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Characterization of Polypeptides by Tandem Mass Spectrometry Using Complementary Fragmentation Techniques

<p>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.</p><p>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.</p><p>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.</p>

Identiferoai:union.ndltd.org:UPSALLA/oai:DiVA.org:uu-7409
Date January 2006
CreatorsNielsen, Michael Lund
PublisherUppsala University, Department of Engineering Sciences, Uppsala : Acta Universitatis Upsaliensis
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeDoctoral thesis, comprehensive summary, text
RelationDigital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, 1651-6214 ; 252

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