Identifying tandem mass spectra of phosphorylated peptides before database search using machine-learning

Identification of post-translational modifications (PTMs), for example phosphorylation, is of high interest in proteomics research since modified proteins are often important for biological functionality. For the identification of modified peptides during tandem mass spectrometry, database search engines typically consider the selected PTMs for any of the spectra in a sample. Selecting many different PTMs together results in drastically increased search space, leading to longer search times and more false positive peptide identifications. To counteract this, we propose the use of a machine-learning-trained model that can reliably classify those spectra which are highly likely to represent phosphorylated peptides before database search. By limiting the PTM search to only these spectra processing times can be improved.