Zhu Jiang, Alexov Emil, Honig Barry
Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, New York 10032, USA.
J Phys Chem B. 2005 Feb 24;109(7):3008-22. doi: 10.1021/jp046307s.
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.
在本研究中，我们实现了四种解析广义玻恩（GB）模型，并结合GROMOS96力场研究了它们的性能。这四种模型包括斯蒂尔及其同事提出的模型、克莱默、特鲁哈拉及其同事提出的HCT模型、利维及其同事提出的AGB模型的一种改进形式，以及布鲁克斯及其同事提出的GBMV2模型。这些模型是独立编码的，并在GROMOS软件包和TINKER中实现。我们根据它们重现泊松-玻尔兹曼（PB）计算结果的能力以及在两条肽段的从头算肽折叠中的性能对它们进行了比较，其中一条肽段在溶液中形成β-发夹结构，另一条形成α-螺旋结构。与之前的工作一致，GBMV2模型在重现PB结果方面最为成功，而其他模型往往低估了埋藏原子的有效玻恩半径。相比之下，对两条肽段（蛋白质G的B1结构域的C端β-发夹和基于丙氨酸的α-螺旋肽3K(I)）折叠过程的随机动力学模拟表明，更简单的GB模型在采样构象空间方面更有效。实际上，与GROMOS96力场结合使用的斯蒂尔模型能够在没有增强采样技术的情况下将发夹肽折叠成类似天然的结构。这部分归因于联合原子GROMOS96力场的特性，与OPLSAA等力场相比，它似乎更灵活，因此能够更有效地采样。我们的结果提出了一种通用策略，即在不同应用中使用不同的力场和溶剂模型组合，例如，在采样中使用GROMOS96和简单的GB模型，在优化中使用OPLSAA和更精确的GB模型。各种方法以统一方式实现这一事实应有助于测试和随后使用不同方法来评估不同应用中的构象自由能。我们的结果还涉及肽折叠和结构预测中涉及的一些一般问题，这些问题将在讨论中阐述。
