Title:A Useful Tool for the Identification of DNA-Binding Proteins Using Graph Convolutional Network
VOLUME: 18 ISSUE: 5
Author(s):Dasheng Chen and Leyi Wei*
Affiliation:College of Intelligence and Computing, Tianjin University, Tianjin, School of Software, Shandong University, Jinan
Keywords:DNA-binding proteins, Graph Convolutional Network (GCN), protein sequence, sequence classification, deep
learning, k-mer spectrum.
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.
