NAC4ED:A high‐throughput computational platform for the rational design of enzyme activity and substrate selectivity  

作　　者：Chuanxi Zhang Yinghui Feng Yiting Zhu Lei Gong Hao Wei Lujia Zhang 

机构地区：[1]Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development,School of Chemistry and Molecular Engineering,East China Normal University,Shanghai,China [2]Department of Micro/Nano Electronics,School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai,China [3]School of Biotechnology,East China University of Science and Technology,Shanghai,China [4]School of Biotechnology,Tianjin University of Science and Technology,Tianjin,China [5]NYU‐ECNU Center for Computational Chemistry at NYU Shanghai,Shanghai,China

出　　处：《mLife》2024年第4期505-514,共10页微生物(英文)

基　　金：supported by the National Key R&D Program of China(grant No.2019YFA0905200);Science and Technology Commission of Shanghai Municipality(grant No.23HC1400500);Agricultural Science and Technology Innovation System of Shanghai,China(grant No.T2023217);Shanghai Frontiers Science Center of Molecule Intelligent Syntheses.

摘　　要：In silico computational methods have been widely utilized to study enzyme catalytic mechanisms and design enzyme performance,including molecular docking,molecular dynamics,quantum mechanics,and multiscale QM/MM approaches.However,the manual operation associated with these methods poses challenges for simulating enzymes and enzyme variants in a high‐throughput manner.We developed the NAC4ED,a high‐throughput enzyme mutagenesis computational platform based on the“near‐attack conformation”design strategy for enzyme catalysis substrates.This platform circumvents the complex calculations involved in transition‐state searching by representing enzyme catalytic mechanisms with parameters derived from near‐attack conformations.NAC4ED enables the automated,high‐throughput,and systematic computation of enzyme mutants,including protein model construction,complex structure acquisition,molecular dynamics simulation,and analysis of active conformation populations.Validation of the accuracy of NAC4ED demonstrated a prediction accuracy of 92.5%for 40 mutations,showing strong consistency between the computational predictions and experimental results.The time required for automated determination of a single enzyme mutant using NAC4ED is 1/764th of that needed for experimental methods.This has significantly enhanced the efficiency of predicting enzyme mutations,leading to revolutionary breakthroughs in improving the performance of high‐throughput screening of enzyme variants.NAC4ED facilitates the efficient generation of a large amount of annotated data,providing high‐quality data for statistical modeling and machine learning.

关 键 词：high‐throughput screening near‐attack conformation protein engineering rational design 

分 类 号：Q55[生物学—生物化学]