Improved Generalized Born Solvent Model Parameters for Protein Simulations.
Improved Generalized Born Solvent Model Parameters for Protein Simulations.
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DOI：
10.1021/ct3010485
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发表时间：
2013-04-09
影响因子：
5.5
通讯作者：
Simmerling, Carlos
中科院分区：
文献类型：
--
作者：
Nguyen, Hai;Roe, Daniel R.;Simmerling, Carlos
广义Born(GB)模型是计算速度最快的隐式溶剂模型之一，已被广泛用于分子动力学(MD)模拟。这种速度是需要权衡的，文献中的许多报告都指出了GB型号的缺点。由于用来计算溶剂化能的经验参数对GB模型的质量有很大的影响，为了提高计算溶剂化能和有效半径的精度，本文对GB-Neck模型进行了修正。用于拟合的数据集比过去使用的数据集要大得多。与GB-OBC和原有的GB-Neck模型相比，新的GB模型(GB-Neck2)与泊松-玻尔兹曼(PB)模型在再现从多肽到蛋白质的各种体系的溶剂化能方面有更好的一致性。二级结构择优也与显式溶剂MD模拟得到的结果吻合得更好。我们还获得了几个具有不同二级结构基序的模型肽的实验结构和热稳定性的近乎定量的再现。未来将探索将其扩展到非蛋白质系统。
The generalized Born (GB) model is one of the fastest implicit solvent models and it has become widely adopted for Molecular Dynamics (MD) simulations. This speed comes with tradeoffs, and many reports in the literature have pointed out weaknesses with GB models. Because the quality of a GB model is heavily affected by empirical parameters used in calculating solvation energy, in this work we have refit these parameters for GB-Neck, a recently developed GB model, in order to improve the accuracy of both the solvation energy and effective radii calculations. The data sets used for fitting are significantly larger than those used in the past. Comparing to other pairwise GB models like GB-OBC and the original GB-Neck, the new GB model (GB-Neck2) has better agreement to Poisson-Boltzmann (PB) in terms of reproducing solvation energies for a variety of systems ranging from peptides to proteins. Secondary structure preferences are also in much better agreement with those obtained from explicit solvent MD simulations. We also obtain near-quantitative reproduction of experimental structure and thermal stability profiles for several model peptides with varying secondary structure motifs. Extension to non-protein systems will be explored in the future.
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