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首页> 外文期刊>Journal of Medicinal Chemistry >Assessing Scoring Functions for Protein-Ligand Interactions
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Assessing Scoring Functions for Protein-Ligand Interactions

机译：评估蛋白质-配体相互作用的评分功能

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摘要

An assessment of nine scoring functions commonly applied in docking using a set of 189 protein-ligand complexes is presented. The scoring functions include the CHARMm potential, the scoring function DrugScore, the scoring function used in AutoDock, the three scoring functions implemented in DOCK, as well as three scoring functions implemented in the CScore module in SYBYL (PMF, Gold, ChemScore). We evaluated the abilities of these scoring functions to recognize near-native configurations among a set of decoys and to rank binding affinities. Binding site decoys were generated by molecular dynamics with restraints. To investigate whether the scoring functions can also be applied for binding site detection, decoys on the protein surface were generated. The influence of the assignment of protonation states was probed by either assigning "standard" protonation states to binding site residues or adjusting protonation states according to experimental evidence. The role of solvation models in conjunction with CHARMm was explored in detail. These include a distance-dependent dielectric function, a generalized Born model, and the Poisson equation. We evaluated the effect of using a rigid receptor on the outcome of docking by generating all-pairs decoys ("cross-decoys") for six trypsin and seven HIV-1 protease complexes. The scoring functions perform well to discriminate near-native from misdocked conformations, with CHARMm, DOCK-energy, DrugScore, ChemScore, and AutoDock yielding recognition rates of around 80%. Significant degradation in performance is observed in going from decoy to cross-decoy recognition for CHARMm in the case of HIV-1 protease, whereas DrugScore and ChemScore, as well as CHARMm in the case of trypsin, show only small deterioration. In contrast, the prediction of binding affinities remains problematic for all of the scoring functions. ChemScore gives the highest correlation value with R~2 = 0.51 for the set of 189 complexes and R~2 = 0.43 for the set of 116 complexes that does not contain any of the complexes used to calibrate this scoring function. Neither a more accurate treatment of solvation nor a more sophisticated charge model for zinc improves the quality of the results. Improved modeling of the protonation states, however, leads to a better prediction of binding affinities in the case of the generalized Born and the Poisson continuum models used in conjunction with the CHARMm force field.
机译：介绍了使用一组189种蛋白质-配体复合物对对接中常用的九种评分功能的评估。计分功能包括CHARMm势，计分功能DrugScore，在AutoDock中使用的计分功能，在DOCK中实现的三个计分功能，以及在SYBYL中的CScore模块中实现的三个计分功能（PMF，Gold，ChemScore）。我们评估了这些评分功能在一组诱饵中识别近乎本地构型并对结合亲和力进行排名的能力。结合位点诱饵是通过分子动力学的约束而产生的。为了研究评分功能是否也可以用于结合位点检测，在蛋白质表面产生了诱饵。通过将“标准”质子化状态分配给结合位点残基或根据实验证据调整质子化状态来探究质子化状态分配的影响。详细探讨了溶剂化模型与CHARMm结合的作用。这些包括与距离有关的介电函数，广义的伯恩模型和泊松方程。我们通过产生六种胰蛋白酶和七种HIV-1蛋白酶复合物的全对诱饵（“交叉诱饵”）来评估使用刚性受体对对接结果的影响。评分功能表现良好，可将近乎原生的错误构象区分开，CHARMm，DOCK能量，DrugScore，ChemScore和AutoDock的识​​别率约为80％。在HIV-1蛋白酶的情况下，对于CHARMm从诱饵到交叉诱饵的识别，性能会显着下降，而对于胰蛋白酶，DrugScore和ChemScore以及CHARMm仅表现出很小的恶化。相反，对于所有得分功能，结合亲和力的预测仍然存在问题。 ChemScore给出了最高的相关值，对于189种配合物而言，R〜2 = 0.51，对于116种不包含用于校准此评分功能的配合物的配合物，R〜2 = 0.43。更精确的溶剂化处理或更复杂的锌电荷模型都不能改善结果的质量。但是，在结合CHARMm力场使用广义Born和Poisson连续谱模型的情况下，改进的质子化状态建模可以更好地预测结合亲和力。

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