MR2CPPIS: Accurate prediction of protein–protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism

被引：2

作者：

Gong Y. ^{[1
,2
]}

Li R. ^{[1
,2
]}

Liu Y. ^{[1
,2
]}

Wang J. ^{[3
]}

Cao B. ^{[4
]}

Fu X. ^{[1
]}

Li R. ^{[1
,2
]}

Chen D.Z. ^{[5
]}

机构：

[1] College of Computer Science and Electronic Engineering, Hunan University, Changsha

[2] Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems, Hunan University, Changsha

[3] Peng Cheng Laboratory, Shenzhen

[4] College of Information and Electronic Engineering, Hunan City University, Yiyang

[5] Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, 46556, IN

来源：

Computers in Biology and Medicine | 2024年 / 176卷

基金：

中国国家自然科学基金;

关键词：

Coordinate attention; Multi-scale; PPI sites prediction; Res2Net; Sequence-based method;

D O I：

10.1016/j.compbiomed.2024.108543

中图分类号：

学科分类号：

摘要：

Proteins play a vital role in various biological processes and achieve their functions through protein–protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://github.com/YyinGong/MR2CPPIS. © 2024 Elsevier Ltd

引用

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