Qu Ge, Zhu Tong, Jiang Yingying, Wu Bian, Sun Zhoutong
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
Sheng Wu Gong Cheng Xue Bao. 2019 Oct 25;35(10):1843-1856. doi: 10.13345/j.cjb.190221.
By constructing mutant libraries and utilizing high-throughput screening methods, directed evolution has emerged as the most popular strategy for protein design nowadays. In the past decade, taking advantages of computer performance and algorithms, computer-assisted protein design has rapidly developed and become a powerful method of protein engineering. Based on the simulation of protein structure and calculation of energy function, computational design can alter the substrate specificity and improve the thermostability of enzymes, as well as de novo design of artificial enzymes with expected functions. Recently, machine learning and other artificial intelligence technologies have also been applied to computational protein engineering, resulting in a series of remarkable applications. Along the lines of protein engineering, this paper reviews the progress and applications of computer-assisted protein design, and current trends and outlooks of the development.
通过构建突变文库并利用高通量筛选方法，定向进化已成为当今蛋白质设计中最流行的策略。在过去十年中，借助计算机性能和算法，计算机辅助蛋白质设计迅速发展并成为蛋白质工程的一种强大方法。基于蛋白质结构模拟和能量函数计算，计算设计可以改变酶的底物特异性、提高酶的热稳定性，还能从头设计具有预期功能的人工酶。最近，机器学习和其他人工智能技术也已应用于计算蛋白质工程，取得了一系列显著成果。本文沿着蛋白质工程的脉络，综述了计算机辅助蛋白质设计的进展与应用以及当前的发展趋势和展望。
