Surflex-Dock employs an empirically derived scoring function to rank putative protein-ligand interactions by flexible docking of small molecules to proteins of known structure. The scoring function employed by Surflex was developed purely on the basis of positive data, comprising noncovalent protein-ligand complexes with known binding affinities. Consequently, scoring function terms for improper interactions received little weight in parameter estimation, and an ad hoc scheme for avoiding protein-ligand interpenetration was adopted. We present a generalized method for incorporating synthetically generated negative training data, which allows for rigorous estimation of all scoring function parameters. Geometric docking accuracy remained excellent under the new parametrization. In addition, a test of screening utility covering a diverse set of 29 proteins and corresponding ligand sets showed improved performance. Maximal enrichment of true ligands over nonligands exceeded 20-fold in over 80% of cases, with enrichment of greater than 100-fold in over 50% of cases.

译文

:Surflex-Dock利用经验得出的评分功能,通过将小分子灵活地对接至已知结构的蛋白质来对推定的蛋白质-配体相互作用进行排名。 Surflex所采用的评分功能纯粹是根据积极数据开发的,包括具有已知结合亲和力的非共价蛋白-配体复合物。因此,对于不适当的相互作用的评分功能项在参数估计中的权重很小,并且采用了避免蛋白质-配体互穿的特设方案。我们提出了一种综合方法来综合生成负训练数据,该方法可以对所有评分功能参数进行严格估计。在新的参数化下,几何对接精度仍然保持出色。此外,一项涵盖多种29种蛋白质和相应配体的筛选效用测试表明,其性能得到了改善。超过80%的案例中,真正的配体比非配体的最大富集超过20倍,超过50%的案例中,富集超过100倍。

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