Name
Title | ||
|---|---|---|
iDRNA-ITF: identifying DNA- and RNA-binding residues in proteins based on induction and transfer framework |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/bib/bbac236 | BRIEFINGS IN BIOINFORMATICS |
Keywords | DocType | Volume |
induction and transfer framework, DNA- and RNA-binding residue identification, nucleic acid-binding residue identification, convolutional attention neural network | Journal | 23 |
Issue | ISSN | Citations |
4 | 1467-5463 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |