General and targeted statistical potentials for protein-ligand interactions
General and targeted statistical potentials for protein-ligand interactions
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DOI：
10.1002/prot.20588
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发表时间：
2005-11-01
影响因子：
2.9
通讯作者：
Verdonk, ML
中科院分区：
文献类型：
--
作者：
Mooij, WTM;Verdonk, ML
我们提出了一种新的原子-原子势，该势来自蛋白质-配体复合体的数据库。首先，我们澄清了文献中描述的两个统计势PMF和Drugcore之间的异同。我们强调了由一个在其参考状态中未被解释的重要因素造成的缺点，并描述了一个新的势，我们将其命名为ASTEX统计势(ASP)。ASP的参考状态考虑了蛋白质原子类型向配体结合位点暴露的差异。我们表明，这种新的势预测结合亲和力的精度类似于GoldScore和ChemScore。我们通过构造两个仅在这方面不同于ASP的额外统计势来研究参考态选择的影响。这两个势中的参考态是沿着Drugcore和PMF的线定义的。在对接实验中，使用为ASP提出的新参考状态的潜力比使用这些文献参考状态时的成功率更高；使用ASP获得了类似于已建立的评分函数GoldScore和ChemScore的成功率。这种情况既适用于蛋白质-配体结构的大型、通用验证集，也适用于针对四个药物相关靶点的活性物质的小测试集。对这些目标的虚拟筛选实验表明，在浓缩方面，不同参考状态之间的差别较小。此外，我们还描述了如何使用统计势来构建目标得分函数。给出了CDK2的例子，使用了四种不同的目标评分函数，偏向于越来越大的目标特定数据库。使用这些有针对性的评分函数，对接成功率以及丰富明显好于一般的ASP评分函数。结果随着构建目标得分函数所使用的结构数量的增加而改善，从而说明随着新的结构数据的获得，这些目标ASP的潜力可以不断提高。
We present a novel atom-atom potential derived from a database of protein-ligand complexes. First, we clarify the similarities and differences between two statistical potentials described in the literature, PMF and Drugscore. We highlight shortcomings caused by an important factor unaccounted for in their reference states, and describe a new potential, which we name the Astex Statistical Potential (ASP). ASP's reference state considers the difference in exposure of protein atom types towards ligand binding sites. We show that this new potential predicts binding affinities with an accuracy similar to that of Goldscore and Chemscore. We investigate the influence of the choice of reference state by constructing two additional statistical potentials that differ from ASP only in this respect. The reference states in these two potentials are defined along the lines of Drugscore and PMF. In docking experiments, the potential using the new reference state proposed for ASP gives better success rates than when these literature reference states were used; a success rate similar to the established scoring functions Goldscore and Chemscore is achieved with ASP. This is the case both for a large, general validation set of protein-ligand structures and for small test sets of actives against four pharmaceutically relevant targets. Virtual screening experiments for these targets show less discrimination between the different reference states in terms of enrichment. In addition, we describe how statistical potentials can be used in the construction of targeted scoring functions. Examples are given for cdk2, using four different targeted scoring functions, biased towards increasingly large target-specific databases. Using these targeted scoring functions, docking success rates as well as enrichments are significantly better than for the general ASP scoring function. Results improve with the number of structures used in the construction of the target scoring functions, thus illustrating that these targeted ASP potentials can be continuously improved as new structural data become available.