Membrane Protein Amphiphilic Helix Structure Prediction Based on Graph Convolution Network

被引：0

作者：

Jia, Baoli ^{[1
,2
]}

Meng, Qingfang ^{[1
,2
]}

Zhang, Qiang ^{[3
]}

Chen, Yuehui ^{[1
,2
]}

机构：

[1] Univ Jinan, Sch Informat Sci & Engn, Jinan 250022, Peoples R China

[2] Shandong Prov Key Lab Network Based Intelligent C, Jinan 250022, Peoples R China

[3] Inst Jinan Semicond Elements Expt, Jinan 250014, Peoples R China

来源：

INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2022, PT II | 2022年 / 13394卷

基金：

中国国家自然科学基金;

关键词：

Membrane protein; Amphiphilic helix; Structure prediction; Graph convolutional network; HYDROPHOBIC MOMENT; SEQUENCES; DATABASE; PDB;

D O I：

10.1007/978-3-031-13829-4_34

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The amphiphilic helix structure in membrane proteins is involved in membrane-related biological processes and has important research significance. In this paper, we constructed a new amphiphilic helix dataset containing 70 membrane proteins with a total of 18,458 amino acid residues. We extracted three commonly used protein features and predicted the membrane proteins amphiphilic helix structure using graph convolutional neural network. We improved the prediction accuracy of membrane proteins amphiphilic helix structure with the newly constructed dataset by rigorous 10-fold cross-validation.

引用

页码：394 / 404

页数：11

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