Suriana Patricia, Dror Ron O
Department of Computer Science, Stanford University.
ArXiv. 2023 Nov 30:arXiv:2312.00191v1.
Deep learning promises to dramatically improve scoring functions for molecular docking, leading to substantial advances in binding pose prediction and virtual screening. To train scoring functions-and to perform molecular docking-one must generate a set of candidate ligand binding poses. Unfortunately, the sampling protocols currently used to generate candidate poses frequently fail to produce any poses close to the correct, experimentally determined pose, unless information about the correct pose is provided. This limits the accuracy of learned scoring functions and molecular docking. Here, we describe two improved protocols for pose sampling: GLOW (auGmented sampLing with sOftened vdW potential) and a novel technique named IVES (IteratiVe Ensemble Sampling). Our benchmarking results demonstrate the effectiveness of our methods in improving the likelihood of sampling accurate poses, especially for binding pockets whose shape changes substantially when different ligands bind. This improvement is observed across both experimentally determined and AlphaFold-generated protein structures. Additionally, we present datasets of candidate ligand poses generated using our methods for each of around 5,000 protein-ligand cross-docking pairs, for training and testing scoring functions. To benefit the research community, we provide these cross-docking datasets and an open-source Python implementation of GLOW and IVES at https://github.com/drorlab/GLOW_IVES.
深度学习有望显著改进分子对接的评分函数，从而在结合姿态预测和虚拟筛选方面取得重大进展。为了训练评分函数并进行分子对接，必须生成一组候选配体结合姿态。不幸的是，目前用于生成候选姿态的采样协议常常无法产生任何接近正确的、通过实验确定的姿态，除非提供有关正确姿态的信息。这限制了学习到的评分函数和分子对接的准确性。在此，我们描述了两种改进的姿态采样协议：GLOW（具有软化范德华势的增强采样）和一种名为IVES（迭代集合采样）的新技术。我们的基准测试结果证明了我们的方法在提高采样准确姿态可能性方面的有效性，特别是对于不同配体结合时形状变化很大的结合口袋。在通过实验确定的和由AlphaFold生成的蛋白质结构中均观察到了这种改进。此外，我们提供了使用我们的方法为大约5000个蛋白质-配体交叉对接对中的每一对生成的候选配体姿态数据集，用于训练和测试评分函数。为了使研究界受益，我们在https://github.com/drorlab/GLOW_IVES上提供了这些交叉对接数据集以及GLOW和IVES的开源Python实现。
