iDRNA-ITF: identifying DNA- and RNA-binding residues in proteins based on induction and transfer framework

Ning Wang, Ke Yan, Jun Zhang, Bin Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Protein-DNA and protein-RNA interactions are involved in many biological activities. In the post-genome era, accurate identification of DNA- and RNA-binding residues in protein sequences is of great significance for studying protein functions and promoting new drug design and development. Therefore, some sequence-based computational methods have been proposed for identifying DNA- and RNA-binding residues. However, they failed to fully utilize the functional properties of residues, leading to limited prediction performance. In this paper, a sequence-based method iDRNA-ITF was proposed to incorporate the functional properties in residue representation by using an induction and transfer framework. The properties of nucleic acid-binding residues were induced by the nucleic acid-binding residue feature extraction network, and then transferred into the feature integration modules of the DNA-binding residue prediction network and the RNA-binding residue prediction network for the final prediction. Experimental results on four test sets demonstrate that iDRNA-ITF achieves the state-of-the-art performance, outperforming the other existing sequence-based methods. The webserver of iDRNA-ITF is freely available at http://bliulab.net/iDRNA-ITF.

Original languageEnglish
Article numberbbac236
JournalBriefings in Bioinformatics
Volume23
Issue number4
DOIs
Publication statusPublished - 1 Jul 2022

Keywords

  • DNA- and RNA-binding residue identification
  • convolutional attention neural network
  • induction and transfer framework
  • nucleic acid-binding residue identification

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