Mass spectrometry of crosslinked peptides
type: CreativeWork
description: Proteins are central functional components of life, yet studying their structure and interactions remains a major challenge. The work I present in this thesis aims at addressing this challenge, advancing the identification of crosslinked peptides by analysing their mass spectrometric fragmentation behaviour and leveraging the knowledge gained for the development of advanced acquisition methods. First, I investigate the influence of the employed fragmentation method and parameters on peptides crosslinked with the most commonly used crosslinkers BS3 (bissulfosuccinimidyl suberate) and DSS (disuccinimidyl suberate). We show that the choice of fragmentation parameters greatly impacts the number of reliably identifiable crosslinks. The most significant factors impacting the crosslink identifications are fragment sequence coverage and acquisition speed. We developed a data-dependent decision tree acquisition method, choosing the best-performing parameters depending on analyte properties. We further established higher-energy C-trap dissociation (HCD) as the best overall single method for the analysis of BS3 / DSS crosslinked peptides. Second, I present a web-based tool that enables the visualisation, sharing, and analysis of mass spectrometric fragment spectra. While the spectra identification process is nowadays dominated by database matching algorithms, visualisation for manual verification, hypothesis testing and (re-)analysis of identified spectra remain important. Our community-standard-compliant tool xiSPEC renders high-quality vector graphics of annotated spectra in the browser, allowing the readily testing of hypotheses and sharing of proteomics data. It is available as a stand-alone online service and has also been successfully integrated into other tools. Third, I studied the fragmentation behaviour of linear peptides using ultraviolet photodissociation (UVPD), fragmenting analytes using a 213 nm UV laser. We analysed a wide variety of peptides using a specifical…
abstract: Proteins are central functional components of life, yet studying their structure and interactions remains a major challenge. The work I present in this thesis aims at addressing this challenge, advancing the identification of crosslinked peptides by analysing their mass spectrometric fragmentation behaviour and leveraging the knowledge gained for the development of advanced acquisition methods. First, I investigate the influence of the employed fragmentation method and parameters on peptides crosslinked with the most commonly used crosslinkers BS3 (bissulfosuccinimidyl suberate) and DSS (disuccinimidyl suberate). We show that the choice of fragmentation parameters greatly impacts the number of reliably identifiable crosslinks. The most significant factors impacting the crosslink identifications are fragment sequence coverage and acquisition speed. We developed a data-dependent decision tree acquisition method, choosing the best-performing parameters depending on analyte properties. We further established higher-energy C-trap dissociation (HCD) as the best overall single method for the analysis of BS3 / DSS crosslinked peptides. Second, I present a web-based tool that enables the visualisation, sharing, and analysis of mass spectrometric fragment spectra. While the spectra identification process is nowadays dominated by database matching algorithms, visualisation for manual verification, hypothesis testing and (re-)analysis of identified spectra remain important. Our community-standard-compliant tool xiSPEC renders high-quality vector graphics of annotated spectra in the browser, allowing the readily testing of hypotheses and sharing of proteomics data. It is available as a stand-alone online service and has also been successfully integrated into other tools. Third, I studied the fragmentation behaviour of linear peptides using ultraviolet photodissociation (UVPD), fragmenting analytes using a 213 nm UV laser. We analysed a wide variety of peptides using a specifical…
author: Kolbowski, Lars Bernhard
language: en
license: https://creativecommons.org/licenses/by/4.0/
date published: 2023
date modified: 2026-07-27T21:28:57Z
provider: Technische Universität Berlin
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Technische Universität Berlin

PropertyValue
property ID: DOI
value: 10.14279/depositonce-18283