人工智能辅助的酶分子改造应用进展

Progress in the application of artificial intelligence-assisted molecular modification of enzymes

摘要:

天然酶在活性、对映体选择性或热稳定性等方面经常难以满足应用与研究的需求，探索高效的酶分子改造技术改善该类酶的某些特性是酶工程的重要任务。酶分子改造技术主要包括理性设计、定向进化和人工智能辅助设计等。定向进化和理性设计是由实验驱动的酶分子改造策略，已经成功地应用于酶工程，但由于蛋白质序列空间的尺寸巨大以及实验数据少，现行的酶分子改造方法仍然面临着重大挑战。随着新一代测序、高通量筛选方法、蛋白质数据库和人工智能技术的发展，数据驱动的酶工程有望应对这些挑战。其中，采用人工智能辅助的统计学习方法，通过数据驱动方式构建序列/结构-酶性能的预测模型，依据预测模型挑选优良突变酶，大大提高了酶分子改造效率。基于酶分子改造的应用需求，本文综述了人工智能辅助酶分子改造的数据采集方法以及人工智能辅助酶分子改造的应用实例等，重点叙述了采用卷积神经网络预测蛋白质热稳定性的方法，以期为该领域的研究人员提供参考。

Abstract:

Natural enzymes are often difficult to meet the needs of application and research in terms of activity, enantiomer selectivity or thermal stability. Therefore, it is an important task of enzyme engineering to explore efficient molecular modification technologies to improve the properties of such enzymes. The molecular modification technologies of enzymes mainly include rational design, directed evolution, and artificial intelligence-assisted design. Directed evolution and rational design are experiment-driven molecular modification approaches of enzymes and have been successfully applied to enzyme engineering. However, due to the huge space sizes of protein sequences and the lack of experimental data, the current modification methods still face major challenges. With the development of next-generation sequencing, high-throughput screening, protein databases, and artificial intelligence (AI), data-driven enzyme engineering is emerging as a promising solution to these challenges. The AI-assisted statistical learning method has been used to establish a model for predicting the sequence/structure-properties of enzymes in a data-driven manner. Excellent mutant enzymes can be selected according to the prediction results, which greatly improve the efficiency of molecular modification. Considering the application requirements of molecular modification of enzymes, this paper reviews the data acquisition methods and application examples of AI-assisted molecular modification of enzymes, with focuses on the convolutional neural network method for predicting protein thermostability, aiming to provide reference for researchers in this field.