Paper Info
Title
Improving protein-RNA interface prediction by combining sequence homology based method with a naive Bayes classifier: preliminary results
Abstract
Protein-RNA interactions play important roles in cellular processes like protein synthesis, RNA processing, and gene expression regulation. Reliable identification of the interfaces involved in RNA-protein interactions is essential for comprehending the mechanisms and the functional implications of these interactions and provides a valuable guide for rational drug discovery and design. Because the determination of 3D structures of protein-RNA complexes has various technical limitations and is typically costly, reliable in silico interface prediction methods that require only the sequence information are urgently needed. We present HomPRIP, a homologous sequence based method for predicting protein-RNA interfaces, based on our conservation analysis of protein-RNA interfaces. We test Hom-PRIP on a benchmark dataset of 199 proteins and compare it with the state-of-the-art protein-RNA interface prediction methods. Our results show that HomPRIP can reliably identify protein-RNA interface residues in 71% of test proteins with at least one putative sequence homolog passing the similarity thresholds of HomPRIP. Moreover, to facilitate predictions for proteins with no identified homologs, we develop HomPRIP-NB, a method combining the HomPRIP predictor and a Naive Bayes (NB) classifier trained using evolutionary information derived from alignments against the NCBI nr database. Our results suggest that HomPRIP-NB significantly outperforms the state-of-the-art machine learning methods for predicting protein-RNA interface residues.
Year
DOI
Venue
2011
10.1145/2147805.2147899
BCB
Keywords
Field
DocType
protein-rna complex,homprip predictor,reliable identification,evolutionary information,state-of-the-art protein-rna interface prediction,silico interface prediction method,sequence information,sequence homology,improving protein-rna interface prediction,putative sequence homolog,preliminary result,protein-rna interface,naive bayes classifier,protein-rna interface residue,protein synthesis,naive bayes,machine learning,gene expression regulation,rna processing,drug discovery
RNA,Drug discovery,Naive Bayes classifier,Computer science,Sequence Homolog,Sequence homology,Artificial intelligence,Classifier (linguistics),Machine learning,Evolutionary information,In silico
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
5
Name
Order
Citations
PageRank
Li C Xue1153.07
Rasna R. Walia2332.27
Yasser El-Manzalawy3445.01
Drena Dobbs442335.43
Vasant Honavar53353468.10