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.

中文翻译：

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开放结合姿势元动力学：一种对蛋白质-配体结合姿势进行排序的有效方法

预测配体与蛋白质结合的正确姿势及其相关的结合亲和力在计算机辅助药物发现中非常重要。为此，已经开发了许多方法，从广泛使用的快速分子对接到计算量大的增强采样分子模拟。在这种情况下，粗粒度元动力学和结合姿态元动力学 (BPMD) 等方法使用具有元动力学偏置的模拟来探测结合亲和力，而不试图完全收敛结合自由能景观以降低计算成本。在 BPMD 中，元动力学偏差扰动配体远离初始姿势。配体对这种偏差的抵抗力用于计算稳定性评分。该方法已被证明可用于重新排列来自对接的预测绑定姿势。在这里，我们介绍了 OpenBPMD，这是 BPMD 的开源 Python 重新实现和重新解释。OpenBPMD 由 OpenMM 模拟引擎提供支持，并使用经过修订的评分函数。该算法通过在广泛的目标上进行测试并显示其匹配或超过原始 BPMD 的性能来验证。我们还调查了准确的水定位对算法性能的作用，并展示了与大规范蒙特卡罗算法的结合如何提高预测的准确性。该算法通过在广泛的目标上进行测试并显示其匹配或超过原始 BPMD 的性能来验证。我们还调查了准确的水定位对算法性能的作用，并展示了与大规范蒙特卡罗算法的结合如何提高预测的准确性。该算法通过在广泛的目标上进行测试并显示其匹配或超过原始 BPMD 的性能来验证。我们还调查了准确的水定位对算法性能的作用，并展示了与大规范蒙特卡罗算法的结合如何提高预测的准确性。