We recently published a blog in which Progenesis QI was being turned to new uses (in food standards); I’m happy to say that we can now say likewise for Progenesis QI for proteomics, this time in structural biology!
A 2014 publication in Nature Methods (Argyris Politis and Florian Stengel et al., ) described the development of a hybrid methodology for determining protein complex structures using MS-based approaches, with Progenesis providing label-free quantitative data that were essential to the structural modelling. We’re naturally thrilled for our software to have contributed to such a cutting-edge project, but first, I’ll go through a little bit of background and the work itself.
The accurate determination of the structure of protein assemblies can be very complex; established high-resolution methods include X-ray crystallography and nuclear magnetic resonance (NMR), but these both face particular challenges. Complexes may not crystallise effectively, intact, or in a biologically appropriate state for X-ray studies, for example; NMR analysis avoids the need for a crystal structure, but tends to require a relatively large amount and concentration of protein sample, and may require various isotopic labelling strategies and/or specialised methodology for large complexes. As such, there are many complexes for which these methods cannot be effectively applied. Lower-resolution methods such as cryo-electron microscopy (EM) and interactomics methods such as co-immunoprecipitation (co-IP) have their part to play, but there is a real need to improve the repertoire of methods available for structural elucidation of multi-unit complexes.
This is where hybrid MS-based analyses come in , allowing improvement in structural modelling of protein complexes, even transiently formed ones, with modest amounts of protein sample and tolerance of different sample conditions. The Nature Methods authors’ hybrid MS approaches comprise both top-down and bottom-up proteomics analyses; the bottom-up analyses firstly include label-free quantitation using Progenesis to determine the protein subunits present and their relative abundance. This provides a critical set of constraints, fed into all subsequent structural modelling. The label-free results are also coupled with cross-linking studies, to identify points of interaction between protein subunits at the ‘peptide-resolution’ level. Again, Progenesis is of use here by generating a peptide database library for use in identifying the linked peptides generated.
On the top-down side, native MS provides complex and sub-complex masses and stoichiometry, building up an interaction network by identifying hierarchies of subunit associations. Furthermore, ion-mobility MS is used to gain topological information on the complexes and sub-complexes; in a nice nod to our colleagues, the determination of CCS values using Waters’ ion mobility technology is also a critical piece of the puzzle.
The constraint data from these approaches are then coupled with high-resolution structural data for individual subunits (or homology models thereof) to build up a picture of the complex as a whole. In doing this, the particular challenges that high-resolution methods can face with large protein complexes can be mitigated, requiring only existing subunit-level information.
Initially, this hybrid approach was carried out on three varying ‘learning structures’. By optimising the relative weighting of the information provided by each method, and assessing the fit of the resulting model structures with the known data, the authors were able to refine their methodology and then bring it to bear on new complexes. In a particularly exciting demonstration, the structure of the proteasome lid was modelled, which previously was only available at EM level. The model was sufficiently accurate to make predictions about the location of a lid subunit missing from the EM structure that fit with published experimental data. Furthermore, through affinity pull-down work coupled with their hybrid MS approach, the authors were also able to propose realistic structures for proteasomal assembly intermediates, demonstrating the ability of the method to help elucidate the dynamic interactome that complexes are part of in reality.
I’d really recommend reading the paper, as we cannot do it justice here; the combination of approaches is both elegant and effective. The synergy between the methods provides enough structural information, and restraints to fit with it, that complex modelling becomes a realistic prospect.
From our point of view, it’s worth returning to the use of Progenesis in the bottom-up part of the method. We were lucky enough to talk to and get the opinion of Florian Stengel himself on our software; he told us that:
“Progenesis was an easy-to-use and indispensable tool to define the content and quantity of subunits within samples and helped to define the search boundaries for other MS based approaches used in this study.”
You can see examples of the data generated by Progenesis in this study in the online supplementary material for the paper. Specifically, Figure 13 shows the use of Progenesis to confirm successful co-enrichment of proteasomal lid subunits, while Figures 21 and 22 show the use of Progenesis quantitative data in proteasome lid pull-down structural modelling, identifying and confirming interactions of the proteasome base subunits with partners in assembly.
Of course it is always great to see another example of Progenesis producing robust data contributing to biological studies; it’s also particularly nice to see our label-free quantitation software effectively applied to structural questions! If you’ve got a recent publication that features the use of Progenesis that you’d like to see discussed on our blog, get in touch.
About Florian Stengel
Florian Stengel studied biochemistry at the FU Berlin and Harvard University. After completing his diploma thesis as a DAAD foreign exchange scholar with Pamela Silver in functional genomics at Harvard Medical School, he went to the University of Cambridge to earn his PhD with Carol Robinson working on the architecture and dynamics of protein complexes using ion mobility and mass spectrometry of intact assemblies.
Since 2011 he is a Sir Henry Wellcome Fellow with the Wellcome Trust and Postdoctoral Research Associate in the laboratory of Ruedi Aebersold at ETH Zurich, where he uses cross-linking mass spectrometry and develops novel hybrid methods for structural biology.
Florian Stengel will start his own laboratory as an Assistant Professor at the University of Konstanz in 2015 and his group will focus on developing and applying novel mass spectrometric and proteomic approaches to quantitatively study the content, assembly and dynamics of intact protein assemblies.
 Argyris Politis, Florian Stengel, Zoe Hall, Helena Hernández, Alexander Leitner, Thomas Walzthoeni, Carol V Robinson & Ruedi Aebersold (2014). A mass spectrometry–based hybrid method for structural modeling of protein complexes. Nat Methods 11 (4): 430-6. (Supplementary material and PMC version of main text freely available).
 Florian Stengel, Ruedi Aebersold and Carol V. Robinson (2012). Joining Forces: Integrating Proteomics and Cross-linking with the Mass Spectrometry of Intact Complexes. Mol Cell Proteomics 11 (3): R111.014027.