Division of Immunology, Beckman Research Institute of the City of Hope , 1500 E Duarte Road, Duarte, California 91010, United States.
J Chem Inf Model. 2014 May 27;54(5):1391-400. doi: 10.1021/ci400750x. Epub 2014 May 7.
Protein-protein interactions are implicated in the pathogenesis of many diseases and are therefore attractive but challenging targets for drug design. One of the challenges in development is the identification of potential druggable binding sites in protein interacting interfaces. Identification of interface surfaces can greatly aid rational drug design of small molecules inhibiting protein-protein interactions. In this work, starting from the structure of a free monomer, we have developed a ligand docking based method, called "FindBindSite" (FBS), to locate protein-protein interacting interface regions and potential druggable sites in this interface. FindBindSite utilizes the results from docking a small and diverse library of small molecules to the entire protein structure. By clustering regions with the highest docked ligand density from FBS, we have shown that these high ligand density regions strongly correlate with the known protein-protein interacting surfaces. We have further predicted potential druggable binding sites on the protein surface using FBS, with druggability being defined as the site with high density of ligands docked. FBS shows a hit rate of 71% with high confidence and 93% with lower confidence for the 41 proteins used for predicting druggable binding sites on the protein-protein interface. Mining the regions of lower ligand density that are contiguous with the high scoring high ligand density regions from FBS, we were able to map 70% of the protein-protein interacting surface in 24 out of 41 structures tested. We also observed that FBS has limited sensitivity to the size and nature of the small molecule library used for docking. The experimentally determined hotspot residues for each protein-protein complex cluster near the best scoring druggable binding sites identified by FBS. These results validate the ability of our technique to identify druggable sites within protein-protein interface regions that have the maximal possibility of interface disruption.
蛋白质-蛋白质相互作用与许多疾病的发病机制有关，因此是药物设计有吸引力但具有挑战性的目标。开发过程中的一个挑战是鉴定蛋白质相互作用界面中潜在的可成药结合位点。鉴定界面表面可以极大地帮助小分子抑制蛋白质-蛋白质相互作用的合理药物设计。在这项工作中，我们从自由单体的结构出发，开发了一种基于配体对接的方法，称为“FindBindSite”（FBS），用于定位蛋白质-蛋白质相互作用界面区域和该界面中的潜在可成药位点。FindBindSite 利用将小分子的小而多样的文库对接至整个蛋白质结构的结果。通过从 FBS 聚类具有最高对接配体密度的区域，我们表明这些高配体密度区域与已知的蛋白质-蛋白质相互作用表面强烈相关。我们还使用 FBS 预测了蛋白质表面上的潜在可成药结合位点，将成药性定义为具有高对接配体密度的位点。对于用于预测蛋白质-蛋白质界面上可成药结合位点的 41 个蛋白质，FBS 的命中率为 71%（置信度高）和 93%（置信度低）。从 FBS 的高得分高配体密度区域中挖掘与低配体密度区域连续的区域，我们能够映射 41 个测试结构中的 24 个蛋白质-蛋白质相互作用表面的 70%。我们还观察到 FBS 对用于对接的小分子文库的大小和性质的灵敏度有限。每个蛋白质-蛋白质复合物的实验确定的热点残基聚类在 FBS 确定的最佳得分可成药结合位点附近。这些结果验证了我们的技术在识别蛋白质-蛋白质界面区域内具有最大界面破坏可能性的成药位点的能力。
