title: FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues.
abstract: A 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.
authors.affiliation: College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, United States of America.|Systems and Computer Engineering, Al-Azhar University, Cairo, Egypt.
authors.forename: Yasser
authors.initials: Y
authors.lastname: El-Manzalawy
authors.affiliation: KINDI Center for Computing Research, College of Engineering, Qatar University, Duha, Qatar.
authors.forename: Mostafa
authors.initials: M
authors.lastname: Abbas
authors.affiliation: KINDI Center for Computing Research, College of Engineering, Qatar University, Duha, Qatar.
authors.forename: Qutaibah
authors.initials: Q
authors.lastname: Malluhi
authors.affiliation: College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, United States of America.
authors.forename: Vasant
authors.initials: V
authors.lastname: Honavar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D003201:Computers; D030562:Databases, Protein; D008958:Models, Molecular; D056510:Position-Specific Scoring Matrices; D011237:Predictive Value of Tests; D011487:Protein Conformation; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012313:RNA / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins; D012313:RNA