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蛋白质-蛋白质对接中侧链构象穷举搜索的计算可行性。

Computational Feasibility of an Exhaustive Search of Side-Chain Conformations in Protein-Protein Docking.

机构信息

Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047.

Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047.

出版信息

J Comput Chem. 2018 Sep 15;39(24):2012-2021. doi: 10.1002/jcc.25381. Epub 2018 Sep 18.

Abstract

Protein-protein docking procedures typically perform the global scan of the proteins relative positions, followed by the local refinement of the putative matches. Because of the size of the search space, the global scan is usually implemented as rigid-body search, using computationally inexpensive intermolecular energy approximations. An adequate refinement has to take into account structural flexibility. Since the refinement performs conformational search of the interacting proteins, it is extremely computationally challenging, given the enormous amount of the internal degrees of freedom. Different approaches limit the search space by restricting the search to the side chains, rotameric states, coarse-grained structure representation, principal normal modes, and so on. Still, even with the approximations, the refinement presents an extreme computational challenge due to the very large number of the remaining degrees of freedom. Given the complexity of the search space, the advantage of the exhaustive search is obvious. The obstacle to such search is computational feasibility. However, the growing computational power of modern computers, especially due to the increasing utilization of Graphics Processing Unit (GPU) with large amount of specialized computing cores, extends the ranges of applicability of the brute-force search methods. This proof-of-concept study demonstrates computational feasibility of an exhaustive search of side-chain conformations in protein pocking. The procedure, implemented on the GPU architecture, was used to generate the optimal conformations in a large representative set of protein-protein complexes. © 2018 Wiley Periodicals, Inc.

摘要

蛋白质-蛋白质对接程序通常执行蛋白质相对位置的全局扫描,然后对假定的匹配进行局部细化。由于搜索空间的大小,全局扫描通常采用刚体搜索,使用计算成本低廉的分子间能量近似。充分的细化必须考虑到结构的灵活性。由于细化执行相互作用蛋白质的构象搜索,因此考虑到大量的内部自由度,这在计算上极具挑战性。不同的方法通过将搜索限制在侧链、旋转异构体状态、粗粒度结构表示、主正则模态等来限制搜索空间。尽管有这些近似,由于剩余自由度的数量非常大,细化仍然带来了极端的计算挑战。鉴于搜索空间的复杂性,穷举搜索的优势显而易见。这种搜索的障碍是计算可行性。然而,现代计算机的计算能力不断提高,特别是由于大量专用计算核心的图形处理单元(GPU)的使用不断增加,扩展了穷举搜索方法的适用范围。这项概念验证研究证明了蛋白质口袋中侧链构象穷举搜索的计算可行性。该程序在 GPU 架构上实现,用于在大量具有代表性的蛋白质-蛋白质复合物集合中生成最佳构象。 © 2018 威利父子公司

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本文引用的文献

1
Deterministic Search Methods for Computational Protein Design.
Methods Mol Biol. 2017;1529:107-123. doi: 10.1007/978-1-4939-6637-0_4.
2
Modeling complexes of modeled proteins.
Proteins. 2017 Mar;85(3):470-478. doi: 10.1002/prot.25183. Epub 2016 Oct 24.
3
The impact of side-chain packing on protein docking refinement.
J Chem Inf Model. 2015 Apr 27;55(4):872-81. doi: 10.1021/ci500380a. Epub 2015 Mar 24.
4
Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures.
BMC Syst Biol. 2015;9 Suppl 1(Suppl 1):S6. doi: 10.1186/1752-0509-9-S1-S6. Epub 2015 Jan 21.
5
Protein-protein docking: from interaction to interactome.
Biophys J. 2014 Oct 21;107(8):1785-1793. doi: 10.1016/j.bpj.2014.08.033.
6
MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers.
Bioinformatics. 2014 Nov 15;30(22):3281-3. doi: 10.1093/bioinformatics/btu532. Epub 2014 Aug 6.
7
An efficient parallel algorithm for accelerating computational protein design.
Bioinformatics. 2014 Jun 15;30(12):i255-i263. doi: 10.1093/bioinformatics/btu264.
8
Parallel implementation of 3D protein structure similarity searches using a GPU and the CUDA.
J Mol Model. 2014 Feb;20(2):2067. doi: 10.1007/s00894-014-2067-1. Epub 2014 Jan 31.
10
A new framework for computational protein design through cost function network optimization.
Bioinformatics. 2013 Sep 1;29(17):2129-36. doi: 10.1093/bioinformatics/btt374. Epub 2013 Jul 10.

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