Send to

Choose Destination
Nucleic Acids Res. 2019 May 21;47(9):4406-4417. doi: 10.1093/nar/gkz203.

TriPepSVM: de novo prediction of RNA-binding proteins based on short amino acid motifs.

Author information

Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.
IRI Life Sciences, Humboldt University Berlin, Philippstrasse 13, 10115 Berlin, Germany.
Free University of Berlin, Takustrasse 9, 14195 Berlin, Germany.
Institute of Computational Biology (ICB), Helmholtz Zentrum Munich, Ingolstaedter Landstr. 1 85764 Neuherberg, Germany.


In recent years, hundreds of novel RNA-binding proteins (RBPs) have been identified, leading to the discovery of novel RNA-binding domains. 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 in bacteria. Here, we describe a support vector machine-based method, called TriPepSVM, for the prediction of RNA-binding proteins. 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 tri-peptides 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.

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center