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The combination of collision-activated dissociation, (CAD) and electron capture dissociation, (ECD) yielded a 125% increase in protein identification. The S-score was developed for measuring the information content in MS/MS spectra. This measure made it possible to single out good quality spectra that were not identified by a search engine. Poor quality MS/MS data was filtered out, streamlining the identification process.

A proteomics grade de novo sequencing approach was developed enabling to almost completely sequence 19% of all MS/MS data with 95% reliability in a typical proteomics experiment.

A new tool, Modificomb, for identifying all types of modifications in a fast, reliable way was developed. New types of modifications have been discovered and the extent of modifications in gel based proteomics turned out to be greater than expected.

PhosTShunter was developed for sensitive identification of all phosphorylated peptides in an MS/MS dataset.

Application of these programs to human milk samples led to identification of a previously unreported and potentially biologically important phosphorylation site.

Peptide fragmentation has been studied. It was shown emphatically on a dataset of 15.000 MS/MS spectra that CAD and ECD have different cleavage preferences with respect to the amino acid context.

Hydrogen rearrangement involving z• species has been investigated. Clear trends have been unveiled. This information elucidated the mechanism of hydrogen transfer.

Partial side-chain losses in ECD have been studied. The potential of these ions for reliably distinguishing Leu/Iso residues was shown. Partial sidechain losses occurring far away from the cleavage site have been detected.

A strong correlation was found between the propensities of amino acids towards peptide bond cleavage employing CAD and the propensity of amino acids to accept in solution backbone-backbone H-bonds and form stable motifs. This indicated that the same parameter governs formation of secondary structures in solution and directs fragmentation in peptide ions by CAD.

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.