Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks

DNA-binding proteins (DBP) play an essential role in the genetics and evolution of organisms. A particular DNA sequence could provide underlying therapeutic benefits for hereditary diseases and cancers. Studying these proteins can timely and effectively understand their mechanistic analysis and play...

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Bibliographic Details
Published in:Frontiers in pharmacology 2022-10, Vol.13, p.1031759-1031759
Main Authors: Yu, Shaoyou, Peng, Dejun, Zhu, Wen, Liao, Bo, Wang, Peng, Yang, Dongxuan, Wu, Fangxiang
Format: Article
Language:English
Subjects:
CC-PSSM
convolutional neural network
DNA-binding proteins
kmer
monoDiKGap
MRMD2.0
Pharmacology
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Summary:DNA-binding proteins (DBP) play an essential role in the genetics and evolution of organisms. A particular DNA sequence could provide underlying therapeutic benefits for hereditary diseases and cancers. Studying these proteins can timely and effectively understand their mechanistic analysis and play a particular function in disease prevention and treatment. The limitation of identifying DNA-binding protein members from the sequence database is time-consuming, costly, and ineffective. Therefore, efficient methods for improving DBP classification are crucial to disease research. In this paper, we developed a novel predictor Hybrid _DBP, which identified potential DBP by using hybrid features and convolutional neural networks. The method combines two feature selection methods, MonoDiKGap and Kmer, and then used MRMD2.0 to remove redundant features. According to the results, 94% of DBP were correctly recognized, and the accuracy of the independent test set reached 91.2%. This means Hybrid_ DBP can become a useful prediction tool for predicting DBP.
ISSN:1663-9812
1663-9812
DOI:10.3389/fphar.2022.1031759