Correspondence should be addressed to:
Charles University in Prague, Faculty of Mathematics and Physics, Malostranske nam. 25, 118 00 Prague 1, Czech Republic
The similarity search in theoretical mass spectra generated from protein sequence databases is a widely accepted approach for identification of peptides from query mass spectra produced by shotgun proteomics. Growing protein sequence databases and noisy query spectra demand database indexing techniques and better similarity measures for the comparison of theoretical spectra against query spectra. We employ a modification of previously proposed parameterized Hausdorff distance for comparisons of mass spectra. The new distance outperforms the original distance, the angle distance and state-of-the-art peptide identification tools OMSSA and X!Tandem in the number of identified peptides even though the q-value is only 0.001. When a precursor mass filter is used as a database indexing technique, our method outperforms OMSSA in the speed of search. When variable modifications are not searched, the search time is similar to X!Tandem. We show that the precursor mass filter is an efficient database indexing technique for high-accuracy data even though many variable modifications are being searched. We demonstrate that the number of identified peptides is bigger when variable modifications are searched separately by more search runs of a peptide identification engine. Otherwise, the false discovery rates are affected by mixing unmodified and modified spectra together resulting in a lower number of identified peptides. Our method is implemented in the freely available application SimTandem which can be used in the framework TOPP based on OpenMS.