Izadi Saeed, Harris Robert C, Fenley Marcia O, Onufriev Alexey V
Early Stage Pharmaceutical Development , Genentech Inc. , 1 DNA Way , South San Francisco , California 94080 , United States.
Department of Pharmaceutical Sciences , University of Maryland School of Pharmacy , Baltimore , Maryland 21201 , United States.
J Chem Theory Comput. 2018 Mar 13;14(3):1656-1670. doi: 10.1021/acs.jctc.7b00886. Epub 2018 Feb 15.
The need for accurate yet efficient representation of the aqueous environment in biomolecular modeling has led to the development of a variety of generalized Born (GB) implicit solvent models. While many studies have focused on the accuracy of available GB models in predicting solvation free energies, a systematic assessment of the quality of these models in binding free energy calculations, crucial for rational drug design, has not been undertaken. Here, we evaluate the accuracies of eight common GB flavors (GB-HCT, GB-OBC, GB-neck2, GBNSR6, GBSW, GBMV1, GBMV2, and GBMV3), available in major molecular dynamics packages, in predicting the electrostatic binding free energies ( ΔΔ G) for a diverse set of 60 biomolecular complexes belonging to four main classes: protein-protein, protein-drug, RNA-peptide, and small complexes. The GB flavors are examined in terms of their ability to reproduce the results from the Poisson-Boltzmann (PB) model, commonly used as accuracy reference in this context. We show that the agreement with the PB of ΔΔ G estimates varies widely between different GB models and also across different types of biomolecular complexes, with R correlations ranging from 0.3772 to 0.9986. A surface-based "R6" GB model recently implemented in AMBER shows the closest overall agreement with reference PB ( R = 0.9949, RMSD = 8.75 kcal/mol). The RNA-peptide and protein-drug complex sets appear to be most challenging for all but one model, as indicated by the large deviations from the PB in ΔΔ G. Small neutral complexes present the least challenge for most of the GB models tested. The quantitative demonstration of the strengths and weaknesses of the GB models across the diverse complex types provided here can be used as a guide for practical computations and future development efforts.
在生物分子建模中，为了准确且高效地表示水环境，人们开发了多种广义玻恩（GB）隐式溶剂模型。虽然许多研究都聚焦于现有GB模型在预测溶剂化自由能方面的准确性，但对于这些模型在结合自由能计算中的质量进行系统评估（这对合理药物设计至关重要）却尚未开展。在此，我们评估了主要分子动力学软件包中可用的八种常见GB类型（GB - HCT、GB - OBC、GB - neck2、GBNSR6、GBSW、GBMV1、GBMV2和GBMV3）在预测60种不同生物分子复合物静电结合自由能（ΔΔG）方面的准确性，这些复合物分属四类主要类型：蛋白质 - 蛋白质、蛋白质 - 药物、RNA - 肽和小分子复合物。我们根据这些GB类型重现泊松 - 玻尔兹曼（PB）模型结果的能力对其进行了检验，在此背景下PB模型通常用作准确性参考。我们发现，不同GB模型之间以及不同类型生物分子复合物之间，ΔΔG估计值与PB的一致性差异很大，R相关性在0.3772至0.9986之间。最近在AMBER中实现的基于表面的“R6”GB模型与参考PB总体一致性最高（R = 0.9949，均方根偏差 = 8.75千卡/摩尔）。除一种模型外，RNA - 肽和蛋白质 - 药物复合物集对所有模型似乎都最具挑战性，这体现在ΔΔG与PB的偏差较大。对于大多数测试的GB模型而言，小分子中性复合物带来的挑战最小。本文对GB模型在不同复杂类型中的优缺点进行的定量展示，可作为实际计算和未来开发工作的指导。
