蛋白质复合物结构预测：方法与进展

黄鹤; 吴桐; 王闻达; 李佳珊; 孙黛雯; 叶启威; 龚新奇

doi:10.12211/2096-8280.2022-079

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蛋白质复合物结构预测：方法与进展

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- 蛋白质复合物结构预测：方法与进展
- Prediction of protein complex structure: methods and progress
- 合成生物学 2023年4卷第3期页码：507-523
- 作者机构：
  
  1.中国人民大学数学科学研究院，北京 100872
  2.北京智源人工智能研究院，北京 100084
- 作者简介：
  
  [ "黄鹤（1995—），男，博士研究生。研究方向为蛋白质复合物结构预测算法等。 E-mail：hehuang@ruc.edu.cn" ]
  [ "龚新奇（1978—），男，教授，博士生导师。龚新奇课题组（数学智能应用实验室）在生物信息学方向构建数学模型、开发计算方法和并应用于研究多聚体超大蛋白质相互作用复合物的结构、网络和动力学等；在机器学习方向利用深度学习和大数据方法设计新的算法框架解决生物大分子和医疗图像的计算。 E-mail：xinqigong@ruc.edu.cn" ]
- 基金信息：
- DOI：10.12211/2096-8280.2022-079
  中图分类号： Q816
- 收稿：2022-12-31，
  
  修回：2023-03-20，
  
  纸质出版：2023-06-30
- 稿件说明：
移动端阅览
黄鹤，吴桐，王闻达，李佳珊，孙黛雯，叶启威，龚新奇. 蛋白质复合物结构预测：方法与进展［J］. 合成生物学， 2023， 4（3）： 507-523

HUANG He， WU Tong， WANG Wenda， LI Jiashan， SUN Daiwen， YE Qiwei， GONG Xinqi. Prediction of protein complex structure： methods and progress［J］. Synthetic Biology Journal， 2023， 4（3）： 507-523
黄鹤，吴桐，王闻达，李佳珊，孙黛雯，叶启威，龚新奇. 蛋白质复合物结构预测：方法与进展［J］. 合成生物学， 2023， 4（3）： 507-523 DOI： 10.12211/2096-8280.2022-079.

HUANG He， WU Tong， WANG Wenda， LI Jiashan， SUN Daiwen， YE Qiwei， GONG Xinqi. Prediction of protein complex structure： methods and progress［J］. Synthetic Biology Journal， 2023， 4（3）： 507-523 DOI： 10.12211/2096-8280.2022-079.

摘要

蛋白质复合物是不同蛋白质链通过相互作用形成的，自然界中很多蛋白质通过形成复合物而执行功能，因此准确地预测复合物的结构对于理解和掌握功能至关重要。近两年来，单条蛋白质链的结构预测有了突破性的进展，从氨基酸序列出发预测蛋白质结构的水平大幅提高。但相较于单体蛋白质，蛋白质复合物结构预测的准确性仍然较低。本文旨在总结蛋白质复合物结构预测的相关算法以及介绍最新进展。首先简要介绍蛋白质结构预测领域的相关人工智能算法，主要包括共进化分析与蛋白质接触预测、深度学习方法与蛋白质结构预测、预训练模型与蛋白质表征学习几个方面；其次系统总结了蛋白质复合物链间相互作用预测的基本方法，从复合物的多重序列比对构建到对于同源或异源复合物的链间残基接触预测；最后从相互作用位点指导复合物结构预测、蛋白质分子对接算法、端到端的复合物结构预测方法等方面阐述了蛋白质复合物结构预测的基本方法和思路。总体来说，目前蛋白质复合物结构预测精度不够高，有效地解决多重序列比对的配对和多聚体复合物模板搜索等问题，或者在大量的序列或结构数据上结合预训练模型的新范式，是一个合理而有效的方案。提升蛋白质复合物结构预测水平在合成生物学领域如抗体设计、药物发现等方面有很好的应用前景。

Abstract

Protein complexes carry out a variety of biological functions

and obtaining the three-dimensional structure of protein complexes is critical for understanding their functions. In many cases

not only can two proteins interact to form a protein dimer

but also multiple proteins interact to form a protein multimer. It is difficult and time-consuming to resolve the structure of protein complexes by experiments. Recently

there have been some attempts and methods to predict the structure of multimers based on the structure prediction for the monomers. Several groups in the CASP14 competition submitted the prediction of protein complex targets

which mainly included template -based methods or protein docking. Later

on the basis of AlphaFold2

researchers developed some end-to-end structure prediction methods for complexes

which accelerates the study of protein complex structure prediction. However

compared with the prediction of monomeric protein structure

the accuracy of prediction for protein complex structure is still lower. This review surveys updated methods and advances in protein complex prediction

including inter-chain residue contact prediction

protein docking

and end-to-end protein complex structure prediction. Firstly

AI algorithms for protein structure prediction are briefly introduced

including coevolutionary analysis and protein contact prediction

deep learning method and protein structure prediction

pretraining model

and protein representation learning. Secondly

basic methods for predicting interactions between protein complexes are systematically summarized

from the construction of multiple sequence alignments of the complexes to the prediction of the inter-residue contact between chains of homologous or heterologous complexes. Finally

basic methods and ideas for protein complex structure prediction are explored from the viewpoint of interaction sites guiding complex structure prediction

protein molecular docking algorithm

end-to-end complex structure prediction methods

etc. In order to better predict the structure of protein complexes

we need to devote our effort to following aspects: 1) constructing protein complexes datasets for training and evaluation of prediction methods for the structure of multimers

2) developing efficient algorithms to improve the prediction accuracy such as MSA paring algorithm and building templates for multi-chain protein complex

and 3) enlarging databases for protein sequences and structures for better modeling protein complex with pretraining and self-supervised learning methods. In all

predicting protein complex structure still remains a challenge

and new methods to improve accuracy will be helpful for analyzing protein functions

designing proteins and drug discovery.

关键词

Keywords

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