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Protein interactions enable much more complex behavior than the sum of the individual protein parts would suggest and represents a level of biological complexity requiring full understanding when unravelling cellular processes. Cross-linking mass spectrometry has emerged as an attractive approach to study these interactions, and recent advances in mass spectrometry and data analysis software have enabled the identification of thousands of cross-links from a single experiment. The resulting data complexity is, however, difficult to understand and requires interactive software tools. Even though solutions are available, these represent an agglomerate of possibilities, and each features its own input format, often forcing manual conversion. Here we present Cross-ID, a visualization platform that links directly into the output of XlinkX for Proteome Discoverer but also plays well with other platforms by supporting a user-controllable text-file importer. The platform includes features like grouping, spectral viewer, gene ontology (GO) enrichment, post-translational modification (PTM) visualization, domains and secondary structure mapping, data set comparison, previsualization overlap check, and more. Validation of detected cross-links is available for proteins and complexes with known structure or for protein complexes through the DisVis online platform ( http://milou.science.uu.nl/cgi/services/DISVIS/disvis/ ). Graphs are exportable in PDF format, and data sets can be exported in tab-separated text files for evaluation through other software.

The focus of this thesis is Crosslinking Mass Spectrometry (XL-MS) — a promising approach which received a lot of traction in recent years. This method allows capturing protein sites located in close proximity without the disturbing of protein native conformation. Here I describe our recent developments in the methodology together with XL-MS application to the complex biological systems. Chapter 1 contains a general overview of the Biomolecular MS and Proteomics over the years and covers its establishing as a robust and efficient method in protein characterization. With the focus on bottom-up proteomics, the chapter introduces instrumentation and main LC-MS/MS workflows which are routinely applied these days. Optimization of the protocol for crosslinking of proteins, proteins complexes, and system-wide crosslinking experiments is available in Chapter 2 and Appendix to this thesis. The approach is exemplified on two model systems: BSA protein as a low-complexity sample and whole PC9 cell lysate as a complex sample. In the case of BSA, the application of a set of proteases for protein digestion is described as a step towards more identified crosslinks. For the whole cell lysate experiment, the chapter reports the detection of thousands of unique crosslinked peptide pairs within a single experiment. Additionally, the detailed description of the crosslinking data analysis steps which are done with XlinkX node incorporated into Proteome Discoverer software suite together with the When applied to the complex biological systems, crosslinking outputs become not trivial in terms of data analysis and visualization. Chapter 3 describes the development and application of Cross-ID, the platform for analysis and visualization of complex XL-MS outputs. It describes the analysis of the proteome-wide crosslinking output generated in Chapter 2 for PC9 cells. Different visualization approaches, validation, and integration into DisVis pipeline for restraints-based interaction interface prediction are highlighted. After we have established and optimized the necessary steps in the crosslinking pipeline, we have started to investigate biological samples of interest. In Chapter 4 we describe the molecular architecture of the type I-F CRISPR adaptation complex comprised of Cas2-32 and Cas14. Integrative structural biology approach allows to correctly place the complex subunits and decipher interaction interface of the protein complex. First, complex mass and its stoichiometry are defined with SDS-PAGE, SEC and Native MS. Then by XL-MS with DSSO reagent, protein sites located close to each other are pinpointed. On the next step, XL-MS with formaldehyde reagent highlights the DNA-protein interaction sites and the final structure of the Cas2-34:Cas14-DNA complex is solved. Chapter 5 focuses on the XL-MS guided modeling and docking of the subunits of fibrin biological biopolymers. The high number of identified distance restraints within this extremely complex sample provides the basis for the modeling of the structures of the heparin-binding domain of β-fibrinogen, interactive and RGD-containing domains of α-fibrinogen as well as the α220-249 region. This chapter for the first time describes the model of the laterally aggregated Fibrin clot generated with the experimental data.

The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein-protein interactions has increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats. Such consensus formats should become accepted in the field and be shown to lead to reproducible results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. Our study therefore builds the framework for establishing best practice guidelines for conducting cross-linking experiments, performing data analysis, and reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results.

Protein interactions enable much more complex behavior than the sum of the individual protein parts would suggest and represents a level of biological complexity requiring full understanding when unravelling cellular processes. Crosslinking mass spectrometry has emerged as an attractive approach to study these interactions and recent advances in mass spectrometry and data analysis software have enabled the identification of thousands of crosslinks from a single experiment. The resulting data complexity is however difficult to understand and requires interactive software tools. Even though solutions are available, these represent an agglomerate of possibilities and each features its own input format often forcing manual conversion. Here we present Cross-ID, a visualization platform that links directly into the output of XlinkX for Proteome Discoverer, but also plays well with other platforms by supporting a user-controllable text-file importer. The platform includes features like grouping, spectral viewer, GO enrichment, PTM-visualization, domain- and secondary structure mapping, dataset comparison, pre-visualization overlap-check and more. Validation of detected crosslinks is available for proteins and complexes with known structure or for protein complexes through the DisVis online platform (http://milou.science.uu.nl/cgi/services/DISVIS/disvis/). Graphs are exportable in PDF format, and datasets can be exported in tab separated text files for evaluation through other software.

The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein protein interactions has increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats. Such consensus formats should become accepted in the field and be shown to lead to reproducible results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. Our study therefore builds the framework for establishing best practice guidelines to conduct cross-linking experiments, perform data analysis, and define reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results.

Summary: Tight regulation of neuronal transport allows for cargo binding and release at specific cellular locations. The mechanisms by which motor proteins are loaded on vesicles and how cargoes are captured at appropriate sites remain unclear. To better understand how KIF1A-driven dense core vesicle (DCV) transport is regulated, we identified the KIF1A interactome and focused on three binding partners, the calcium binding protein calmodulin (CaM) and two synaptic scaffolding proteins: liprin-α and TANC2. We showed that calcium, acting via CaM, enhances KIF1A binding to DCVs and increases vesicle motility. In contrast, liprin-α and TANC2 are not part of the KIF1A-cargo complex but capture DCVs at dendritic spines. Furthermore, we found that specific TANC2 mutations—reported in patients with different neuropsychiatric disorders—abolish the interaction with KIF1A. We propose a model in which Ca2+/CaM regulates cargo binding and liprin-α and TANC2 recruit KIF1A-transported vesicles. : Stucchi et al. show that KIF1A-dependent transport is regulated by CaM, liprin-α, and TANC2. KIF1A binding to DCVs is controlled by a Ca2+/CaM molecular mechanism, and KIF1A-driven DCVs are recruited in dendritic spines by the PSD scaffolds liprin-α and TANC2. Keywords: neuron, transport, dendritic spines, synapse, KIF1A, scaffold, calmodulin, calcium, TANC2, liprin-α