Department of Chemistry , Stony Brook University , Stony Brook , New York 11794 , United States.
Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 11794 , United States.
J Chem Theory Comput. 2020 Jan 14;16(1):528-552. doi: 10.1021/acs.jctc.9b00591. Epub 2019 Dec 3.
Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled φ/ψ parameters using 2D φ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms MD simulations in explicit solvent. Our results show that after amino-acid-specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino-acid-specific Protein Data Bank (PDB) Ramachandran maps better but also shows significantly improved capability to differentiate amino-acid-dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated for by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. Of the explicit water models tested here, we recommend use of OPC with ff19SB.
分子动力学 (MD) 模拟在研究生物分子的运动和功能方面变得越来越流行。然而，模拟的准确性高度取决于分子力学 (MM) 力场 (FF)，这是一组具有可调参数的函数，用于从原子位置计算势能。然而，FF 的整体质量，例如我们之前发布的 ff99SB 和 ff14SB，可能受到多年前做出的假设的限制。在本文提出的更新模型 (ff19SB) 中，我们显著改进了所有 20 种氨基酸的骨架轮廓。我们使用 2D φ/ψ 构象扫描为多种氨基酸拟合耦合的 φ/ψ 参数，并使用整个二维量子力学 (QM) 能量表面作为参考数据。我们通过在水溶液中同时使用 QM 和 MM 来解决二面角参数拟合中的极化不一致性。最后，我们检查了骨架拟合对侧链构象的可能依赖性。为了广泛验证 ff19SB 参数，并与使用其他 Amber 模型的结果进行比较，我们总共在明溶剂中进行了约 5 毫秒的 MD 模拟。我们的结果表明，在经过针对具有溶剂极化的 QM 数据的氨基酸特异性训练后，ff19SB 不仅更好地再现了氨基酸特异性蛋白质数据库 (PDB) Ramachandran 图谱的差异，而且还显著提高了区分氨基酸依赖性性质（如螺旋倾向）的能力。我们还得出结论，ff14SB 中存在螺旋结构低估的固有问题，这通过 TIP3P 对过于紧凑结构的偏置导致的螺旋含量增加得到了（不精确）补偿。总之，当与更准确的水模型（如 OPC）结合使用时，ff19SB 应该对建模序列特异性行为、蛋白质突变以及合理的蛋白质设计具有更好的预测能力。在本文测试的明水模型中，我们建议使用 OPC 与 ff19SB 一起使用。
