Statistical potential for modeling and ranking of protein-ligand interactions.
Statistical potential for modeling and ranking of protein-ligand interactions.
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DOI：
10.1021/ci200377u
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发表时间：
2011-12-27
影响因子：
5.6
通讯作者：
Sali A
中科院分区：
文献类型：
--
作者：
Fan H;Schneidman-Duhovny D;Irwin JJ;Dong G;Shoichet BK;Sali A
在结构生物学和药物化学中的应用需要蛋白质-配体评分函数用于两个不同的任务：（i）对蛋白质结合位点中的小分子的不同姿态进行排序;以及（ii）通过它们与蛋白质位点的互补性对不同的小分子进行排序。利用概率论，我们开发了两个原子距离依赖的统计评分函数：PoseScore优化识别天然结合几何形状的配体从其他姿势和RankScore优化区分配体从非结合分子。这两个分数都是基于蛋白质-配体复合物的8，885种晶体学结构，但在三个关键参数的值上有所不同。研究了影响评分准确性的因素，包括用于评分的最大原子距离和非天然配体几何形状，以及使用蛋白质模型代替晶体结构来训练和测试评分函数。对于19个靶标的测试集，RankScore分别改善了DOCK 3.6计算的13个和14个靶标的配体富集（logAUC）和早期富集（EF 1）评分。此外，RankScore在重新评分方面的表现优于其他七个测试的评分函数。接受晶体结构和诱饵几何形状与所有原子的根均方误差高达2 μ m的晶体结构作为正确的结合姿势，PoseScore给出了最好的分数，以正确的结合姿势之间的100个诱饵88%的所有情况下，在基准集包含100个蛋白质-配体复合物。PoseScore的准确性与DrugScoreCSD和ITScore/SE相当，并且上级其他12个测试的评分函数。因此，RankScore可以通过对目标与不同小分子的复合物进行排名来促进配体发现; PoseScore可以通过对给定蛋白质-配体对的不同构象进行排名来用于蛋白质-配体复合物结构预测。统计潜力可通过综合建模平台软件包（http：//salilab.org/imp/）和LigScore网络服务器（http：//salilab.org/ligscore/）获得。
Applications in structural biology and medicinal chemistry require protein-ligand scoring functions for two distinct tasks: (i) ranking different poses of a small molecule in a protein binding site; and (ii) ranking different small molecules by their complementarity to a protein site. Using probability theory, we developed two atomic distance-dependent statistical scoring functions: PoseScore was optimized for recognizing native binding geometries of ligands from other poses and RankScore was optimized for distinguishing ligands from nonbinding molecules. Both scores are based on a set of 8,885 crystallographic structures of protein-ligand complexes, but differ in the values of three key parameters. Factors influencing the accuracy of scoring were investigated, including the maximal atomic distance and non-native ligand geometries used for scoring, as well as the use of protein models instead of crystallographic structures for training and testing the scoring function. For the test set of 19 targets, RankScore improved the ligand enrichment (logAUC) and early enrichment (EF1) scores computed by DOCK 3.6 for 13 and 14 targets, respectively. In addition, RankScore performed better at rescoring than each of seven other scoring functions tested. Accepting both the crystal structure and decoy geometries with all-atom root-mean-square errors of up to 2 Å from the crystal structure as correct binding poses, PoseScore gave the best score to a correct binding pose among 100 decoys for 88% of all cases in a benchmark set containing 100 protein-ligand complexes. PoseScore accuracy is comparable to that of DrugScoreCSD and ITScore/SE, and superior to 12 other tested scoring functions. Therefore, RankScore can facilitate ligand discovery, by ranking complexes of the target with different small molecules; PoseScore can be used for protein-ligand complex structure prediction, by ranking different conformations of a given protein-ligand pair. The statistical potentials are available through the Integrative Modeling Platform (IMP) software package (http://salilab.org/imp/) and the LigScore web server (http://salilab.org/ligscore/).
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影响因子：
8
作者：
COLOVOS, C;YEATES, TO
通讯作者：
YEATES, TO
影响因子：
6.8
作者：
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作者：
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