Research progress of artificial intelligence in desiging protein structures

CHEN Zhihang; JI Menglin; QI Yifei

doi:10.12211/2096-8280.2023-008

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Research progress of artificial intelligence in desiging protein structures

Invited Review | 更新时间：2025-03-20

- Research progress of artificial intelligence in desiging protein structures
- Synthetic Biology Journal Vol. 4, Issue 3, Pages: 464-487(2023)
- 作者机构：
  
  复旦大学药学院，上海 201203
- 作者简介：
- 基金信息：
- DOI：10.12211/2096-8280.2023-008
  CLC： Q816
- Received：13 January 2023，
  
  Revised：2023-03-15，
  
  Published：30 June 2023
- 稿件说明：
移动端阅览
陈志航，季梦麟，戚逸飞. 人工智能蛋白质结构设计算法研究进展［J］. 合成生物学， 2023， 4（3）： 464-487

CHEN Zhihang， JI Menglin， QI Yifei. Research progress of artificial intelligence in desiging protein structures［J］. Synthetic Biology Journal， 2023， 4（3）： 464-487
陈志航，季梦麟，戚逸飞. 人工智能蛋白质结构设计算法研究进展［J］. 合成生物学， 2023， 4（3）： 464-487 DOI： 10.12211/2096-8280.2023-008.

CHEN Zhihang， JI Menglin， QI Yifei. Research progress of artificial intelligence in desiging protein structures［J］. Synthetic Biology Journal， 2023， 4（3）： 464-487 DOI： 10.12211/2096-8280.2023-008.

摘要

蛋白质是各类生命活动不可缺少的承担者，其序列决定了折叠后的三维结构和功能。这些具有特定功能的蛋白质在生物医学等多个领域具有重要的应用价值。计算蛋白质设计可以根据所需的蛋白功能和结构设计氨基酸序列，生成自然界中不存在的蛋白质。传统计算蛋白质设计通常采用能量函数和特定的搜索优化算法获得设计的序列。近年来，随着先进算法的发展、大数据的积累和计算机硬件算力的增长，人工智能技术得到了蓬勃发展，并逐渐应用于蛋白质设计领域。本文综述了近年人工智能在蛋白质结构设计中的进展，侧重于各类算法的介绍，从固定骨架设计、可变骨架设计和序列结构生成三个方面回顾了最新的蛋白质结构设计算法，并阐明了其相对于传统计算方法的新颖性和创新性。在人工智能技术的赋能下，蛋白质设计的成功率和合理性获得大幅提高，按需功能蛋白设计的时代即将到来。

Abstract

Proteins are essential to life as they carry out a great variety of biological functions. Protein sequences determine their three-dimensional structures

and therefore physiological functions. Proteins with specific functions have important applications in many fields such as biomedicine

where they are utilized in drug design and delivery. In the past

protein engineering and directed evolution are commonly used to improve the activity and stability of proteins. These methods

however

are both complex and expensive

as they require a large number of biological experiments for validation. Computational protein design (CPD) allows the design of amino acid sequences based on desired protein functions and structures

and more intriguingly

generation of proteins even not found in nature. Conventional CPD uses energy function and optimization algorithm to design protein sequences. In recent years

with the rapid development of artificial intelligence (AI) technique

the accumulation of big data and the development of high speed computing

AI has made great progresses in learning

and been successfully applied in CPD. In this review

based on the input constraints and sampling space size

we present a systematic overview of recent applications of AI in protein design from three aspects: fixed-backbone design

flexible-backbone design

and sequence structure generation. We focus on algorithms and protein feature encoding

present the effect of dataset size and architectural improvements on model performance in prediction

and showcase several enzymes

antibodies

and binding proteins that were successfully designed using these models. The advantages of AI compared with traditional CPD methods are also discussed. Finally

we highlight challenges in AI-aided protein design

and propose some strategies for solutions.

关键词

Keywords

references

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