深度学习在基于序列的蛋白质互作预测中的应用进展

doi:10.12211/2096-8280.2023-074

合成生物学 ›› 2024, Vol. 5 ›› Issue (1): 88-106.DOI: 10.12211/2096-8280.2023-074

深度学习在基于序列的蛋白质互作预测中的应用进展

朱景勇¹^,²^,³, 李钧翔³^,⁴, 李旭辉³^,⁵, 张瑾², 毋文静²

^1.浙江理工大学生命科学与医药学院，浙江杭州 310018
^2.嘉兴学院生物与化学工程学院，浙江嘉兴 314000
^3.浙江清华长三角研究院，衰老科学创新研发中心，浙江嘉兴 341001
^4.禾美生物科技（浙江）有限公司，浙江嘉兴 341001
^5.浙江清华长三角研究院，浙江省应用酶学重点实验室，浙江嘉兴 314006

收稿日期:2023-10-24 修回日期:2023-11-28 出版日期:2024-02-29 发布日期:2024-03-20
通讯作者: 张瑾
作者简介:朱景勇（1998—），男，硕士研究生。研究方向为深度学习预测蛋白质互作。 E-mail：jingyongzhu2016@163.com
张瑾（1976—），男，教授，硕士生导师。研究方向为代谢疾病的致病机理及药物靶点发掘。 E-mail：zhangjin7688@163.com
基金资助:
国家自然科学基金(32172708);浙江省自然科学基金重点项目(LZ23C170002)

Advances in applications of deep learning for predicting sequence-based protein interactions

Jingyong ZHU¹^,²^,³, Junxiang LI³^,⁴, Xuhui LI³^,⁵, Jin ZHANG², Wenjing WU²

^1.College of Life Sciences and Medicine，Zhejiang Sci-tech University，Hangzhou 310018，Zhejiang，China
^2.College of Biological Chemical Sciences and Engineering，Jiaxing University，Jiaxing 314000，Zhejiang，China
^3.Agecode R&D Center，Yangtze Delta Region Institute of Tsinghua University，Jiaxing 341001，Zhejiang，China
^4.Harvest Biotech. Co. ，Ltd. ，Jiaxing 341001，Zhejiang，China
^5.Zhejiang Provincial Key Laboratory of Applied Enzymology，Yangtze Delta Region Institute of Tsinghua University，Jiaxing 314006，Zhejiang，China

Received:2023-10-24 Revised:2023-11-28 Online:2024-02-29 Published:2024-03-20
Contact: Jin ZHANG

摘要/Abstract

摘要：

蛋白质-蛋白质相互作用在细胞信号转导、基因表达和代谢调控等生物过程中发挥重要作用，鉴定蛋白质间的相互作用对于理解复杂生物过程至关重要。预测蛋白质间的相互作用可以为药物发现、蛋白质功能研究和设计等领域提供帮助。近年来，随着人工智能技术的蓬勃发展，深度学习技术在预测蛋白质互作领域做出巨大贡献，其中基于序列的深度学习模型通过学习蛋白质序列信息的深层特征进行互作预测。本文综述了深度学习在基于序列的蛋白质互作预测中的应用，按照算法框架和时间线对该领域进展进行分类归纳，介绍了数据处理、序列编码方法、算法架构以及模型的评估指标等内容，并分析了当前面临的问题以及未来的发展方向。随着深度学习技术的发展，预测蛋白质互作的效率大幅提高，未来需要发展泛化能力更强的预测模型，助力蛋白质互作的预测。

关键词: 蛋白质互作, 深度学习, 人工智能, 序列编码, 神经网络

Abstract:

Protein-protein interactions play a crucial role in biological processes such as cell signal transduction, gene expression and metabolic regulation, and thus their identification is essential for understanding these complex biological processes. Predicting protein-protein interactions is a hot topic of great significance, which can provide assistances in areas such as drug discovery and protein function research and design as well. In recent years, with the development of artificial intelligence, machine learning technologies have been applied gradually to the prediction of protein-protein interactions, which has shown good potentials. However, when processing a large amount of protein information, traditional machine learning methods are difficult to mine the intrinsic patterns and potential features, and deep learning techniques are needed. Compared with the three-dimensional structure of proteins, sequence information is easier to obtain, and the development of high-throughput sequencing technology provides abundant protein sequence information, which greatly facilitates the development of sequence-based deep learning technologies. Sequence-based deep learning models predict protein-protein interactions by learning intrinsic patterns and features from protein sequence information, which greatly improves prediction efficiency and accuracy. In this review, we focus on progress of deep learning in predicting sequence-based protein interactions, categorize, which is summarized according to the algorithmic framework and timeline, briefly describing the construction methods of datasets and the evaluation metrics of the models, discussing in detail the sequence encoding methods and common algorithmic architectures, and demonstrating the computational models based on various types of algorithms and their features and advantages. Finally, we analyze current challenges in predicting protein-protein interactions using deep learning methods, and discuss possible solutions. With the development of deep learning technology, the efficiency of predicting protein-protein interactions has increased dramatically. As a result, there is a need to develop models with stronger generalization and more robust prediction capabilities to aid the prediction of protein-protein interactions in the future.

Key words: protein interactions, deep learning, artificial intelligence, sequence encoding, neural network

中图分类号:

Q816

朱景勇, 李钧翔, 李旭辉, 张瑾, 毋文静. 深度学习在基于序列的蛋白质互作预测中的应用进展[J]. 合成生物学, 2024, 5(1): 88-106.

Jingyong ZHU, Junxiang LI, Xuhui LI, Jin ZHANG, Wenjing WU. Advances in applications of deep learning for predicting sequence-based protein interactions[J]. Synthetic Biology Journal, 2024, 5(1): 88-106.

图/表 9

图1 利用深度学习基于序列预测蛋白质相互作用的一般流程

Fig. 1 General workflow for predicting protein-protein interactions by sequence-based deep learning

表1 常用的数据库以及基本信息

Table 1 Public databases and basic information

数据库名称Database name	简介 Description	链接 URL	最近更新 Last update	参考文献Reference
BioGRID	以蛋白质为核心的互作数据库	https://thebiogrid.org	2023	[25]
DIP	通过实验验证和文献确认的PPI互作信息	https://dip.doe-mbi.ucla.edu/dip	2020	[27]
HIPPIE	人工整合的超过60 000条PPI互作数据	https://cbdm.uni-mainz.de/hippie	2023	[30]
HPRD	人类PPI数据库，包括41 327对互作信息	https://hprd.org	2010	[26]
HVIDB	HVIDB重点介绍了48 643个经过实验验证的人与病毒PPI	http://zzdlab.com/hvidb/	2020	[31]
Intact	从22 954份文献中提取1 194 594份互作数据信息	https://www.ebi.ac.uk/intact/home	2022	[32]
Negatome	通过整理文献和分析蛋白质复合物的三维结构得到的非相互作用信息	http://mips.helmholtz-muenchen.de/proj/ppi/negatome	2014	[29]
STRING	蛋白质互作数据库，涉及14 094种生物的67 592 464个蛋白质	https://cn.string-db.org	2023	[33]

表2 基于序列的PPI预测模型中常见的蛋白质编码方法

Table 2 Protein encoding methods in sequence-based PPI prediction models

类型 Type	编码方法 Encoding method	形式 Form	向量长度 Vector length	参考文献 Reference
基于序列成分	二肽组成 dipeptide composition	一维向量	400	[36]
	氨基酸组成 amino acid composition	一维向量	20	[37]
	伪氨基酸组成 pseudo-amino acid composition	一维向量	20+L，L为最大滞后值	[38]
	联合三元组 conjoint triad	一维向量	343	[16]
基于自相关	自协方差 auto covariance	一维向量	L×理化性质个数，L为最大滞后值	[39]
	交叉协方差 cross covariance	一维向量	L×N×(N-1)，L为最大滞后值，N为理化性质个数	[40]
	自交叉协方差 auto-cross covariance	一维向量	L×N×N，L为最大滞后值，N为理化性质个数	[41]
	Moran自相关 Moran autocorrelation	一维向量	L×理化性质个数，L为最大滞后值	[42]
	Geary自相关 Geary autocorrelation	一维向量	L×理化性质个数，L为最大滞后值	[43]
	物理化学距离变换 physicochemical distance transformation	一维向量	531×β，β为最大间隔距离	[44]
基于进化信息	位置特异性得分矩阵PSSM	二维矩阵	20×N，N为序列长度	[45]
	ACC-PSSM	一维向量	400×L，L为最大滞后值	[46]
	Top-n-grams	一维向量	L×N，L为序列长度，N为阈值	[47]
	BLOSUM62	二维矩阵	20×N，N为序列长度	[48]
	伪位置特异性得分矩阵Pseudo-PSSM	一维向量	40	[49]

图2 利用one-hot将氨基酸序列转化为向量矩阵

Fig.2 Converting amino acid sequences into vector matrices using the one-hot strategy

表3 近5年相关的计算模型

Table 3 Computational models developed within the past 5 years

类型 Type	发表时间 Published time	模型名称 Model name	算法框架 Algorithm framework	预测类型 Prediction type	参考文献 Reference
基于DNN	2018	—	DNN	PPI	[73]
	2019	EnsDNN	DNN	PPI	[74]
	2019	—	DNN	PPI	[75]
	2019	—	DNN	PPI	[76]
	2019	DeepFEPPI	DNN	PPI	[77]
	2019	DNN-PPI	DNN	PPI	[78]
	2020	—	DNN	PPI	[79]
	2020	—	DNN	PPI	[80]
	2022	DNN-XGB	DNN，XGB	PPI	[81]
	2022	DWPPI	DNN	PPI	[82]
	2022	—	DNN	PPI	[83]
	2022	CT-DNN	DNN	PPI	[84]
	2022	—	DNN	PPI	[85]
基于CNN	2018	DPPI	CNN	PPI	[86]
	2019	—	CNN-RF	PPI	[87]
	2019	—	CNN	PPI	[88]
	2020	Visual	CNN	PPIsite	[89]
	2020	DeepPPISP	TextCNN	PPIsite	[90]
	2020	EnAmDNN	CNN	PPI	[91]
	2020	—	CNN	PPIsite	[92]
	2021	TransPPI	CNN	PPI	[93]
	2021	—	CNN	PPI	[94]
	2021	D-script	CNN	PPI	[95]
	2021	CAMP	CNN	PepPI	[57]
	2021	DeepViral	CNN	PPI	[96]
	2022	DeepTrio	CNN	PPI	[97]
	2023	EResCNN	ResCNN+RF	PPI	[98]
	2023	ProtInteract	CNN	PPI	[99]
基于RNN及变体	2019	—	RNN	PPI	[100]
	2019	DLPred	LSTM	PPIsite	[101]
	2019	—	LSTM	PPI	[102]
	2020	—	LSTM	PPI	[103]
	2021	—	GRNN	PPI	[104]
	2021	LSTM-PHV	LSTM	PPI	[105]
	2023	—	RNN	PPI	[106]
基于注意力机制和Transformer	2021	HANPPIS	Stratified attention	PPIsite	[107]
	2022	Cross-attention PHV	Cross-attention	PPI	[108]
	2022	ADH-PPI	Self-attention	PPI	[109]
	2022	SDNN	Self-attention	PPI	[110]
	2023	EnsemPPIS	Transformer	PPIsite	[111]
基于混合网络	2018	DNN-PPI	CNN+LSTM	PPI	[112]
	2019	PIPR	RNN+CNN	PPI	[113]
	2019	IPPI	DNN+LSTM	PPI	[114]
	2021	DELPHI	RNN+CNN	PPIsite	[115]
	2021	OR-RCNN	RNN+CNN	PPI	[116]
	2023	MM-StackEns	SNN+GNN	PPI	[117]

图3 DNN、RNN、CNN的基本网络结构（a）DNN包含输入层，多个隐藏层，输出层；（b）RNN包括一个输入单元Input、一个隐藏单元Hidden和一个输出单元Output。将RNN按时间展开，符号的下标代表时间，意味着H t 接收来自I t 和H t-1的输入，然后将计算结果传播给O t 和H t+1；（c）CNN包括输入层，多个卷积层和池化层以及全连接层和输出层

Fig. 3 Basic network structure of DNN, RNN, and CNN(a) DNN contains an input layer, multiple hidden layers, and an output layer; (b) RNN consists of an input unit (Input), a hidden unit (Hidden), and an output unit (Output) to unfold the RNN in time, with the subscript of the symbols representing the time, indicating that H t receives the inputs from I t and H t-1, and then propagates the results of the computation to O t and H t+1; (c) CNNs include an input layer, multiple convolutional and pooling layers, and fully connected and output layers

图4 注意力机制和Transformer的基本结构（a）详细描绘了注意力机制的核心操作，包括Query、Key、与Key对应的Value。首先通过F（Q，K）计算每一个Query和Key的相似性得分（S1～S4）。这些得分经过Softmax函数归一化得到每个Key的权重（A1～A4）。每个Key有一个与之对应的“值”（Value1～Value4），将每个Value与其相应的权重加权求和最终得到Attention value。（b）引用自文献［72］，展示了Transformer模型的核心架构，分为编码器和解码器两部分。左侧的编码器首先接受输入并通过“输入嵌入”与“位置编码”进行预处理，然后多次经过包含“多头注意力”机制和前馈神经网络的结构单元，目标是将输入序列转化为一个上下文丰富的连续向量表示。右侧的解码器则负责根据编码器提供的上下文信息生成输出序列。它的输入初步为“Outputs （shifted right）”，确保在解码过程中当前位置的输出仅依赖于前面的信息。解码器同样经过多次的“多头注意力”和前馈网络处理，最终通过线性变换和Softmax层得到输出概率分布，代表各个可能输出的概率

Fig. 4 Basic structure of the basic structure of Attention mechanism and Transformer(a) Providing a detailed depiction of the core operations in the attention mechanism, such as Query, Key, and corresponding value for each Key. The similarity scores (S1 to S4) between each Query and Key are first computed using the function F (Q, K), which are then normalized through the Softmax function to derive weights for each Key (A1 to A4). Every Key is associated with a "value" (Value 1 to Value 4). The final attention value is derived by summing up the product of each value and its respective weight. (b) Adapted from reference [72], showcasing the fundamental architecture of the transformer model that is split into encoder and decoder components. The encoder on the left initially receives inputs and pre-processes them through "input embedding" and "positional encoding", and then repeatedly passes them through structural units containing the "multi-head attention" mechanism and feed-forward neural networks. The objective is to transform the input sequence into a context-rich continuous vector representation. On the right, the decoder is responsible for producing an output sequence based on the context provided by the encoder. Its initial input is labeled as "Output", ensuring that the output at the current position in the decoding process only depends on prior information. The decoder also undergoes multiple rounds of "multi-head attention" and feed-forward network processing, ultimately yielding an output probability distribution via linear transformation and a Softmax layer, representing a probability for the potential output

表4 四种基本预测结果及其具体含义

Table 4 Four basic prediction results and their specific meanings

预测结果 Prediction result	英文名称 English name	描述 Description
真阳性	true positive, TP	对阳性样本预测为阳性
假阳性	false positive, FP	对阴性样本预测为阳性
真阴性	true negative, TN	对阴性样本预测为阴性
假阴性	false negative, FN	对阳性样本预测为阴性

表5 六种评估指标及其计算公式

Table 5 Six evaluation metrics and their calculation formulas

评估指标 Evaluation index	计算公式 Calculation formula
准确率(accuracy)	$T P + T N T P + T N + F P + F N$
精准率(precision)	$T P T P + F P$
敏感性(sensitivity)	$T P T P + F N$
特异性(specificity)	$T N T N + F P$
F1值(F1 score)	$2 T P 2 T P + F N + F P$
马修斯相关系数(Matthews correlation coefficient)	$T P × T N - F P × F N T P + F P T P + F N T N + F P T N + F N$

表5 六种评估指标及其计算公式

Table 5 Six evaluation metrics and their calculation formulas

评估指标 Evaluation index	计算公式 Calculation formula
准确率(accuracy)	$T P + T N T P + T N + F P + F N$
精准率(precision)	$T P T P + F P$
敏感性(sensitivity)	$T P T P + F N$
特异性(specificity)	$T N T N + F P$
F1值(F1 score)	$2 T P 2 T P + F N + F P$
马修斯相关系数(Matthews correlation coefficient)	$T P × T N - F P × F N T P + F P T P + F N T N + F P T N + F N$

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Advances in applications of deep learning for predicting sequence-based protein interactions

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