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

机译:蛋白质 - 蛋白对接中侧链构象的详尽搜索的计算可行性

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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. (C) 2018 Wiley Periodicals, Inc.
机译:蛋白质 - 蛋白酶对接程序通常进行蛋白质相对位置的全局扫描,然后进行局部匹配的局部细化。由于搜索空间的大小,全局扫描通常使用计算廉价的分子能量逼近来实现为刚体搜索。足够的细化必须考虑到结构性灵活性。由于细化执行了相互作用蛋白的构象搜索,因此鉴于巨大的内部自由度,它是极其计算的具有挑战性。不同的方法通过将搜索限制到侧链,旋转态,粗粒结构表示,主正常模式等,限制搜索空间。尽管如此,即使是近似,由于剩余的剩余自由度,细化也会提出极端的计算挑战。鉴于搜索空间的复杂性,穷举搜索的优势是显而易见的。这种搜索的障碍是计算可行性。然而,现代计算机的增长计算能力,尤其是由于具有大量专用计算核的图形处理单元(GPU)的利用率越来越多,延伸了布鲁斯搜索方法的适用范围。该概念证明研究表明了蛋白质堆积中侧链构象的详尽搜索的计算可行性。在GPU架构上实施的程序用于在大型代表性组蛋白质复合物中产生最佳构象。 (c)2018 Wiley期刊,Inc。

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