Comparison between Generalized-Born and Poisson–Boltzmann methods in physics-based scoring functions for protein structure prediction
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- 作者：Matthew C. Lee ; Rong Yang and Yong Duan
- 关键词：Poisson– ; Boltzmann continuum solvent model ; Protein structure prediction ; AMBER force field ; Molecular mechanics
- 刊名：Journal of Molecular Modeling
- 出版时间：December 2005
- 出版年：2005
- 期刊代码：149_1610-2940
- 类别：bio
- 卷：12
- 期：1
- 页码：101-110
- 数据来源：sp
摘要
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.