ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution
ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution
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DOI：
10.1021/acs.jctc.9b00591
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发表时间：
2020-01-01
影响因子：
5.5
通讯作者：
Simmerling, Carlos
中科院分区：
文献类型：
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作者：
Tian, Chuan;Kasavajhala, Koushik;Simmerling, Carlos
分子动力学(MD)模拟在研究生物分子的运动和功能方面越来越受欢迎。然而，模拟的准确性在很大程度上取决于分子力学(MM)力场(FF)，这是一组具有可调参数的函数，用于计算原子位置的势能。然而，FF的整体质量，如我们之前发布的ff99SB和ff14SB，可能会受到多年前做出的假设的限制。在这里提供的最新模型(Ff19SB)中，我们显著改善了所有20个氨基酸的主干轮廓。我们使用二维phi/psi构象扫描来拟合多个氨基酸的phi/psi耦合参数，并使用整个2D量子力学(QM)能量表面作为参考数据。我们在水溶液中同时使用QM和MM来解决二面体参数拟合中的偏振不一致性问题。最后，我们考察了主链装配对侧链旋转体的可能依赖性。为了广泛地验证ff19SB参数，并与其他琥珀模型的结果进行比较，我们在显式溶剂中进行了总共类似于5ms的MD模拟。我们的结果表明，在用溶剂极化对QM数据进行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图谱中的差异，而且显著提高了区分氨基酸依赖属性(如螺旋倾向)的能力。我们还得出结论，在ff14Sb中存在固有的对螺旋度的低估，这被TIP3P偏向过度紧凑结构导致的螺旋含量的增加所(不准确地)补偿。总而言之，当ff19SB与更精确的水模型(如OPC)相结合时，对于建模特定序列的行为、蛋白质突变以及合理的蛋白质设计应该具有更好的预测能力。在这里测试的显式水模型中，我们建议使用带有ff19SB的OPC。
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 phi/psi parameters using 2D phi/psi 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 similar to 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.