合成生物学 ›› 2023, Vol. 4 ›› Issue (3): 571-589.DOI: 10.12211/2096-8280.2023-011

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“可折叠性”在酶智能设计改造中的应用研究——以AlphaFold2为例

孟巧珍1, 郭菲2   

  1. 1.天津大学智能与计算学部 计算机学院,天津 300350
    2.中南大学计算机学院,湖南 长沙 410000
  • 收稿日期:2023-02-06 修回日期:2023-03-28 出版日期:2023-06-30 发布日期:2023-07-05
  • 通讯作者: 郭菲
  • 作者简介:孟巧珍(1993—),女,博士研究生。研究方向为蛋白质结构预测,蛋白质序列设计,生物信息学等。 E-mail:2015210125@tju.edu.cn
    郭菲(1984—),女,教授,博士生导师。研究方向为机器学习、深度学习、数据挖掘、生物信息学、医学图像分析等。 E-mail:guofei@csu.edu.cn
  • 基金资助:
    国家自然科学基金(62172296);国家科技计划(2020YFA0908400)

Applications of foldability in intelligent enzyme engineering and design: take AlphaFold2 for example

Qiaozhen MENG1, Fei GUO2   

  1. 1.College of Intelligence and Computing,School of Computer Science and Technology,Tianjin University,Tianjin 300350,China
    2.School of Computer Science and Engineering,Central South University,Changsha 410000,Hunan,China
  • Received:2023-02-06 Revised:2023-03-28 Online:2023-06-30 Published:2023-07-05
  • Contact: Fei GUO

摘要:

天然酶具有绿色环保、高效催化的优点,但由于工业环境的酸碱性、温度等条件不够适宜,天然酶在实际工业生产中往往存在错误折叠、功能受限等问题。使用人工智能技术辅助酶的改造设计,相比传统方法具有高效、快速、低成本的优势,但在这个过程中大部分工作没有考虑设计改造酶的“可折叠性”问题。同时,最近几年来,以AlphaFold2为代表的蛋白质结构预测工具借助人工智能技术取得了突破性的进展,已经具有原子级别的结构预测精度。这一工具的日益成熟,不仅有助于对蛋白结构功能机制的了解,同时可以丰富现有酶结构数据,用于后续的研究。因此,基于现有酶改造以及从头设计新酶过程中出现的错误折叠导致成功率不高、实验验证成本高的问题,我们认为结合蛋白质结构预测工具辅助酶的改造设计任务,可以增加设计可靠酶的数量,同时降低实验成本。本文首先梳理回顾人工智能技术在酶设计改造中的应用,主要从序列和结构两个角度展开。然后将现有蛋白质结构预测工具归纳成四种类型分别介绍其设计原理和预测能力。接着以AlphaFold2为代表性工作,归纳了三种在现有技术基础上利用结构预测工具进一步提高酶改造的合理性以及酶设计的“可折叠性”的方式:①结构“分析器”;②突变“筛选器”;③折叠“监督器”。最后在讨论部分总结并提出了一些现有算法的不足和缺陷。随着人工智能技术的逐渐发展以及人类对蛋白质作用机理的研究,酶的改造设计精度一定会有所提高,这将助力合成生物学的快速发展。

关键词: 人工智能, 合成生物学, 蛋白质设计, 蛋白质结构预测, 可折叠

Abstract:

Natural enzymes often have advantages of environmental friendliness, high catalytic efficiency and so on. However, due to inappropriate pH, temperature and other conditions in industrial environment, the application of natural enzymes in industrial production is unsatisfactory owing to challenges such as misfolding of proteins and limited functions. Compared with traditional methods, enzyme design and engineering with the help of artificial intelligence (AI) have advantages of high efficiency, high speed and low cost, but most work does not consider the 'foldability' in the process of enzyme engineering. A designed enzyme may fold to another state for minimum energy, so called misfolding. As we all know, protein design is regarded as an inverse folding process. Can we utilize protein folding tools to constrain the foldability of the designed enzyme? In recent years, protein structure prediction tools represented by AlphaFold2 have made breakthroughs with the help of AI for accuracy at atomic levels, which enriches existing enzyme structure data for subsequent studies to address the above question. Therefore, we discuss applying protein structural tools to fulfill the task of enzyme design and engineering, increase the proportion of reliable enzymes designed and reduce the cost of experiments. Firstly, we review the application of artificial intelligence technology in enzyme design and engineering from the perspective of sequence and structure. Then, we summarize existing protein structure prediction tools into four types and introduce their methods and prediction ability respectively. Furthermore, taking AlphaFold2 as an example, we group the applications which improve the rationality of enzyme modification and the "foldability" of design into three categories: 1) Structure 'Analyzer', 2) Mutation 'Filter' and 3) Folding 'Monitor'. Finally, we highlight drawbacks with existing algorithms for further improvements. With the rapid development of AI and understanding on protein function mechanism, the precision of enzyme modifications and designs will be increased.

Key words: Artificial intelligence, Synthetic biology, Protein design, Protein structure prediction, Foldability

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