We are curious about two aspects of proteins in cells: how do they look and where do they go?
Structural biology has made amazing advances on providing us models of proteins alone or in complexes by technologies such as crystallography, electron microscopy or nuclear magnetic resonance spectroscopy. These methods continue to advance yet fail mostly on requiring proteins to be extracted from their native environments, a process that many proteins do not honour with keeping their native structure. We are one of the pioneers of developing cross-linking/mass spectrometry as an alternative approach. This sees proteins cross-linked in their native environment and the sites of cross-linking then being determined by mass spectrometry and data analysis. We are currently optimising many steps of this process: choice of cross-link reagents, protein-to-reagent ratio, digestion conditions, sample preparation, mass spectrometric acquisition, peak picking, data base construction. We are also rewriting our search software. While being occupied with housekeeping and learning more about the nature of our data to in future better conclude on observations we fully embrace ideas of open sharing our insights and tools by making use of preprint repositories, providing our code via GitHub, submitting our data to public repositories and establishing field standards. I hope these preparations will form the basis for a number of exciting biological discoveries this year.
Spatial proteomics has defined the protein organisation of the cell with the paradigm in mind that location determines function. Proteomic studies find many proteins in unexpected cellular locations. We encountered this during our analyses of mitotic and interphase chromatin. Can functional components of organelles be distinguished from biochemical artefacts or misguided cellular sorting? The clue might reside in compositional changes that follow biological challenges and that can be decoded by machine learning. (Kustatscher & Rappsilber, 2016). We combined proteomics data with machine learning to link proteins functionally by co-behavior in their localisation or expression when cells are responding to biological stimuli. This starts providing insights on how cellular processes transgress the boundaries provided by organelles.
Yuan, Z., Riera, A., Bai, L., Sun, J., Nandi, S., Spanos, C., Chen, Z.A., Barbon, M., Rappsilber*, J., Stillman*, B., Speck*, C., Li*, H. (2017). Structural basis of Mcm2-7 replicative helicase loading by ORC-Cdc6 and Cdt1. Nat. Struct. Mol. Biol. [Epub ahead of print]
Chen, Z., Fischer, L., Tahir, S., Bukowski-Wills, J.-C., Barlow, P., and Rappsilber, J. (2016). Quantitative cross-linking/mass spectrometry reveals subtle protein conformational changes. Wellcome Open Res 1, 5.
Kustatscher, G., and Rappsilber, J. (2016). Compositional Dynamics: Defining the Fuzzy Cell. Trends Cell Biol. 26, 800–803.