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Current Proteomics

Editor-in-Chief

ISSN (Print): 1570-1646
ISSN (Online): 1875-6247

Research Article

A Useful Tool for the Identification of DNA-Binding Proteins Using Graph Convolutional Network

Author(s): Dasheng Chen and Leyi Wei*

Volume 18, Issue 5, 2021

Published on: 10 December, 2020

Page: [661 - 668] Pages: 8

DOI: 10.2174/1570164618999201210225354

Price: $65

Abstract

Background: DNA and protein are important components of living organisms. DNA binding protein is a helicase, which is a protein specifically responsible for binding to DNA single- stranded regions. It is a necessary component for DNA replication, recombination and repair, and plays a key role in the function of various biomolecules. Although there are already some classification prediction methods for this protein, the use of graph neural networks for this work is still limited.

Objective: The classification of unknown protein sequences into the correct categories, subcategories and families is important for biological sciences. In this article, using graph neural networks, we developed a novel predictor GCN-DBP for protein classification prediction.

Methods: Each protein sequence is treated as a document in this study, and then segment the words according to the concept of k-mer, thereby, finally achieving the purpose of segmenting the document. This research aims to use document word relationships and word co-occurrence as a corpus to construct a text graph, and then learn protein sequence information by two-layer graph convolutional networks.

Results: Finally, we tested GCN-DBP on the independent data set PDB2272, and its accuracy reached 64.17% and MCC was 28.32%. Moreover, in order to compare the proposed method with other existing methods, we have conducted more experiments.

Conclusion: The results show that the proposed method is superior to the other four methods and will be a useful tool.

Keywords: DNA-binding proteins, Graph Convolutional Network (GCN), protein sequence, sequence classification, deep learning, k-mer spectrum.

Graphical Abstract

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