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.
On going research aims at:
- Exploring more sophisticated approaches (e.g., based on clustering analysis of protein sequences) to determine the optimal BLAST database for a given classification task
- Applying the proposed methodology to develop reliable yet computationally efficient methods for related amino acid sequence labeling (e.g., protein-DNA interface residue prediction) and sequence classification (e.g., identifying RNA-binding proteins)
- Exploring whether there is a single optimal BLAST database that can be used across multiple tasks (e.g., protein-RNA, protein-DNA, and protein-protein interface prediction) or whether the optimal BLAST database is task-dependent
- Developing parallel or distributed implementations and/or advanced data structures to further reduce the run time and memory usage of the methods in order to support very high throughput analyses
Tools and software:
Online webserver: FastRNABindR