Peptide identification by database search of mixture tandem mass spectra

In high-throughput proteomics the development of com-putational methods and novel experimental strategies of-ten rely on each other. In certain areas, mass spectrom-etry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-elut-ing peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95 % accuracy from mixture spectra while considering only a 0.01 % of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and preci-sion at identifying single-peptide spectra while simultane-ously being able to identify 38 % more peptides from mix-ture spectra at significantly higher precision. Molecula