PMID- 36629262
OWN - NLM
STAT- MEDLINE
DCOM- 20230323
LR  - 20230918
IS  - 1362-4962 (Electronic)
IS  - 0305-1048 (Print)
IS  - 0305-1048 (Linking)
VI  - 51
IP  - 5
DP  - 2023 Mar 21
TI  - HybridRNAbind: prediction of RNA interacting residues across structure-annotated 
      and disorder-annotated proteins.
PG  - e25
LID - 10.1093/nar/gkac1253 [doi]
AB  - The sequence-based predictors of RNA-binding residues (RBRs) are trained on 
      either structure-annotated or disorder-annotated binding regions. A recent study 
      of predictors of protein-binding residues shows that they are plagued by high 
      levels of cross-predictions (protein binding residues are predicted as nucleic 
      acid binding) and that structure-trained predictors perform poorly for the 
      disorder-annotated regions and vice versa. Consequently, we analyze a 
      representative set of the structure and disorder trained predictors of RBRs to 
      comprehensively assess quality of their predictions. Our empirical analysis that 
      relies on a new and low-similarity benchmark dataset reveals that the 
      structure-trained predictors of RBRs perform well for the structure-annotated 
      proteins while the disorder-trained predictors provide accurate results for the 
      disorder-annotated proteins. However, these methods work only modestly well on 
      the opposite types of annotations, motivating the need for new solutions. Using 
      an empirical approach, we design HybridRNAbind meta-model that generates accurate 
      predictions and low amounts of cross-predictions when tested on data that 
      combines structure and disorder-annotated RBRs. We release this meta-model as a 
      convenient webserver which is available at 
      https://www.csuligroup.com/hybridRNAbind/.
CI  - (c) The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic 
      Acids Research.
FAU - Zhang, Fuhao
AU  - Zhang F
AD  - Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and 
      Engineering, Central South University, Changsha 410083, China.
FAU - Li, Min
AU  - Li M
AUID- ORCID: 0000-0002-0188-1394
AD  - Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and 
      Engineering, Central South University, Changsha 410083, China.
FAU - Zhang, Jian
AU  - Zhang J
AD  - School of Computer and Information Technology, Xinyang Normal University, 
      Xinyang 464000, China.
FAU - Kurgan, Lukasz
AU  - Kurgan L
AUID- ORCID: 0000-0002-7749-0314
AD  - Department of Computer Science, Virginia Commonwealth University, Richmond, 
      VA 23284, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Nucleic Acids Res
JT  - Nucleic acids research
JID - 0411011
RN  - 0 (Proteins)
RN  - 63231-63-0 (RNA)
RN  - 0 (RNA-Binding Proteins)
SB  - IM
MH  - Computational Biology/methods
MH  - Databases, Protein
MH  - Protein Binding/genetics
MH  - *Proteins/chemistry
MH  - *RNA/chemistry
MH  - *RNA-Binding Proteins/chemistry
PMC - PMC10018345
EDAT- 2023/01/12 06:00
MHDA- 2023/03/21 06:00
PMCR- 2023/01/11
CRDT- 2023/01/11 07:33
PHST- 2022/12/15 00:00 [accepted]
PHST- 2022/11/22 00:00 [revised]
PHST- 2022/07/14 00:00 [received]
PHST- 2023/01/12 06:00 [pubmed]
PHST- 2023/03/21 06:00 [medline]
PHST- 2023/01/11 07:33 [entrez]
PHST- 2023/01/11 00:00 [pmc-release]
AID - 6984587 [pii]
AID - gkac1253 [pii]
AID - 10.1093/nar/gkac1253 [doi]
PST - ppublish
SO  - Nucleic Acids Res. 2023 Mar 21;51(5):e25. doi: 10.1093/nar/gkac1253.