State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China.
College of Chemistry, Zhengzhou University, Zhengzhou 450001, P. R. China.
J Chem Inf Model. 2022 Sep 26;62(18):4369-4379. doi: 10.1021/acs.jcim.2c00916. Epub 2022 Sep 9.
Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to improve the prediction performance of existing ML-based SFs, we estimated the water distribution with a HydraMap (HM) method and then incorporated the features extracted from protein-bound waters obtained in this way into three ML-based SFs: RF-Score, ECIF, and PLEC. It was found that a combination of HM-based features can consistently improve the performance of all three SFs, including their scoring, ranking, and docking power. HydraMap-based features show consistently good performance with both crystal structures and docked structures, demonstrating their robustness for SFs. Overall, HM-based features, which are a statistical representation of hydration sites at protein-ligand interfaces, are expected to improve the prediction performance for diverse SFs.
水在配体-蛋白界面的分子在配体的结合中起着至关重要的作用，但在许多目前使用的基于机器学习（ML）的评分函数（SF）中，蛋白质结合水的行为在很大程度上被忽视了。为了提高现有基于 ML 的 SF 的预测性能，我们使用 HydraMap（HM）方法估计了水的分布，然后将从这种方式获得的蛋白质结合水中提取的特征纳入三种基于 ML 的 SF 中：RF-Score、ECIF 和 PLEC。结果发现，HM 特征的组合可以一致地提高所有三种 SF 的性能，包括它们的评分、排名和对接能力。基于 HM 的特征在晶体结构和对接结构上都表现出一致的良好性能，证明了它们对 SF 的稳健性。总的来说，基于 HM 的特征是蛋白质-配体界面水合位点的统计表示，有望提高各种 SF 的预测性能。
