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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.