Protein–ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein–ligand system, solvent‐mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein–ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P‐score. This is illustrated using six different protein–ligand targets as case studies.

中文翻译：

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使用 P 分数排名准确预测动态蛋白质-配体结合

蛋白质-配体结合预测通常依赖于对接方法和相关的评分函数来提出配体在生物靶标中的结合模式。这种方法面临着重大挑战，包括蛋白质-配体系统的灵活性、溶剂介导的相互作用以及相关的熵变化。此外，由于计算熵和熵结合相互作用所需的时间很短，因此评分函数的准确度很弱。这里描述的工作流程试图通过将监督分子动力学与动态平均量子力学碎片分子轨道相结合来解决这些限制。这种组合显着提高了预测蛋白质-配体复合物实验结合结构的能力，而与配体的起始位置或结合位点构象无关。我们发现，通过将停留时间和相互作用能量作为描述符结合到名为 的新颖评分函数中，可以增强预测能力。P-分数。使用六种不同的蛋白质配体靶标作为案例研究来说明这一点。