Application of deep learning in protein function prediction

doi:10.12211/2096-8280.2022-078

Abstract

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.

Key words: deep learning, protein, function prediction, binding site, gene ontology

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。

关键词: 深度学习, 蛋白质, 功能预测, 结合位点, 基因本体论

CLC Number:

Yidong SONG, Qianmu YUAN, Yuedong YANG. 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.

Figures/Tables 3

Table 1 Commonly used databases

名称	内容	下载
PDB数据库	蛋白质结构	https://www.rcsb.org/
BioLiP数据库	配体-蛋白质相互作用数据	https://zhanggroup.org/BioLiP/
UniProt数据库	蛋白质序列和注释数据	https://www.uniprot.org/
GO数据库	细胞组分，分子功能，生物学过程	http://geneontology.org/
GOA数据库	基因本体注释数据	https://www.ebi.ac.uk/GOA/

Table 2 Summary of the latest binding site prediction methods

方法		数据来源	年份	特征^①	算法	是否开源^②
蛋白-蛋白	SPPIDER^[45]	PDB	2007	物理化学性质，基于MSA的进化信息，DSSP结构信息，dSA （预测的和真实RSA的差值）	全连接神经网络	S
	SCRIBER^[46]	BioLip	2019	相对溶剂可及性，进化保守性，相对氨基酸结合倾向性，物理化学性质，内部无序性，二级结构，残基位置	逻辑回归	S
	DELPHI	PDB, BioLip	2020	高分值片段对，ProtVec1D，PSSM，进化保守性，相对溶剂可及性，相对氨基酸结合倾向性，亲水性，内部无序性，物理化学性质，PKx，位置信息	CNN+GRU	S, C
	DeepPPISP^[47]	PDB	2020	PSSM，二级结构，one-hot蛋白序列	CNN	S, C
	MaSIF^[48]	—	2020	表面几何与物理化学特征，如局部曲率、Poisson-Boltzmann静电、氢键供体或受体以及亲水性	几何深度学习	C
	GraphPPIS	PDB	2021	PSSM，HMM，DSSP	GCN	S, C
蛋白-多肽	SPRINT^[49]	PDB	2016	one-hot蛋白序列，PSSM，相对溶剂可及性，二级结构，物理化学性质	SVM	S
	PepBind^[50]	BioLiP	2018	PSSM，HMM，二级结构，内部无序性	SVM+基于模板的方法	S
	Visual^[51]	BioLiP	2020	PSSM，半球暴露，二级结构，溶剂可及性，扭转角，物理化学性质	CNN	C
	$B i t e N e t p p$	BioLip	2021	体素化的11种原子密度	3D CNN	S, C
	PepNN	PDB	2022	残基间距离，C_α的相对方向，局部坐标系间旋转矩阵，残基的相对位置，one-hot蛋白序列，扭转骨架角，语言模型特征	互注意力机制+GNN	C
蛋白-核酸	DNAPred^[52]	PDB	2019	PSSM，预测的二级结构和溶剂可及性，结合与非结合氨基酸的频率差	SVM	S
	NucBind^[53]	PDB	2019	PSSM，HMM，预测的二级结构，预测结构	SVM+COACH-D^[54]	S
	NCBRPred^[55]	—	2021	PSSM，HMM，预测的二级结构和溶剂可及性	GRU	S, C
	GraphBind	BioLiP	2021	残基的原子特征，DSSP，PSSM，HMM	GNN	S, C
	GraphSite	BioLiP	2022	AlphaFold2 single特征，PSSM，HMM，DSSP	Graph Transformer	S, C
蛋白-小分子或离子配体	TargetS^[56]	PDB	2013	PSSM，预测的二级结构，相对氨基酸结合倾向性	AdaBoost	S
	IonCom^[19]	BioLiP	2016	PSSM，预测的二级结构和溶剂可及性，保守性，氨基酸的离子结合频率，预测结构	AdaBoost+SVM+COFACTOR^[57]+ S-SITE^[18]+ TM-SITE^[18]	S, C
	MIB^[16]	PDB	2016	结构模板数据	Fragment Transformation	S
	DELIA	BioLip	2020	PSSM，HMM，二级结构，可溶性，S-SITE特征，基于结构的距离矩阵	CNN	S
	LMetalSite	BioLiP	2022	语言模型特征	Transformer+ 多任务学习	S, C
综合不同类型配体	MTDsite	BioLip	2021	PSSM，HMM，SPIDER3，溶剂可及性表面积，扭转角，分界线内的残基数，半球暴露	BiLSTM+ 多任务学习	C
综合不同类型配体	DeepDISOBind	DisProt	2022	one-hot蛋白序列，相对氨基酸亲和性，二级结构，内部无序性	CNN+多任务学习	S, C

Table 2 Summary of the latest binding site prediction methods

方法		数据来源	年份	特征^①	算法	是否开源^②
蛋白-蛋白	SPPIDER^[45]	PDB	2007	物理化学性质，基于MSA的进化信息，DSSP结构信息，dSA （预测的和真实RSA的差值）	全连接神经网络	S
	SCRIBER^[46]	BioLip	2019	相对溶剂可及性，进化保守性，相对氨基酸结合倾向性，物理化学性质，内部无序性，二级结构，残基位置	逻辑回归	S
	DELPHI	PDB, BioLip	2020	高分值片段对，ProtVec1D，PSSM，进化保守性，相对溶剂可及性，相对氨基酸结合倾向性，亲水性，内部无序性，物理化学性质，PKx，位置信息	CNN+GRU	S, C
	DeepPPISP^[47]	PDB	2020	PSSM，二级结构，one-hot蛋白序列	CNN	S, C
	MaSIF^[48]	—	2020	表面几何与物理化学特征，如局部曲率、Poisson-Boltzmann静电、氢键供体或受体以及亲水性	几何深度学习	C
	GraphPPIS	PDB	2021	PSSM，HMM，DSSP	GCN	S, C
蛋白-多肽	SPRINT^[49]	PDB	2016	one-hot蛋白序列，PSSM，相对溶剂可及性，二级结构，物理化学性质	SVM	S
	PepBind^[50]	BioLiP	2018	PSSM，HMM，二级结构，内部无序性	SVM+基于模板的方法	S
	Visual^[51]	BioLiP	2020	PSSM，半球暴露，二级结构，溶剂可及性，扭转角，物理化学性质	CNN	C
	$B i t e N e t p p$	BioLip	2021	体素化的11种原子密度	3D CNN	S, C
	PepNN	PDB	2022	残基间距离，C_α的相对方向，局部坐标系间旋转矩阵，残基的相对位置，one-hot蛋白序列，扭转骨架角，语言模型特征	互注意力机制+GNN	C
蛋白-核酸	DNAPred^[52]	PDB	2019	PSSM，预测的二级结构和溶剂可及性，结合与非结合氨基酸的频率差	SVM	S
	NucBind^[53]	PDB	2019	PSSM，HMM，预测的二级结构，预测结构	SVM+COACH-D^[54]	S
	NCBRPred^[55]	—	2021	PSSM，HMM，预测的二级结构和溶剂可及性	GRU	S, C
	GraphBind	BioLiP	2021	残基的原子特征，DSSP，PSSM，HMM	GNN	S, C
	GraphSite	BioLiP	2022	AlphaFold2 single特征，PSSM，HMM，DSSP	Graph Transformer	S, C
蛋白-小分子或离子配体	TargetS^[56]	PDB	2013	PSSM，预测的二级结构，相对氨基酸结合倾向性	AdaBoost	S
	IonCom^[19]	BioLiP	2016	PSSM，预测的二级结构和溶剂可及性，保守性，氨基酸的离子结合频率，预测结构	AdaBoost+SVM+COFACTOR^[57]+ S-SITE^[18]+ TM-SITE^[18]	S, C
	MIB^[16]	PDB	2016	结构模板数据	Fragment Transformation	S
	DELIA	BioLip	2020	PSSM，HMM，二级结构，可溶性，S-SITE特征，基于结构的距离矩阵	CNN	S
	LMetalSite	BioLiP	2022	语言模型特征	Transformer+ 多任务学习	S, C
综合不同类型配体	MTDsite	BioLip	2021	PSSM，HMM，SPIDER3，溶剂可及性表面积，扭转角，分界线内的残基数，半球暴露	BiLSTM+ 多任务学习	C
综合不同类型配体	DeepDISOBind	DisProt	2022	one-hot蛋白序列，相对氨基酸亲和性，二级结构，内部无序性	CNN+多任务学习	S, C

Table 3 Summary of the latest GO prediction methods

	方法	年份	特征	算法	是否开源^①
基于序列	GOLabeler	2018	GO词频，序列对比信息，氨基酸三联体（3 mer），蛋白家族信息，结构域和基序，ProFET^[58]序列特征	LTR	S, C
	DeepGOPlus	2020	基于序列和基序的功能信息	CNN	S, C
	TALE^[25]	2021	one-hot蛋白序列，GO层次结构矩阵、序列相似性	Transformer+CNN	C
	GAT-GO	2022	one-hot蛋白序列，PSSM，HMM，ESM-1b嵌入信息	GAT
	DeeProtGO^[72]	2022	SeqVec序列嵌入、序列相似性、物种分类、InterPro蛋白结构域和蛋白家族信息、GO注释信息	层次化的全连接神经网络	C
基于结构	COFACTOR^[73]	2017	蛋白序列、结构信息和PPI网络	序列比对+结构比对+ 基于网络邻居的功能聚合	S
基于结构	DeepFRI	2021	蛋白质接触图，语言模型特征	GCN	S, C
基于网络	DeepGO^[74]	2018	蛋白序列，PPI网络	CNN+层次化的全连接神经网络	S, C
	NetGO	2019	GO词频，序列对比信息，氨基酸三联体（3 mer），蛋白家族信息，结构域和基序，ProFET^[58]序列特征，蛋白质相互作用网络	LTR	S
	NetGO 2.0	2021	GO词频，基于序列信息，蛋白质相互作用网络，序列中的深层模式，文献信息	LTR	S
	S2F	2021	同源信息，HMMER特征，InterPro特征，进化信息，PPI网络	label diffusion	S, C
	DeepGraphGO	2021	InterPro特征，PPI网络	GCN	C

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