Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States.
J Chem Inf Model. 2023 Aug 14;63(15):4772-4779. doi: 10.1021/acs.jcim.3c00416. Epub 2023 Jul 18.
Molecular docking is a preferred method to predict ligand binding modes and their binding energy to target protein receptors, which is critical in early phase structure-based drug discovery. However, there is a persistent challenge in docking that can be attributed to the induced fit effect, as receptor binding sites undergo induced fit conformational changes upon ligand binding to achieve better binding modes. In this work, based on CHARMM-GUI and , we present a straightforward CHARMM-GUI induced fit docking (CGUI-IFD) workflow to generate reliable protein-ligand binding modes. The CGUI-IFD workflow generates an ensemble of receptor binding site conformations through ligand-binding site (LBS) refinement, runs rigid receptor docking, and performs high-throughput molecular dynamics (MD) simulations of protein-ligand complex structures in explicit solvents. The results are evaluated based on the ligand root-mean-square deviation (RMSD)-based binding stability and the molecular mechanics generalized Born surface area binding energy. For a benchmark test, we used 258 cross-docking protein-ligand pairs across 41 target proteins from the Schrodinger IFD-MD data set. The application of CGUI-IFD on this data set shows 80% success rate (within 2.5 Å RMSD from the experimental structures). We expect that the CGUI-IFD workflow can be useful to generate reliable ligand binding modes for cross-docking cases.
分子对接是预测配体结合模式及其与靶蛋白受体结合能的首选方法，这在基于结构的药物发现的早期阶段至关重要。然而，对接中存在一个持久的挑战，这可以归因于诱导契合效应，因为受体结合位点在配体结合时会发生诱导契合构象变化，以实现更好的结合模式。在这项工作中，基于 CHARMM-GUI 和 ，我们提出了一种简单的 CHARMM-GUI 诱导契合对接（CGUI-IFD）工作流程，以生成可靠的蛋白质-配体结合模式。CGUI-IFD 工作流程通过配体结合位点（LBS）精化生成受体结合位点构象的集合，运行刚性受体对接，并在显式溶剂中对蛋白质-配体复合物结构进行高通量分子动力学（MD）模拟。结果基于配体均方根偏差（RMSD）结合稳定性和分子力学广义 Born 表面面积结合能进行评估。作为基准测试，我们使用了来自 Schrodinger IFD-MD 数据集的 41 个靶蛋白的 258 个交叉对接蛋白质-配体对。CGUI-IFD 在该数据集上的应用显示出 80%的成功率（与实验结构的 RMSD 在 2.5 Å 以内）。我们期望 CGUI-IFD 工作流程可用于生成可靠的交叉对接情况下的配体结合模式。
