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Rescoring Docking Hit Lists for Model Cavity Sites: Predictions and Experimental Testing

机译:对模型腔部位的停靠命中清单进行记录:预测和实验测试

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摘要

Molecular docking computationally screens thousands to millions of organic molecules against protein structures, looking for those with complementary fits. Many approximations are made, often resulting in low “hit rates.” A strategy to overcome these approximations is to rescore top-ranked docked molecules using a better but slower method. One such is afforded by molecular mechanics–generalized Born surface area (MM– GBSA) techniques. These more physically realistic methods have improved models for solvation and electrostatic interactions and conformational change compared to most docking programs. To investigate MM–GBSA rescoring, we re-ranked docking hit lists in three small buried sites: a hydrophobic cavity that binds apolar ligands, a slightly polar cavity that binds aryl and hydrogen-bonding ligands, and an anionic cavity that binds cationic ligands. These sites are simple; consequently, incorrect predictions can be attributed to particular errors in the method, and many likely ligands may actually be tested. In retrospective calculations, MM–GBSA techniques with binding-site minimization better distinguished the known ligands for each cavity from the known decoys compared to the docking calculation alone. This encouraged us to test rescoring prospectively on molecules that ranked poorly by docking but that ranked well when rescored by MM– GBSA. A total of 33 molecules highly ranked by MM–GBSA for the three cavities were tested experimentally. Of these, 23 were observed to bind— these are docking false negatives rescued by rescoring. The 10 remaining molecules are true negatives by docking and false positives by MM–GBSA. X-ray crystal structures were determined for 21 of these 23 molecules. In many cases, the geometry prediction by MM–GBSA improved the initial docking pose and more closely resembled the crystallographic resu yet in several cases, the rescored geometry failed to capture large conformational changes in the protein. Intriguingly, rescoring not only rescued docking false positives, but also introduced several new false positives into the top-ranking molecules. We consider the origins of the successes and failures in MM–GBSA rescoring in these model cavity sites and the prospects for rescoring in biologically relevant targets.
机译:分子对接通过计算筛选出成千上万个针对蛋白质结构的有机分子,寻找具有互补性的有机分子。进行了许多近似,通常会导致“命中率”低。克服这些近似的一种策略是使用更好但更慢的方法对排名靠前的对接分子进行重新评分。分子力学通用的玻恩表面积(MM- GBSA)技术就是其中一种。与大多数对接程序相比,这些更实际的方法具有改进的溶剂化和静电相互作用以及构象变化模型。为了研究MM-GBSA的计分,我们在三个小的掩埋位点重新排列了对接命中列表的位置:一个与非极性配体结合的疏水腔,一个与芳基和氢键结合的配体结合的微极性腔以及一个与阳离子配体结合的阴离子腔。这些站点很简单;因此,错误的预测可以归因于该方法中的特定错误,并且可能实际测试了许多可能的配体。在回顾性计算中,与单独的对接计算相比,结合位点最小化的MM–GBSA技术更好地将每个腔体的已知配体与已知诱饵区分开。这鼓励我们对通过对接排名较差但在用MM– GBSA评分时排名较高的分子进行前瞻性测试。对三个腔体进行了MM-GBSA高度排名的总共33个分子的实验测试。在这些中,有23种被发现具有约束力-这些是通过计分挽救的对接假阴性。剩余的10个分子通过MM-GBSA对接而为真阴性,而假阳性为MM–GBSA。确定了这23个分子中的21个的X射线晶体结构。在许多情况下,MM–GBSA进行的几何预测可以改善初始对接姿势,并且与晶体学结果更相似。但是在某些情况下,重新绘制的几何图形无法捕获蛋白质中的大构象变化。有趣的是,计分不仅挽救了对接的假阳性现象,而且还将一些新的假阳性现象引入了排名最高的分子中。我们考虑了在这些模型腔位中进行MM-GBSA记录的成功与失败的起因,以及在生物学上相关的目标进行记录的前景。

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