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AuthorEL-Manzalawy, Yasser
AuthorAbbas, Mostafa
AuthorMalluhi, Qutaibah
AuthorHonavar, Vasant
Available date2016-10-18T10:07:41Z
Publication Date2016-07-06
Publication NamePLoS ONEen_US
Identifierhttp://dx.doi.org/10.1371/journal.pone.0158445
CitationEL-Manzalawy Y, Abbas M, Malluhi Q, Honavar V (2016) FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues. PLoS ONE 11(7): e0158445.
URIhttp://hdl.handle.net/10576/4900
AbstractA wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM)-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles). Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein-protein and protein-DNA interfaces.
SponsorEdward Frymoyer Endowed Professorship in Information Sciences and Technology. The Center for Big Data Analytics and Discovery Informatics which is co-sponsored by the Institute for Cyberscience, the Huck Institutes of the Life Sciences, the Social Science Research Institute, and the College of Information Sciences and Technology at the Pennsylvania State University. NPRP grant No. 4-1454-1-233 from the Qatar National Research Fund (a member of Qatar Foundation).
Languageen
PublisherPublic Library of Science (PLoS)
SubjectSequence database
SubjectBLAST algorithm
SubjectDatabase searching
SubjectAmino acid sequence analysis
Subjectdatabase and informatics methods
TitleFastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues
TypeArticle
Issue Number7
Volume Number11
ESSN1932-6203


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