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Methods for detection and measurement of virulence factors of pathogenic bacteria

Title Methods for detection and measurement of virulence factors of pathogenic bacteria
Student George Rosenberger
Type MSc
Completion Date 2011-09-02
Abstract

Pathogenic bacteria like M. tuberculosis and S. pyogenes are responsible for many severe human diseases. The strategies of these bacteria to infect hosts are not yet completely resolved, but many host-pathogen interactions involve protein-protein recognition. The involved bacterial proteins are called virulence factors. Since many of them are secreted or located on the cell surface, their protein-protein interaction (PPI) interfaces are under heavy evolutionary pressure for host-specific adaptations.

To investigate and improve current methods for detection and measurement of viru- lence factors of pathogenic bacteria, two independent projects were defined as part of this work:

The goal of the first project was the development of a method to detect putative virulence factors in silico. The problem was approached by estimation of residue con- servation of orthologous protein sequences of the proteomes of human pathogenic and non-pathogenic species and strains of the genera Mycobacterium and Streptococcus. This revealed protein hotspots, which were projected to known and modeled protein structures. To filter only those hotspots required for host-specific adaptation, a method was devel- oped to detect clustering hotspots and thus putative PPI sites on the protein surface. The method was evaluated by a statistical analysis and literature research of known virulence factors.

The goal of the second, independent project was the development of a method to im- prove current approaches for absolute label-free quantification of proteins by mass spec- trometry experiments. Current methods employ an empirical method for selection of peptides to infer the protein quantity from. The problem was approached by correction for the peptide sequence-specific portion of the measured peptide intensity, the peptide response. Peptide response predictors were trained by machine learning regression algo- rithms on experimental data. Different models were created to integrate the predicted peptide responses and the measured peptide intensities of all peptides of a protein to es- timate the theoretical protein intensity. The prediction and estimation performance was evaluated using internal and external validation methods.

For both projects, the proof of concept was demonstrated and software packages were implemented to enable automated analysis workflows and rapid evaluation of the results.