Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.
NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China.
J Chem Inf Model. 2022 May 23;62(10):2499-2509. doi: 10.1021/acs.jcim.1c01537. Epub 2022 Apr 22.
The protein-ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, π-stacking, π-cation, and metal-ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.
蛋白质配体评分函数在计算机辅助药物发现中起着重要作用，在虚拟筛选和先导化合物优化中被广泛应用。在这项研究中，我们开发了一种新的经验蛋白质配体评分函数，具有针对氢键、范德华力和静电相互作用的氨基酸特异性相互作用分量。此外，新的评分函数还包括疏水、π-堆积、π-阳离子和金属配体相互作用。为了更好地评估 AA-Score 的性能，我们分别生成了几个新的测试集，用于评分、排序和对接性能的评估。广泛的测试表明，与其他广泛使用的传统评分函数相比，AA-Score 在评分、对接和排序方面表现良好。AA-Score 的性能提升得益于将单个相互作用分解为氨基酸特异性类型。为了便于应用，我们开发了一个易于使用的工具，用于分析蛋白质-配体相互作用指纹，并使用 AA-Score 预测结合亲和力。源代码和相关运行示例可在 https://github.com/xundrug/AA-Score-Tool 上找到。
