Prediction of protein complex structure: methods and progress

doi:10.12211/2096-8280.2022-079

Abstract

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.

Key words: protein complex, protein-protein interaction, inter-chain contact prediction, protein docking, structure prediction

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。

关键词: 蛋白质复合物, 蛋白质相互作用, 蛋白质链间接触预测, 蛋白质分子对接, 结构预测

CLC Number:

Q816

He HUANG, Tong WU, Wenda WANG, Jiashan LI, Daiwen SUN, Qiwei YE, Xinqi GONG. 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.

Figures/Tables 3

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方法	输入特征				网络架构	任务
方法	共进化	单体距离图	单体结构	蛋白语言模型	残差网络	同源	异源
ComplexContact^[48]	√				√		√
Glinter^[61]		√	√	√	√ + 图学习	√	√
DeepHomo^[64]	√	√			√	√
DeepHomo2^[65]	√	√		√	√	√
DRcon^[66]	√	√			√ + 空洞卷积	√
DeepInteract^[62]			√		几何深度学习		√
CDPred^[63]	√	√		√	√ + 注意力机制	√	√
PDII^[69]		√			图像修复	√	√
PGT^[67]		√		√	√ + 图注意力+三角更新	√

方法	输入特征				网络架构	任务
方法	共进化	单体距离图	单体结构	蛋白语言模型	残差网络	同源	异源
ComplexContact^[48]	√				√		√
Glinter^[61]		√	√	√	√ + 图学习	√	√
DeepHomo^[64]	√	√			√	√
DeepHomo2^[65]	√	√		√	√	√
DRcon^[66]	√	√			√ + 空洞卷积	√
DeepInteract^[62]			√		几何深度学习		√
CDPred^[63]	√	√		√	√ + 注意力机制	√	√
PDII^[69]		√			图像修复	√	√
PGT^[67]		√		√	√ + 图注意力+三角更新	√

[1]	Qiaozhen MENG, Fei GUO. Applications of foldability in intelligent enzyme engineering and design: take AlphaFold2 for example [J]. Synthetic Biology Journal, 2023, 4(3): 571-589.
[2]	Yiming TANG, Yifei YAO, Zhongyuan YANG, Yun ZHOU, Zichao WANG, Guanghong WEI. Pathological aggregation and liquid-liquid phase separation of proteins associated with neurodegenerative diseases [J]. Synthetic Biology Journal, 2023, 4(3): 590-610.