An algorithm for identifying multiply modified endogenous proteins using both full-scan and high-resolution tandem mass spectrometric data

Mass spectrometry based proteomic experiments have advanced considerably over the past decade with high‐resolution and mass accuracy tandem mass spectrometry (MS/MS) capabilities now allowing routine interrogation of large peptides and proteins. Often a major bottleneck to 'top‐down' proteomics, however, is the ability to identify and characterize the complex peptides or proteins based on the acquired high‐resolution MS/MS spectra. For biological samples containing proteins with multiple unpredicted processing events, unsupervised identifications can be particularly challenging. Described here is a newly created search algorithm (MAR) designed for the identification of experimentally detected peptides or proteins. This algorithm relies only on predefined list of 'differential' modifications (e.g. phosphorylation) and a FASTA‐formatted protein database, and is not constrained to full‐length proteins for identification. The algorithm is further powered by the ability to leverage identified mass differences between chromatographically separated ions within full‐scan MS spectra to automatically generate a list of likely 'differential' modifications to be searched. The utility of the algorithm is demonstrated with the identification of 54 unique polypeptides from human apolipoprotein enriched from the high‐density lipoprotein particle (HDL), and searching time benchmarks demonstrate scalability (12 high‐resolution MS/MS scans searched per minute with modifications considered). This parallelizable algorithm provides an additional solution for converting high‐quality MS/MS data of multiply processed proteins into reliable identifications. Copyright © 2011 John Wiley & Sons, Ltd.