Accuracy Comparison of Generalized Born Models in the Calculation of Electrostatic Binding Free Energies

作者：Izadi Saeed; Harris Robert C; Fenley Marcia O^*; Onufriev Alexey V^*

来源：Journal of Chemical Theory and Computation, 2018, 14(3): 1656-1670.

DOI：10.1021/acsjctc.7b00886

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 (Delta Delta G(el)) 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 Delta Delta G(el) estimates varies widely between different GB models and also across different types of biomolecular complexes, with R-2 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-2 = 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 Delta Delta G(el). 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.

• 出版日期2018-3
• 单位美国弗吉尼亚理工大学(Virginia Tech)