机器学习在蛋白质功能预测领域的研究进展

Advances in machine learning for predicting protein functions

摘要:

蛋白质是有机生命体内不可或缺的化合物，在生命活动中发挥着多种重要作用，了解蛋白质的功能有助于医学和药物研发等领域的研究。此外，酶在绿色合成中的应用一直备受人们关注，但是由于酶的种类和功能多种多样，获取特定功能酶的成本高昂，限制了其进一步的应用。目前，蛋白质的具体功能主要通过实验表征确定，该方法实验工作繁琐且耗时耗力，同时，随着生物信息学和测序技术的高速发展，已测序得到的蛋白质序列数量远大于功能获得注释的序列数量，高效预测蛋白质功能变得至关重要。随着计算机技术的蓬勃发展，由数据驱动的机器学习方法已成为应对这些挑战的有效解决方案。本文对蛋白质功能及其注释方法以及机器学习的发展历程和操作流程进行了概述，聚焦于机器学习在酶功能预测领域的应用，对未来人工智能辅助蛋白质功能高效研究的发展方向提出了展望。

Abstract:

Proteins play a variety of functional roles in cellular activities and are indispensable for life. Understanding the functions of proteins is crucial in many fields such as medicine and drug development. In addition, the application of enzymes in green synthesis has been of great interest, but the high cost of obtaining specific functional enzymes as well as the variety of enzyme types and functions hamper their application. At present, the specific functions of proteins are mainly determined through tedious and time-consuming experimental characterization. With the rapid development of bioinformatics and sequencing technologies, the number of protein sequences that have been sequenced is much larger than those can be annotated, thus developing efficient methods for predicting protein functions becomes crucial. With the rapid development of computer technology, data-driven machine learning methods have become a promising solution to these challenges. This review provides an overview of protein function and its annotation methods as well as the development history and operation process of machine learning. In combination with the application of machine learning in the field of enzyme function prediction, we present an outlook on the future direction of efficient artificial intelligence-assisted protein function research.