Mikurova A V, Skvortsov V S
Institute of Biomedical Chemistry, Moscow, Russia.
Biomed Khim. 2018 Jun;64(3):247-252. doi: 10.18097/PBMC20186403247.
Preliminary results of construction of overall model for prediction of IC50 value of ligands of influenza virus neuraminidase of any strain are presented. We used MM-PBSA (MM-GBSA) energy terms calculated for the complexes obtained after modeling of 30 variants of neuraminidase structures, subsequent docking and simulation of molecular dynamics as independent variables in prediction equations. The structures of known neuraminidase-inhibiting drugs (oseltamivir, zanamivir and peramivir) and a neuraminidase substrate (MUNANA) were used as ligands. The correlation equation based on calculated energetic parameters of inhibitor complexes with neuraminidase did not result in the prediction of IC50 with acceptable parameters (R2£0.3). However, if information about binding energy of the substrate used for neuraminidase assay (and IC50 detection) is included the resulting IC50 prediction equations become significant (R2³0.55). It is concluded that models based on IC50 values as a predictable variable and combining information about binding of different ligands to different variants of the target proteins must take into account the binding properties of the substrate (used for IC50 determination). The predictive power of such models depends critically on the quality of the modeling of the ligand-protein complexes.
本文给出了构建用于预测任何毒株流感病毒神经氨酸酶配体IC50值的总体模型的初步结果。我们将针对神经氨酸酶结构的30种变体进行建模、随后对接以及分子动力学模拟后得到的复合物计算出的MM-PBSA(MM-GBSA)能量项用作预测方程中的自变量。已知的神经氨酸酶抑制药物(奥司他韦、扎那米韦和帕拉米韦)以及一种神经氨酸酶底物(MUNANA)的结构用作配体。基于抑制剂与神经氨酸酶复合物计算出的能量参数的相关方程未能以可接受的参数预测IC50(R2≤0.3)。然而,如果纳入用于神经氨酸酶测定(以及IC50检测)的底物结合能的信息,则所得的IC50预测方程变得显著(R2≥0.55)。得出的结论是,基于IC50值作为可预测变量并结合不同配体与靶蛋白不同变体结合信息的模型必须考虑底物(用于IC50测定)的结合特性。此类模型的预测能力严重依赖于配体 - 蛋白质复合物建模的质量。