Prediction of DNA-binding residues from sequence information using convolutional neural network

Jiyun Zhou, Qin Lu, Ruifeng Xu, Lin Gui, Hongpeng Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Most DNA-binding residue prediction methods overlooked the motif features which are important for the recognition between protein and DNA. In order to efficiently use the motif features for prediction, we first propose to use Convolutional Neural Network (CNN) in deep learning to extract discriminant motif features. We then propose a neural network classifier, referred to as CNNsite, by combining the extracted motif features, sequence features and evolutionary features. The evaluation on PDNA-62, PDNA-224 and TR-265 shows that motif features perform better than sequence features and evolutionary features. The evaluation on PDNA-62, PDNA-224 and an independent data set shows that CNNsite also outperforms the previous methods. We also show that many motif features composed by the residues which play important roles in DNA-protein interactions have large discriminant powers. It indicates that CNNsite has very good ability to extract important motif features for DNA-binding residue prediction.
Original languageEnglish
Pages (from-to)132-152
Number of pages21
JournalInternational Journal of Data Mining and Bioinformatics
Volume17
Issue number2
DOIs
Publication statusPublished - 1 Jan 2017

Keywords

  • Binding
  • CNN
  • DNA
  • Evolutionary
  • Interaction
  • Motif
  • Neural network
  • Protein
  • PSSM
  • Residue
  • Sequence

ASJC Scopus subject areas

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences

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