Muegge I, Martin Y C
Pharmaceutical Products Division, Abbott Laboratories, Abbott Park, Illinois 60064-6100, USA.
J Med Chem. 1999 Mar 11;42(5):791-804. doi: 10.1021/jm980536j.
A fast, simplified potential-based approach is presented that estimates the protein-ligand binding affinity based on the given 3D structure of a protein-ligand complex. This general, knowledge-based approach exploits structural information of known protein-ligand complexes extracted from the Brookhaven Protein Data Bank and converts it into distance-dependent Helmholtz free interaction energies of protein-ligand atom pairs (potentials of mean force, PMF). The definition of an appropriate reference state and the introduction of a correction term accounting for the volume taken by the ligand were found to be crucial for deriving the relevant interaction potentials that treat solvation and entropic contributions implicitly. A significant correlation between experimental binding affinities and computed score was found for sets of diverse protein-ligand complexes and for sets of different ligands bound to the same target. For 77 protein-ligand complexes taken from the Brookhaven Protein Data Bank, the calculated score showed a standard deviation from observed binding affinities of 1.8 log Ki units and an R2 value of 0.61. The best results were obtained for the subset of 16 serine protease complexes with a standard deviation of 1.0 log Ki unit and an R2 value of 0.86. A set of 33 inhibitors modeled into a crystal structure of HIV-1 protease yielded a standard deviation of 0.8 log Ki units from measured inhibition constants and an R2 value of 0.74. In contrast to empirical scoring functions that show similar or sometimes better correlation with observed binding affinities, our method does not involve deriving specific parameters that fit the observed binding affinities of protein-ligand complexes of a given training set. We compared the performance of the PMF score, Böhm's score (LUDI), and the SMOG score for eight different test sets of protein-ligand complexes. It was found that for the majority of test sets the PMF score performs best. The strength of the new approach presented here lies in its generality as no knowledge about measured binding affinities is needed to derive atomic interaction potentials. The use of the new scoring function in docking studies is outlined.
本文提出了一种快速、简化的基于势能的方法，该方法基于蛋白质-配体复合物的给定三维结构来估计蛋白质-配体的结合亲和力。这种基于知识的通用方法利用了从布鲁克海文蛋白质数据库中提取的已知蛋白质-配体复合物的结构信息，并将其转化为蛋白质-配体原子对的距离依赖型亥姆霍兹自由相互作用能（平均力势，PMF）。发现定义合适的参考状态以及引入一个校正项来考虑配体所占体积对于推导隐含处理溶剂化和熵贡献的相关相互作用势至关重要。对于不同的蛋白质-配体复合物集以及与同一靶点结合的不同配体集，实验结合亲和力与计算得分之间存在显著相关性。对于从布鲁克海文蛋白质数据库中选取的77个蛋白质-配体复合物，计算得分与观察到的结合亲和力的标准偏差为1.8个对数Ki单位，R2值为0.61。对于16个丝氨酸蛋白酶复合物的子集，获得了最佳结果，标准偏差为1.0个对数Ki单位，R2值为0.86。一组33种模拟到HIV-1蛋白酶晶体结构中的抑制剂，其计算得分与测量的抑制常数的标准偏差为0.8个对数Ki单位，R2值为0.74。与显示出与观察到的结合亲和力相似或有时更好相关性的经验评分函数不同，我们的方法不涉及推导适合给定训练集蛋白质-配体复合物观察到的结合亲和力的特定参数。我们比较了PMF评分、博姆评分（LUDI）和SMOG评分在八个不同蛋白质-配体复合物测试集上的性能。结果发现，对于大多数测试集，PMF评分表现最佳。本文提出的新方法的优势在于其通用性，因为推导原子相互作用势不需要关于测量结合亲和力的知识。文中概述了新评分函数在对接研究中的应用。
