛋白质的灵活性和动态性可以改善蛋白质结构的预测和计算模型的改进。这项工作是第一次将粗粒度多体势与熵模型结合起来,该模型考虑了整个结构的贡献,研究了原生诱饵选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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