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More efficient screening of protein-protein complex model structures for reducing the number of candidates

机译：更有效地筛选蛋白质 - 蛋白质复合模型结构，用于减少候选物的数量

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Rigid-body protein-protein docking is very efficient in generating tens of thousands of docked complex models (decoys) in a very short time without considering structure change upon binding, but typical docking scoring functions are not necessarily sufficiently accurate to narrow these decoys down to a small number of plausible candidates. Flexible refinements and sophisticated evaluation of the decoys are thus required to achieve more accurate prediction. Since this process is time-consuming, an efficient screening method to reduce the number of decoys is necessary immediately following rigid-body dockings. We attempted to develop an efficient screening method by clustering decoys generated by the rigid-body docking ZDOCK. We introduced the three metrics ligand-root-mean-square deviation (L-RMSD), interface-ligand-RMSD (iL-RMSD), and the fraction of common contacts (FCC), and examined various ranges of cut-offs for clusters to determine the best set of clustering parameters. Although the employed clustering algorithm is simple, it successfully reduced the number of decoys. Using iL-RMSD with a cut-off radius of 8 ?, the number of decoys that contain at least one near-native model with 90% probability decreased from 4,808 to 320, a 93% reduction in the original number of decoys. Using FCC for the clustering step, the top 1,000 success rates, defined as the probability that the top 1,000 models contain at least one near-native structure, reached 97%. We conclude that the proposed method is very efficient in selecting a small number of decoys that include near-native decoys.2019 ? The Biophysical Society of Japan.
机译：刚体蛋白 - 蛋白蛋白对接在很短的时间内产生数万个停靠的复杂模型（诱饵）而不考虑结构变化，但典型的对接得分功能并不一定是足够准确的，使这些诱饵缩小到下降到少数合理的候选人。因此需要灵活的改进和对诱饵的复杂评估来实现更准确的预测。由于该过程是耗时的，因此在刚体对接之后，需要一种有效的筛选方法来减少诱饵的数量。我们试图通过刚体对接Zdock产生的诱饵来开发一种有效的筛选方法。我们介绍了三个度量的配体 - 根均方偏差（L-RMSD），接口 - 配体-RMSD（IL-RMSD），以及常见触点（FCC）的一部分，并检查了簇的各种截止值确定最佳集群参数集。虽然采用的聚类算法很简单，但它成功减少了诱饵的数量。使用截止半径为8的IL-RMSD？，含有至少一个具有90％概率的近似模型的诱饵的数量从4,808降至320，诱饵数量减少93％。使用FCC进行聚类步骤，最高1,000个成功率，定义为前1,000型型号包含至少一个近天然结构的概率，达到97％。我们得出结论，该方法在选择包括近天然雕核的少量诱饵中是非常有效的。日本的生物物理学学会。

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