Abstract
An automated top-down approach including data-dependent MS(3) experiment for protein identification/characterization is described. A mixture of wild-type yeast proteins has been separated on-line using reverse-phase liquid chromatography and introduced into a hybrid linear ion trap (LTQ) Fourier transform ion cylclotron resonance (FTICR) mass spectrometer, where the most abundant molecular ions were automatically isolated and fragmented. The MS(2) spectra were interpreted by an automated algorithm and the resulting fragment mass values were uploaded to the ProSight PTM search engine to identify three yeast proteins, two of which were found to be modified. Subsequent MS(3) analyses pinpointed the location of these modifications. In addition, data-dependent MS(3) experiments were performed on standard proteins and wild-type yeast proteins using the stand alone linear trap mass spectrometer. Initially, the most abundant molecular ions underwent collisionally activated dissociation, followed by data-dependent dissociation of only those MS(2) fragment ions for which a charge state could be automatically determined. The resulting spectra were processed to identify amino acid sequence tags in a robust fashion. New hybrid search modes utilized the MS(3) sequence tag and the absolute mass values of the MS(2) fragment ions to collectively provide unambiguous identification of the standard and wild-type yeast proteins from custom databases harboring a large number of post-translational modifications populated in a combinatorial fashion.