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采用经验推导的评分函数,通过小分子与已知结构的蛋白质的灵活对接,对推定的蛋白质-配体相互作用进行排名。Surflex采用的评分功能是纯粹基于阳性数据开发的,包括具有已知结合亲和力的非共价蛋白质-配体复合物。因此,对于不正确的相互作用的评分函数项在参数估计中几乎没有权重,并且采用了一种避免蛋白质-配体相互渗透的临时方案。我们提出了一种通用方法,用于合并综合生成的负训练数据,该方法可以对所有评分函数参数进行严格估计。在新的参数化下,几何对接精度仍然非常好。此外,涵盖29种蛋白质和相应配体组的筛选效用测试显示出改进的性能。在超过80% 的情况下,非配体上真正配体的最大富集超过20倍,在超过50% 的情况下,富集超过100倍。

+1
+2
100研值 100研值 ¥99课程
检索文献一次
下载文献一次

去下载>

成功解锁2个技能,为你点赞

《SCI写作十大必备语法》
解决你的SCI语法难题!

技能熟练度+1

视频课《玩转文献检索》
让你成为检索达人!

恭喜完成新手挑战

手机微信扫一扫,添加好友领取

免费领《Endnote文献管理工具+教程》

微信扫码, 免费领取

手机登录

获取验证码
登录