TY - JOUR T1 - TriPepSVM - <em>de novo</em> prediction of RNA-binding proteins based on short amino acid motifs JF - bioRxiv DO - 10.1101/466151 SP - 466151 AU - Annkatrin Bressin AU - Roman Schulte-Sasse AU - Davide Figini AU - Erika C Urdaneta AU - Benedikt M Beckmann AU - Annalisa Marsico Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/11/08/466151.abstract N2 - In recent years hundreds of novel RNA-binding proteins (RBPs) have been identified leading to the discovery of novel RNA-binding domains (RBDs). Furthermore, unstructured or disordered low-complexity regions of RBPs have been identified to play an important role in interactions with nucleic acids. However, these advances in understanding RBPs are limited mainly to eukaryotic species and we only have limited tools to faithfully predict RNA-binders from bacteria. Here, we describe a support vector machine (SVM)-based method, called TriPepSVM, for the classification of RNA-binding proteins and non-RBPs. TriPepSVM applies string kernels to directly handle protein sequences using tri-peptide frequencies. Testing the method in human and bacteria, we find that several RBP-enriched tripeptides occur more often in structurally disordered regions of RBPs. TriPepSVM outperforms existing applications, which consider classical structural features of RNA-binding or homology, in the task of RBP prediction in both human and bacteria. Finally, we predict 66 novel RBPs in Salmonella Typhimurium and validate the bacterial proteins ClpX, DnaJ and UbiG to associate with RNA in vivo. ER -