深度学习在蛋白质功能预测中的应用

宋益东; 袁乾沐; 杨跃东

doi:10.12211/2096-8280.2022-078

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深度学习在蛋白质功能预测中的应用

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- 深度学习在蛋白质功能预测中的应用
  封面论文
- Application of deep learning in protein function prediction
- 合成生物学 2023年4卷第3期页码：488-506
- 作者机构：
  
  中山大学计算机学院，广东广州 510000
- 作者简介：
  
  [ "宋益东（1998—），男，博士研究生。研究方向为蛋白质功能预测、蛋白质无序预测等。 E-mail：songyd6@mail2.sysu.edu.cn" ]
  [ "杨跃东（1980—），男，教授，博士生导师。研究方向为高性能生物信息计算，蛋白质结构与功能预测，智能药物设计，跨尺度多组学数据挖掘，及生物医药超算平台。 E-mail：yangyd25@mail.sysu.edu.cn" ]
- 基金信息：
  
  国家重点研发计划(2022YF1203100);国家自然科学基金(12126610)
- DOI：10.12211/2096-8280.2022-078
  中图分类号： Q71
- 收稿：2022-12-31，
  
  修回：2023-03-07，
  
  纸质出版：2023-06-30
- 稿件说明：
移动端阅览
宋益东，袁乾沐，杨跃东. 深度学习在蛋白质功能预测中的应用［J］. 合成生物学， 2023， 4（3）： 488-506

SONG Yidong， YUAN Qianmu， YANG Yuedong. Application of deep learning in protein function prediction［J］. Synthetic Biology Journal， 2023， 4（3）： 488-506
宋益东，袁乾沐，杨跃东. 深度学习在蛋白质功能预测中的应用［J］. 合成生物学， 2023， 4（3）： 488-506 DOI： 10.12211/2096-8280.2022-078.

SONG Yidong， YUAN Qianmu， YANG Yuedong. Application of deep learning in protein function prediction［J］. Synthetic Biology Journal， 2023， 4（3）： 488-506 DOI： 10.12211/2096-8280.2022-078.

摘要

蛋白质功能预测是生物信息学中的一项重要任务，在疾病机制的阐明和药物靶点发现等领域有着重要作用。因为传统的测定蛋白质功能的生化实验通常成本高、耗时长、通量低，所以开发出高效且准确的蛋白质功能预测计算方法十分重要。蛋白质功能预测可以分为残基水平的结合位点预测和蛋白水平的基因本体论（gene ontology， GO）预测。本文首先介绍该领域常用的数据库及蛋白质特征信息，接着对当下最新的蛋白质功能预测方法进行总结。在结合位点预测方面，根据配体类型分别介绍了最新的蛋白质-蛋白质、蛋白质-多肽、蛋白质-核酸和蛋白质-小分子或离子配体的结合位点预测方法；在GO预测方面，按照预测方法的类别分别介绍了最近的基于序列、基于结构和基于蛋白相互作用网络的方法。最后，对目前的蛋白质功能预测方法进行总结、分析优劣，并展望该领域未来的发展方向。

Abstract

Protein function prediction is essential for bioinformatics analysis

which benefits a wide range of biological studies such as understanding the functions of metagenomes

uncovering mechanism underlying diseases

and finding new drug targets. With the rapid development of high-throughput sequencing technology

protein sequence data have been increased quickly

but functions of most proteins have not yet been identified. Since traditional biochemical experiments to determine protein functions are usually expensive

time-consuming

and less efficient

developing more efficient and effective computational methods for protein function prediction is of great significance. Deep learning technology has made breakthroughs in many fields

including image recognition

natural language processing

genomic analysis and drug discovery. In this review

we address applications of deep learning in protein function prediction

which can be divided into residue-level binding site prediction and protein-level gene ontology (GO) prediction. Protein binding sites are regions that bind to specific ligands

which play an important role in signal transduction

metabolism

revealing molecular mechanisms underlying diseases

and designing new drugs. Gene ontology is a standard function classification system for genes

which provides a set of annotations to comprehensively describe the properties of genes and gene products. Firstly

we introduce commonly used large-scale protein structure and function databases. Secondly

discriminative protein sequence and structure features are described. Thirdly

we summarize the latest protein function prediction methods: in terms of the prediction of binding sites

we introduce the latest methods based on the ligand type

including protein

peptide

nucleic acid and small molecule as well as ion ligand

and in the aspect of GO prediction

we highlight the latest sequence-based

structure-based

and protein interaction network-based methods developed with protein information. Finally

we comment the advantages and disadvantages of the current protein function prediction methods

and discuss the future development in this field.

关键词

Keywords

references

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