title: Comparative study of generalized born models: Born radii and peptide folding.
abstract: In this study, we have implemented four analytical generalized Born (GB) models and investigated their performance in conjunction with the GROMOS96 force field. The four models include that of Still and co-workers, the HCT model of Cramer, Truhlar, and co-workers, a modified form of the AGB model of Levy and co-workers, and the GBMV2 model of Brooks and co-workers. The models were coded independently and implemented in the GROMOS software package and in TINKER. They were compared in terms of their ability to reproduce the results of Poisson-Boltzmann (PB) calculations and in their performance in the ab initio peptide folding of two peptides, one that forms a beta-hairpin in solution and one that forms an alpha-helix. In agreement with previous work, the GBMV2 model is most successful in reproducing PB results while the other models tend to underestimate the effective Born radii of buried atoms. In contrast, stochastic dynamics simulations on the folding of the two peptides, the C-terminus beta-hairpin of the B1 domain of protein G and the alanine-based alpha-helical peptide 3K(I), suggest that the simpler GB models are more effective in sampling conformational space. Indeed, the Still model used in conjunction with the GROMOS96 force field is able to fold the hairpin peptide to a native-like structure without the benefit of enhanced sampling techniques. This is due in part to the properties of the united-atom GROMOS96 force field which appears to be more flexible, and hence to sample more efficiently, than force fields such as OPLSAA. Our results suggest a general strategy which involves using different combinations of force fields and solvent models in different applications, for example, using GROMOS96 and a simple GB model in sampling and OPLSAA and a more accurate GB model in refinement. The fact that various methods have been implemented in a unified way should facilitate the testing and subsequent use of different methods to evaluate conformational free energies in different applications. Our results also bear on some general issues involved in peptide folding and structure prediction which are addressed in the Discussion.
authors.affiliation: Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, New York 10032, USA.
authors.forename: Jiang
authors.initials: J
authors.lastname: Zhu
authors.forename: Emil
authors.initials: E
authors.lastname: Alexov
authors.forename: Barry
authors.initials: B
authors.lastname: Honig
mesh_terms: D000409:Alanine / Q000737:chemistry; D001703:Biophysics / Q000379:methods; D002627:Chemistry, Physical / Q000379:methods; D003198:Computer Simulation; D006860:Hydrogen Bonding; D008958:Models, Molecular; D015233:Models, Statistical; D008968:Molecular Conformation; D009419:Nerve Tissue Proteins / Q000737:chemistry*; D010455:Peptides / Q000737:chemistry*; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D012997:Solvents / Q000737:chemistry; D013816:Thermodynamics
chemical_list: C029152:G-substrate; D009419:Nerve Tissue Proteins; D010455:Peptides; D012997:Solvents; D000409:Alanine