Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses

Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.

预测配体（ligand）与蛋白质（protein）结合的正确构象及其相关结合亲和力（binding affinity），在计算机辅助药物发现（computer-aided drug discovery）领域具有重要意义。为此已开发出多种方法，从广泛使用的快速分子对接（molecular docking），到计算成本高昂的增强采样分子模拟（enhanced sampling molecular simulations）。在此背景下，粗粒度元动力学（coarse-grained metadynamics）与结合构象元动力学（binding pose metadynamics, BPMD）等方法采用带有元动力学偏置（metadynamics bias）的模拟来探究结合亲和力，无需完全收敛结合自由能景观（binding free energy landscape）以降低计算成本。在BPMD中，元动力学偏置会将配体从初始构象中扰动开来，通过配体对该偏置的抵抗程度计算稳定性评分（stability score）。该方法已被证实可有效对分子对接得到的预测结合构象进行重排序。本研究推出了OpenBPMD——一种对BPMD的开源Python重实现与重新诠释版本。OpenBPMD基于OpenMM模拟引擎（OpenMM simulation engine）开发，并采用了修订后的评分函数。我们通过在大量靶标上进行测试验证了该算法的有效性，证实其性能达到甚至超越了原始BPMD。此外，我们还探究了精准水分子定位对该算法性能的影响，并证实结合正则系综蒙特卡洛（grand-canonical Monte Carlo）算法可提升预测准确性。