Harder, better, faster, stronger: Large-scale QM and QM/MM for predictive modeling in enzymes and proteins
Harder, better, faster, stronger: Large-scale QM and QM/MM for predictive modeling in enzymes and proteins
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DOI：
10.1016/j.sbi.2021.07.004
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发表时间：
2022-02-01
影响因子：
6.8
通讯作者：
Kulik, Heather J.
中科院分区：
文献类型：
--
作者：
Vennelakanti, Vyshnavi;Nazemi, Azadeh;Kulik, Heather J.
酶机制和蛋白质功能的计算预测需要精确的基于物理的模型和适当的采样。我们讨论了在生化系统的大规模量子力学(QM)建模方面的最新进展，这些进展降低了高精度模型的成本。采样和精度之间的权衡促使在多尺度QM/MM或迭代方法中使用分子力学(MM)建模。与实验或波函数理论相比，传统密度泛函理论和经典MM力场在描述非共价相互作用方面仍然存在局限性。由于对酶作用(即静电学)、自由能垒和机制的预测对QM/MM中的协议和嵌入方法很敏感，因此收敛测试和量化QM水平相互作用的系统方法是一个必要的、活跃的发展领域。
Computational prediction of enzyme mechanism and protein function requires accurate physics-based models and suitable sampling. We discuss recent advances in large-scale quantum mechanical (QM) modeling of biochemical systems that have reduced the cost of high-accuracy models. Tradeoffs between sampling and accuracy have motivated modeling with molecular mechanics (MM) in a multiscale QM/MM or iterative approach. Limitations to both conventional density-functional theory and classical MM force fields remain for describing noncovalent interactions in comparison to experiment or wavefunction theory. Because predictions of enzyme action (i.e. electrostatics), free energy barriers, and mechanisms are sensitive to the protocol and embedding method in QM/MM, convergence tests and systematic methods for quantifying QM-level interactions are a needed, active area of development.