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.
摘要：
天然酶在活性方面往往难以满足应用和研究的需要,对映体选择性或热稳定性。因此,探索有效的分子修饰技术以改善此类酶的性质是酶工程的重要任务。酶的分子修饰技术主要包括合理设计、定向进化,和人工智能辅助设计。定向进化和合理设计是实验驱动的酶分子修饰方法，已成功应用于酶工程。然而,由于蛋白质序列的巨大空间大小和缺乏实验数据，目前的改性方法仍然面临重大挑战。随着下一代测序技术的发展,高通量筛选，蛋白质数据库,和人工智能(AI)，数据驱动的酶工程正在成为解决这些挑战的有希望的解决方案。AI辅助统计学习方法已用于建立以数据驱动方式预测酶的序列/结构特性的模型。可以根据预测结果选择优秀的突变酶，大大提高了分子修饰的效率。考虑到酶分子修饰的应用需求,本文综述了AI辅助酶分子修饰的数据获取方法和应用实例,重点研究了预测蛋白质热稳定性的卷积神经网络方法，旨在为该领域的研究人员提供参考。
