Kumar Anmol, Goel Himanshu, Yu Wenbo, Zhao Mingtian, MacKerell Alexander D
Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn Street, HSF II, Baltimore, Maryland 21201, United States.
J Chem Theory Comput. 2024 Dec 24;20(24):11032-11048. doi: 10.1021/acs.jctc.4c01165. Epub 2024 Dec 5.
Appropriate treatment of water contributions to protein-ligand interactions is a very challenging problem in the context of adequately determining the number of waters to investigate and undertaking conformational sampling of the ligands, the waters, and the surrounding protein. In the present study, an extension of the Site Identification by Ligand Competitive Saturation-Monte Carlo (SILCS-MC) docking approach is presented that enables the determination of the location of water molecules in the binding pocket and their impact on the predicted ligand binding orientation and affinities. The approach, termed SILCS-WATER, involves MC sampling of the ligand along with explicit water molecules in a binding site followed by selection of a subset of waters within specified energetic and distance cutoffs that contribute to ligand binding and orientation. To allow for convergence of both the water and ligand orientations, SILCS-WATER is based on just the overlap of the ligand and water with the SILCS FragMaps and the interaction energy between the waters and ligand. Results show that the SILCS-WATER methodology can capture important waters and improve ligand binding orientations. For 6 of 10 multiple ligand-protein systems, the method improved relative binding affinity prediction against experimental results, with substantial improvements in five systems, when compared to standard SILCS-MC. Improved reproduction of crystallographic ligand binding orientations is shown to be an indicator of when SILCS-WATER will yield improved binding affinity correlations. The method also identifies waters interacting with ligands that occupy unfavorable locations with respect to the protein whose displacement through the appropriate ligand modifications should improve ligand binding affinity. Results are consistent with the binding affinity being modeled as a ligand-water complex interacting with the protein. The presented approach offers new possibilities in revealing water networks and their contributions to the binding orientation and affinity of a ligand for a protein and is anticipated to be of utility for computer-aided drug design.
在充分确定要研究的水分子数量以及对配体、水分子和周围蛋白质进行构象采样的背景下,恰当处理水分子对蛋白质 - 配体相互作用的贡献是一个极具挑战性的问题。在本研究中,提出了一种配体竞争饱和 - 蒙特卡罗(SILCS - MC)对接方法的扩展,即SILCS - WATER,它能够确定结合口袋中水分子的位置及其对预测的配体结合方向和亲和力的影响。该方法包括在结合位点对配体以及明确的水分子进行蒙特卡罗采样,然后在指定的能量和距离截止值内选择有助于配体结合和方向的水分子子集。为了使水分子和配体方向都能收敛,SILCS - WATER仅基于配体和水分子与SILCS片段图谱的重叠以及水分子和配体之间的相互作用能。结果表明,SILCS - WATER方法能够捕捉重要的水分子并改善配体结合方向。对于10个多配体 - 蛋白质系统中的6个,与标准的SILCS - MC相比,该方法改进了相对于实验结果的相对结合亲和力预测,在5个系统中有显著改进。晶体学配体结合方向的更好重现被证明是SILCS - WATER能够产生改进的结合亲和力相关性的一个指标。该方法还识别出与配体相互作用的水分子,这些水分子相对于蛋白质占据不利位置,通过适当的配体修饰将其取代应该可以提高配体结合亲和力。结果与将结合亲和力建模为与蛋白质相互作用的配体 - 水复合物一致。所提出的方法为揭示水网络及其对配体与蛋白质结合方向和亲和力的贡献提供了新的可能性,预计对计算机辅助药物设计有用。