发表机构
FAIR at Meta; Pritzker School of Molecular Engineering, University of Chicago（Meta FAIR; 芝加哥大学普里茨克分子工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用机器学习势eSEN-omol对完整酶进行量子精度模拟，再现实验能垒、解析关键中间态并区分机制，实现千倍加速，为酶催化研究提供实用途径。
AI 中文摘要
催化酶中与成键/断键相关的电子重排需要超越经典分子力学（MM）的量子力学（QM）处理。混合QM/MM方法能够实现可处理的模拟，但需要针对特定体系进行设置，并且对QM区域的选择及QM/MM界面的处理敏感。我们展示了使用机器学习原子间势（MLIP）eSEN-omol，对包含多达54,000个原子和总计1微秒模拟时间的显式溶剂中全原子完整酶进行量子精度处理。我们再现了分支酸变位酶中克莱森重排的实验能垒趋势，解析了PETase催化的聚合物解聚中的关键中间态，并区分了核苷二磷酸激酶中金属激活的磷酸基转移的机制替代方案。我们实现了相对于典型QM/MM计算1000倍的加速，且无需针对特定体系进行调优。这些结果确立了MLIP作为实现酶催化量子精度模拟的实用途径。
英文摘要
Electronic rearrangements associated with bond forming/breaking in catalytic enzymes require quantum mechanical (QM) treatment beyond classical molecular mechanics (MM). Hybrid QM/MM methods enable tractable simulations but require system-specific setup and are sensitive to the QM region choice and treatment of the QM/MM interface. We demonstrate quantum-accurate treatment of all-atom, complete enzymes in explicit solvent comprising up to 54k atoms and 1 microsecond of total simulation time using the machine-learned interatomic potential (MLIP) eSEN-omol. We reproduce experimental barrier trends for Claisen rearrangement in chorismate mutase, resolve critical intermediate states in PETase catalyzed polymer depolymerization, and distinguish mechanistic alternatives for metal-activated phosphoryl transfer in nucleoside diphosphate kinase. We realize 1000x speedups relative to typical QM/MM calculations without system-specific tuning. These results establish MLIPs as a practical route to QM-accurate simulations of enzyme catalysis.
