Machine Learning in Enzyme Engineering
Machine Learning in Enzyme Engineering
复制标题
DOI：
10.1021/acscatal.9b04321
复制
发表时间：
2020-01-17
期刊：
影响因子：
12.9
通讯作者：
Damborsky, Jiri
中科院分区：
文献类型：
--
作者：
Mazurenko, Stanislav;Prokop, Zbynek;Damborsky, Jiri
酶工程在开发生物技术、生物医学和生命科学的高效生物催化剂方面发挥着核心作用。除了经典的理性设计和定向进化方法之外，机器学习方法也越来越多地应用于寻找数据模式，帮助预测蛋白质结构，提高酶的稳定性、溶解度和功能，预测底物特异性，并指导合理的蛋白质设计。在这个视角中，我们分析了用于训练和验证酶工程预测因子的数据库和方法的最新技术。我们讨论了社区当前面临的局限性和挑战，以及有潜力解决这些挑战的实验和理论方法的最新进展。我们还提出了我们对开发高效生物催化剂设计应用的未来可能方向的看法。
Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have been increasingly applied to find patterns in data that help predict protein structures, improve enzyme stability, solubility, and function, predict substrate specificity, and guide rational protein design. In this Perspective, we analyze the state of the art in databases and methods used for training and validating predictors in enzyme engineering. We discuss current limitations and challenges which the community is facing and recent advancements in experimental and theoretical methods that have the potential to address those challenges. We also present our view on possible future directions for developing the applications to the design of efficient biocatalysts.