Bioinformatic Approach for Prediction of CsrA/RsmA-Regulating Small RNAs in Bacteria

Abstract

CsrA/RsmA is a RNA-binding protein that functions as a global regulator controlling important processes such as virulence, secondary metabolism, motility, and biofilm formation in diverse bacterial species. The activity of CsrA/RsmA is regulated by small RNAs that contain multiple binding sites for the protein. The expression of these noncoding RNAs effectively sequesters the protein and reduces free cellular levels of CsrA/RsmA. While multiple bacterial small RNAs that bind to and regulate CsrA/RsmA levels have been discovered, it is anticipated that there are several such small RNAs that remain undiscovered. To assist in the discovery of these small RNAs, we have developed a bioinformatics approach that combines sequence- and structure-based features to predict small RNA regulators of CsrA/RsmA. This approach analyzes structural motifs in the ensemble of low energy secondary structures of known small RNA regulators of CsrA/RsmA and trains a binary classifier on these features. The proposed machine learning approach leads to several testable predictions for small RNA regulators of CsrA/RsmA, thereby complementing and accelerating experimental efforts aimed at discovery of noncoding RNAs in the CsrA/RsmA pathway.