title: Comparison between Generalized-Born and Poisson-Boltzmann methods in physics-based scoring functions for protein structure prediction.
abstract: Continuum solvent models such as Generalized-Born and Poisson-Boltzmann methods hold the promise to treat solvation effect efficiently and to enable rapid scoring of protein structures when they are combined with physics-based energy functions. Yet, direct comparison of these two approaches on large protein data set is lacking. Building on our previous work with a scoring function based on a Generalized-Born (GB) solvation model, and short molecular-dynamics simulations, we further extended the scoring function to compare with the MM-PBSA method to treat the solvent effect. We benchmarked this scoring function against seven publicly available decoy sets. We found that, somewhat surprisingly, the results of MM-PBSA approach are comparable to the previous GB-based scoring function. We also discussed the effect to the scoring function accuracy due to presence of large ligands and ions in some native structures of the decoy sets.
authors.affiliation: Department of Chemistry and Biochemistry, University of Delaware, Newark, DE 19716, USA.
authors.forename: Matthew C
authors.initials: MC
authors.lastname: Lee
authors.forename: Rong
authors.initials: R
authors.lastname: Yang
authors.forename: Yong
authors.initials: Y
authors.lastname: Duan
mesh_terms: D055598:Chemical Phenomena; D002627:Chemistry, Physical; D003201:Computers; D008958:Models, Molecular; D011489:Protein Denaturation; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D011506:Proteins