Date of Award

7-17-2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Bioinformatics

First Advisor

Boris Zybailov

Abstract

Chemical cross-linking mass spectrometry (CXMS) is a useful method for studying protein-protein interactions. Short length non-specific cross-linkers are able to capture many interactions between and within proteins. However, due to the complexity of mass spectra of the non-specific cross-linked peptides coupled with unavailability efficient data analysis tool non-specific cross-linkers are not widely used. In this dissertation I describe a new algorithm XLPM (X-linked peptide mapping) for the analysis of CXMS data. The XLPM algorithm has a novel b-y ion filter. The XLPM is validated on non-specific cross-linker data successfully. Furthermore, I have developed a new probabilistic scoring system for the protein mass spectrometry data analysis using database searching. The scoring system can be applied to large scale CXMS data analysis. The XLPM is implemented as a web site as well as a standalone Perl package. A visualization XLPM map viewer is also developed to visualize the XLPM results. In nutshell, the higher charges, abundance of non-cross-linked peptide over cross-linked peptides and different chemistries of cross-linking reagents make the CXMS data analysis difficult. The XLPM algorithm and the new scoring system open new frontiers for the CXMS data analysis for the overall application of the short length non-specific cross-linkers.

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