Institut de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain.
Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluis Companys 23, Barcelona 08010, Spain.
J Chem Inf Model. 2020 Mar 23;60(3):1644-1651. doi: 10.1021/acs.jcim.9b01062. Epub 2020 Feb 28.
The prediction of a ligand's binding mode into its macromolecular target is essential in structure-based drug discovery. Even though tremendous effort has been made to address this problem, most of the developed tools work similarly, trying to predict the binding free energy associated with each particular binding mode. In this study, we decided to abandon this criterion, following structural stability instead. This view, implemented in a novel computational workflow, quantifies the steepness of the local energy minimum associated with each potential binding mode. Surprisingly, the protocol outperforms docking scoring functions in case of fragments (ligands with MW < 300 Da) and is as good as docking for drug-like molecules. It also identifies substructures that act as structural anchors, predicting their binding mode with particular accuracy. The results open a new physical perspective for binding mode prediction, which can be combined with existing thermodynamic-based approaches.
配体与生物大分子靶标结合模式的预测是基于结构的药物发现中的关键步骤。尽管已经付出了巨大的努力来解决这个问题，但大多数开发的工具的工作原理相似，试图预测与每种特定结合模式相关的结合自由能。在这项研究中，我们决定放弃这个标准，转而遵循结构稳定性。这种观点在一个新的计算工作流程中得到了实现，该流程量化了与每个潜在结合模式相关的局部能量最小值的陡峭程度。令人惊讶的是，该方案在片段（分子量 < 300 Da 的配体）的情况下优于对接评分函数，并且与药物样分子的对接一样好。它还可以识别作为结构锚的亚结构，以特别高的精度预测它们的结合模式。这些结果为结合模式预测开辟了一个新的物理视角，可以与现有的基于热力学的方法相结合。
