@article{Zhang2024, 
author = {Chuanxi Zhang and Yinghui Feng and Yiting Zhu and Lei Gong and Hao Wei and Lujia Zhang},
title = {NAC4ED: A high-throughput computational platform for the rational design of enzyme activity and substrate selectivity},
year = {2024},
journal = {mLife},
volume = {3},
number = {4},
pages = {505-514},
keywords = {high-throughput screening, near-attack conformation, protein engineering, rational design},
url = {https://www.sciopen.com/article/10.1002/mlf2.12154},
doi = {10.1002/mlf2.12154},
abstract = {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. NAC4ED is currently available at http://lujialab.org.cn/software/.}
}