A comparative analysis of machine learning classifiers for predicting protein-binding nucleotides in RNA sequences.

ABSTRACT: RNA-protein interactions play vital roles in driving the cellular machineries. Despite significant involvement in several biological processes, the underlying molecular mechanism of RNA-protein interactions is still elusive. This may be due to the experimental difficulties in solving co-crystallized RNA-protein complexes. Inherent flexibility of RNA molecules to adopt different conformations makes them functionally diverse. Their interactions with protein have implications in RNA disease biology. Thus, study of binding interfaces can provide a mechanistic insight of the molecular functioning and aberrations caused due to altered interactions. Moreover, high-throughput sequencing technologies have generated huge sequence data compared to available structural data of RNA-protein complexes. I

SUBMITTER: Agarwal A 

PROVIDER: S-EPMC9249596 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature