Protein Identification by Spectral Networks Analysis

While advances in tandem mass spectrometry (MS/MS) steadily increase the rate of generation of MS/MS spectra, standard algorithmic approaches for peptide identification recently seemed to be reaching the limit on the amount of information that could be extracted from MS/MS spectra. However, a closer look reveals that a common limiting procedure is to analyze each spectrum in isolation, even though high throughput mass spectrometry regularly generates many spectra from related peptides. By capitalizing on this redundancy we show that, similarly to the alignment of protein sequences, unidentified MS/MS spectra can also be aligned for the identification of modified and unmodified variants of the same peptide. Moreover, this alignment procedure can be iterated for the accurate grouping of multiple modification variants of the same peptides. Furthermore, the combination of shotgun proteomics with the alignment of spectra from overlapping peptides led to the development of Shotgun Protein Sequencing – similarly to the assembly of DNA reads into whole genomic sequences, we show that assembly of MS/MS spectra enables the highest ever de novo sequencing accuracy, while recovering nearly complete protein sequences. We further show that shotgun protein sequencing has the potential to overcome the limitations of �current protein sequencing approaches and thus catalyze the otherwise impractical applications of proteomics methodologies in studies of unknown proteins.