Comparison between Generalized-Born and Poisson-Boltzmann methods in physics-based scoring functions for protein structure prediction.
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.
Lee
Matthew C
MC
Department of Chemistry and Biochemistry, University of Delaware, Newark, DE 19716, USA.
Yang
Rong
R
Duan
Yong
Y
D055598:Chemical Phenomena; D002627:Chemistry, Physical; D003201:Computers; D008958:Models, Molecular; D011489:Protein Denaturation; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
D011506:Proteins