关键词: 预反应态, 近进攻构象, 催化循环, 突变效应, 底物适配性, 分子动力学模拟

Abstract:

The bottleneck of enzyme design for biosynthetic elements lies in the incompetence of the limited computing resources with demanding for an in-depth computation on complicated potential energy surfaces of catalytic reactions. However, two unprecedented achievements are expected to expand artificial intelligence machine learning in protein engineering-one is a variety of high-efficient mutants brought by high-throughput directed evolution experiments, and the other is the high-quality molecular simulation of all-atom with femtosecond precision revealed by ab initio quantum mechanics calculation and three-dimensional structural information. This work briefly describes the basic concept and application of the pre-reaction state (PRS) model from the perspectives of the fundamental enzyme theories, the near-attack conformation of Michealis complex, and the control points of the catalytic cycle efficiency. The pre-reaction state model tries to use the intrinsic features of biochemical reactions with low activation energy in which transition state and pre-reaction states share similar physiochemical stability, flexibly selects the rate-determining transition states related to the evolutional goal of the catalytic element, and employs classical molecular dynamics simulations to understand the relationship of active conformation population with distal mutations, substrate spectrum, and experimental conditions. The general pre-reaction state protocol is: first, the near-transition state structural features are extracted from the high-level quantum-mechanical calculation on the rate-determining transition structures; then the PRS molecular dynamic simulations are collected from the restrained to the free state, which is used to study the adaptability between mutants and substrates. The population in the PRS trajectory is used as a semi-quantitative correlation coefficient of “pre-reaction state-enzyme activity” （PRS-EA）, and the adaptation map of enzyme and substrate is mined from the pre-reaction state stability. Although the mechanism-based pre-reaction state analysis provides an insightful rationale at atom levels as a post-NAC approach, the quantitative relationship between the PRS structure and enzymatic reaction cannot be fully illustrated owing to the ambiguity of the PRS constraint, the repeatability of molecular dynamics simulation, and the arbitrariness of reactive population. The high throughput quantum calculation for transition state samplings and machine learning and artificial intelligence could be integrated to unveil the quantitative structure-activity relationship, paving a way for the practical applications of pre-reaction state in protein engineering.

Key words: pre-reaction state, near attack conformation, catalytic cycle, mutation effect, substrate adaptability, molecular dynamics simulation