Characterization of Polypeptides by Tandem Mass Spectrometry Using Complementary Fragmentation Techniques

In the growing field of proteomics identification of proteins by tandem mass spectrometry (MS/MS) is performed by matching experimental mass spectra against calculated spectra of all possible peptides in a protein database. One problem with this approach is the false-positive identifications. MS-based proteomics experiments are further affected by a rather poor efficiency typical in the range of 10-15%, implicating that only a low percentage of acquired mass spectrometric data is significantly identified and assigned a peptide sequence. In this thesis improvement in spectrum specificity is accomplished by using a combination of high-accuracy mass spectrometry and techniques that will yield complementary sequence information. Performing collision-activated dissociation (CAD) and electron capture dissociation (ECD) upon the same peptide ion will yield such complementary sequence information. Implementing this into a proteomics approach and showing the advantages of using complementary fragmentation techniques for improving peptide identification is shown. Furthermore, a novel database-independent score is introduced (S-score) based upon the maximum length of the peptide sequence tag derived from complementary use of CAD and ECD. The S-score can be used to separate poor quality spectra from good quality spectra. An-other aspect of the S-score is the development of the ‘reliable sequence tag’ which can be used to recover below threshold identifications and for a reliable backbone for de novo sequencing of peptides. A novel proteomics-grade de novo sequencing algorithm has also been developed based upon the RST, which can retrieve peptide identification with the highest reliability (>95%). Furthermore, a novel software tool for unbiased identifications of any post-translational modifications present in a peptide sample is introduced (ModifiComb). Combining all the tools described in this thesis increases the identification specificity (>30 times), recovers false-negative identifications and increases the overall efficiency of proteomics experiements to above 40%. Currently one of the highest achieved in large-scale proteomics