Paper Info
Title
NCBRPred: predicting nucleic acid binding residues in proteins based on multilabel learning
Abstract
The interactions between proteins and nucleic acid sequences play many important roles in gene expression and some cellular activities. Accurate prediction of the nucleic acid binding residues in proteins will facilitate the research of the protein functions, gene expression, drug design, etc. In this regard, several computational methods have been proposed to predict the nucleic acid binding residues in proteins. However, these methods cannot satisfactorily measure the global interactions among the residues along protein. Furthermore, these methods are suffering cross-prediction problem, new strategies should be explored to solve this problem. In this study, a new computational method called NCBRPred was proposed to predict the nucleic acid binding residues based on the multilabel sequence labeling model. NCBRPred used the bidirectional Gated Recurrent Units (BiGRUs) to capture the global interactions among the residues, and treats this task as a multilabel learning task. Experimental results on three widely used benchmark datasets and an independent dataset showed that NCBRPred achieved higher predictive results with lower cross-prediction, outperforming 10 existing state-of-the-art predictors. The web-server and a stand-alone package of NCBRPred are freely available at http://bliulab.net/NCBRPred. It is anticipated that NCBRPred will become a very useful tool for identifying nucleic acid binding residues.
Year
DOI
Venue
2021
10.1093/bib/bbaa397
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
nucleic acid binding residue prediction, cross-prediction problem, multilabel learning, sequence labeling model
Journal
22
Issue
ISSN
Citations 
5
1467-5463
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jun Zhang140854.35
Qingcai Chen280966.72
Bin Liu341933.30