Spectral clustering in peptidomics studies allows homology searching and modification profiling: HomClus, a versatile tool.

Many genomes of nonmodel organisms are yet to be annotated. Peptidomics research on those organisms therefore cannot adopt the commonly used database-driven identification strategy, leaving the more difficult de novo sequencing approach as the only alternative. The reported tool uses the growing resources of publicly or in-house available fragmentation spectra and sequences of (model) organisms to elucidate the identity of peptides of experimental spectra of nonannotated species. Clustering algorithms are implemented to infer the identity of unknown peak lists based on their publicly or in-house available counterparts. The reported tool, which we call the HomClus-tool, can cope with post-translational modifications and amino acid substitutions. We applied this tool on two locusts (Schistocerca gregaria and Locusta migratoria) LC-MALDI-TOF/TOF datasets. Compared to a Mascot database search (using the available UniProt-KB proteins of these species), we were able to double the amount of peptide identifications for both spectral sets. Known bioactive peptides from Drosophila melanogaster (i.e., fragmentations spectra generated in silico thereof) were used as a starting point for clustering, trying to reveal their experimental homologues' counterparts.