TY - JOUR
T1 - iDRNA-ITF
T2 - identifying DNA- and RNA-binding residues in proteins based on induction and transfer framework
AU - Wang, Ning
AU - Yan, Ke
AU - Zhang, Jun
AU - Liu, Bin
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
KW - DNA- and RNA-binding residue identification
KW - convolutional attention neural network
KW - induction and transfer framework
KW - nucleic acid-binding residue identification
UR - http://www.scopus.com/inward/record.url?scp=85134636342&partnerID=8YFLogxK
U2 - 10.1093/bib/bbac236
DO - 10.1093/bib/bbac236
M3 - Article
C2 - 35709747
AN - SCOPUS:85134636342
SN - 1467-5463
VL - 23
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 4
M1 - bbac236
ER -