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Combining native and ‘omics’ mass spectrometry to identify endogenous ligands bound to membrane proteins


Ligands bound to protein assemblies provide critical information for function, yet are often difficult to capture and define. Here we develop a top-down method, ‘nativeomics’, unifying ‘omics’ (lipidomics, proteomics, metabolomics) analysis with native mass spectrometry to identify ligands bound to membrane protein assemblies. By maintaining the link between proteins and ligands, we define the lipidome/metabolome in contact with membrane porins and a mitochondrial translocator to discover potential regulators of protein function.

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Fig. 1: Nativeomics defines ligands bound to the trimeric membrane porin OmpF through progressive dissection using multiple stages of MSn.
Fig. 2: Nativeomics and MD simulations define the structure of endogenous lipids bound directly to aquaporin Z.
Fig. 3: Identification of unknown ligands bound to TSPO and subsequent fitting of PE 16:0/18:1 into unresolved electron density in the X-ray structure.

Data availability

MS data for the main figures and Extended Data are available from figshare All other data are available from the authors on request.


  1. 1.

    Li, F., Liu, J., Zheng, Y., Garavito, R. M. & Ferguson-Miller, S. Protein structure. Crystal structures of translocator protein (TSPO) and mutant mimic of a human polymorphism. Science 347, 555–558 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Smart, O. S. et al. Validation of ligands in macromolecular structures determined by X-ray crystallography. Acta Crystallogr. D 74, 228–236 (2018).

    CAS  Google Scholar 

  3. 3.

    Foster, S. R. et al. Discovery of human signaling systems: pairing peptides to g protein-coupled receptors. Cell 179, 895–908.e21 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Coman, C. et al. Simultaneous metabolite, protein, lipid extraction (simplex): a combinatorial multimolecular omics approach for systems biology. Mol. Cell. Proteom. 15, 1453–1466 (2016).

    CAS  Google Scholar 

  5. 5.

    Laschet, C., Dupuis, N. & Hanson, J. The G protein-coupled receptors deorphanization landscape. Biochem. Pharmacol. 153, 62–74 (2018).

    CAS  PubMed  Google Scholar 

  6. 6.

    Liebisch, G. et al. Shorthand notation for lipid structures derived from mass spectrometry. J. Lipid Res. 54, 1523–1530 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Parker, J. L. & Newstead, S. Structural basis of nucleotide sugar transport across the Golgi membrane. Nature 551, 521–524 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Bozelli, J. C. et al. Membrane curvature allosterically regulates the phosphatidylinositol cycle, controlling its rate and acyl-chain composition of its lipid intermediates. J. Biol. Chem. 293, 17780–17791 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Senko, M. W. et al. Novel parallelized quadrupole/linear ion trap/orbitrap tribrid mass spectrometer improving proteome coverage and peptide identification rates. Anal. Chem. 85, 11710–11714 (2013).

    CAS  PubMed  Google Scholar 

  10. 10.

    Ben-Nissan, G. et al. Triple-stage mass spectrometry unravels the heterogeneity of an endogenous protein complex. Anal. Chem. 89, 4708–4715 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Skinner, O. S. et al. Top-down characterization of endogenous protein complexes with native proteomics. Nat. Chem. Biol. 14, 36–41 (2018).

    CAS  PubMed  Google Scholar 

  12. 12.

    Liko, I. et al. Lipid binding attenuates channel closure of the outer membrane protein OmpF. Proc. Natl Acad. Sci. USA 115, 6691–6696 (2018).

    CAS  PubMed  Google Scholar 

  13. 13.

    Ziervogel, B. K. & Roux, B. The binding of antibiotics in OmpF porin. Structure 21, 76–87 (2013).

    CAS  PubMed  Google Scholar 

  14. 14.

    Laganowsky, A. et al. Membrane proteins bind lipids selectively to modulate their structure and function. Nature 510, 172–175 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Lippens, J. L. et al. Fourier transform-ion cyclotron resonance mass spectrometry as a platform for characterizing multimeric membrane protein complexes. J. Am. Soc. Mass Spectrom. 29, 183–193 (2018).

    CAS  PubMed  Google Scholar 

  16. 16.

    Schmidt, V., Sidore, M., Bechara, C., Duneau, J. P. & Sturgis, J. N. The lipid environment of Escherichia coli Aquaporin Z. Biochim. Biophys. Acta Biomembr. 1861, 431–440 (2019).

    CAS  PubMed  Google Scholar 

  17. 17.

    Li, F. et al. Translocator Protein 18 kDa (TSPO): an old protein with new functions? Biochemistry 55, 2821–2831 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Nji, E., Chatzikyriakidou, Y., Landreh, M. & Drew, D. An engineered thermal-shift screen reveals specific lipid preferences of eukaryotic and prokaryotic membrane proteins. Nat. Commun. 9, e4253 (2018).

    Google Scholar 

  19. 19.

    Fantin, S. M. et al. Collision Induced unfolding classifies ligands bound to the integral membrane translocator protein. Anal. Chem. 91, 15469–15476 (2019).

    CAS  PubMed  Google Scholar 

  20. 20.

    Fahy, E., Sud, M., Cotter, D. & Subramaniam, S. LIPID MAPS online tools for lipid research. Nucleic Acids Res. 35, W606–W612 (2007).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Mistarz, U. H., Chandler, S. A., Brown, J. M., Benesch, J. L. P. & Rand, K. D. Probing the dissociation of protein complexes by means of gas-phase h/d exchange mass spectrometry. J. Am. Soc. Mass Spectrom. 30, 45–57 (2019).

    CAS  PubMed  Google Scholar 

  22. 22.

    van Montfort, R. L. M., Basha, E., Friedrich, K. L., Slingsby, C. & Vierling, E. Crystal structure and assembly of a eukaryotic small heat shock protein. Nat. Struct. Biol. 8, 1025–1030 (2001).

    PubMed  Google Scholar 

  23. 23.

    Xu, Y. et al. Structures of bacterial homologues of SWEET transporters in two distinct conformations.Nature 515, 448–452 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Liu, S., Neidhardt, E. A., Grossman, T. H., Ocain, T. & Clardy, J. Structures of human dihydroorotate dehydrogenase in complex with antiproliferative agents. Structure 8, 25–33 (2000).

    CAS  PubMed  Google Scholar 

  25. 25.

    Hua, T. et al. Crystal structure of the human cannabinoid receptor CB 1. Cell 167, 750–762.e14 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Warne, T., Serrano-Vega, M. J., Tate, C. G. & Schertler, G. F. X. Development and crystallization of a minimal thermostabilised G protein-coupled receptor. Protein Expr. Purif. 65, 204–213 (2009).

    CAS  PubMed  Google Scholar 

  27. 27.

    Yen, H. Y. et al. PtdIns(4,5)P2 stabilizes active states of GPCRs and enhances selectivity of G-protein coupling. Nature 559, 423–427 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Housden, N. G. et al. Directed epitope delivery across the Escherichia coli outer membrane through the porin OmpF. Proc. Natl Acad. Sci. USA 107, 21412–21417 (2010).

    CAS  PubMed  Google Scholar 

  29. 29.

    Reading, E. et al. The role of the detergent micelle in preserving the structure of membrane proteins in the gas phase. Angew. Chemie Int. Ed. 54, 4577–4581 (2015).

    CAS  Google Scholar 

  30. 30.

    Li, F., Xia, Y., Meiler, J. & Ferguson-Miller, S. Characterization and modeling of the oligomeric state and ligand binding behavior of purified translocator protein 18 kDa from Rhodobacter sphaeroides. Biochemistry 52, 5884–5899 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Hernández, H. & Robinson, C. V. Determining the stoichiometry and interactions of macromolecular assemblies from mass spectrometry. Nat. Protoc. 2, 715–726 (2007).

    PubMed  Google Scholar 

  32. 32.

    McGee, J. P. et al. Voltage rollercoaster filtering of low-mass contaminants during native protein analysis. J. Am. Soc. Mass Spectrom. 31, 763–767 (2020).

    CAS  PubMed  Google Scholar 

  33. 33.

    Mehmood, S. et al. Charge reduction stabilizes intact membrane protein complexes for mass spectrometry. J. Am. Chem. Soc. 136, 17010–17012 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Liko, I., Hopper, J. T. S., Allison, T. M., Benesch, J. L. P. & Robinson, C. V. Negative ions enhance survival of membrane protein complexes. J. Am. Soc. Mass Spectrom. 27, 1099–1104 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Laganowsky, A., Reading, E., Hopper, J. T. S. & Robinson, C. V. Mass spectrometry of intact membrane protein complexes. Nat. Protoc. 8, 639–651 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Gault, J. et al. High-resolution mass spectrometry of small molecules bound to membrane proteins. Nat. Methods 13, 333–336 (2016).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).

    CAS  PubMed  Google Scholar 

  38. 38.

    Van Der Spoel, D. et al. GROMACS: fast, flexible, and free. J. Comput. Chem. 26, 1701–1718 (2005).

    Google Scholar 

  39. 39.

    Marrink, S. J., Risselada, H. J., Yefimov, S., Tieleman, D. P. & de Vries, A. H. The MARTINI force field: coarse grained model for biomolecular simulations. J. Phys. Chem. B. 111, 7812–7824 (2007).

    CAS  PubMed  Google Scholar 

  40. 40.

    Qi, Y. et al. CHARMM-GUI martini maker for coarse-grained simulations with the Martini force field. J. Chem. Theory Comput. 11, 4486–4494 (2015).

    CAS  PubMed  Google Scholar 

  41. 41.

    Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: a new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981).

    CAS  Google Scholar 

  42. 42.

    Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    CAS  Google Scholar 

  43. 43.

    Wang, J. Comment on ‘Crystal structures of translocator protein (TSPO) and mutant mimic of a human polymorphism’. Science 350, 519–519 (2015).

    CAS  PubMed  Google Scholar 

  44. 44.

    Moriarty, N. W., Grosse-Kunstleve, R. W. & Adams, P. D. Electronic ligand builder and optimization workbench (eLBOW): a tool for ligand coordinate and restraint generation. Acta Crystallogr. D 65, 1074–1080 (2009).

    CAS  PubMed  Google Scholar 

  45. 45.

    Adams, P. D. et al. PHENIX: a comprehensive python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    CAS  Google Scholar 

  46. 46.

    Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D 66, 12–21 (2010).

    CAS  PubMed  Google Scholar 

  47. 47.

    Going, C. C., Xia, Z. & Williams, E. R. New supercharging reagents produce highly charged protein ions in native mass spectrometry. Analyst 140, 7184–7194 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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C.V.R. is funded by a Wellcome Trust Investigator Award (no. 104633/Z/14/Z), an European Research Council Advanced Grant ENABLE (no. 641317) and a Medical Research Council Programme grant (no. MR/N020413/1). M.L. is supported by an Ingvar Carlsson Award from the Swedish Foundation for Strategic Research and a KI Faculty-funded Career Position. For the provision of computational resources, we thank the HECBioSim Consortium (Engineering and Physical Sciences Research Council EPSRC grant no. EP/R029407/1), and The University of Southampton High Performance Computing Facilities, Iridis 4 and 5. This research was also supported by an EPSRC Institutional Sponsorship 2016 award (no. EP/P511377/1) to J.G. and C.V.R. C.V.R. and J.G. perform consultancy services for OMass Therapeutics Ltd, and H.-Y.Y. and I.L. are employees of that company. J.G. is a Junior Research Fellow at The Queen’s College, Oxford. We thank N. Housden and C. Kleanthous (University of Oxford) for providing purified OmpF and OBS1; S. Chandler, D. Cooper-Shepherd and J. Benesch (University of Oxford) for HSP 16.5 and 16.9; and V. Papadopoulos and C. Essagian (McGill University Health Center) for generously providing the plasmid for Rs TSPO; T. Alison (University of Canterbury, New Zealand), M. McDonough (University of Oxford) and C.-C. Su (Case Western Reserve University, USA) for helpful discussions with electron density fitting; S. Fantin and B. Ruotolo (University of Michigan) for insightful discussions regarding TSPO, as well as members of the Robinson, Benesch and Rauschenbach groups (University of Oxford), Thermo Fisher Scientific and OMass Therapeutics for many helpful discussions and support.

Author information




J.G., I.L. and C.V.R. designed experiments with M.L. J.G. and I.L. performed MS experiments with assistance of R.H., C.M., J.E.P.S. and R.V. M.J.G.W.L. contributed to preliminary MS experiments. P.M.R., G.A. and M.G. designed and implemented features incorporated into the Orbitrap Eclipse mass spectrometer that enabled the work. R.H. and R.V. assisted with the instrument setup for preliminary experiments. J.G. and I.L. expressed and purified AqpZ and AmtB. D.S. expressed and purified semiSWEET and TSPO. M.A. provided lipidated AqpZ and H.-Y.Y. provided beta-1-adrenergic receptor and cannabinoid receptor. J.R.B. performed electron density fitting experiments. D.J. and S.K. performed all molecular dynamics simulations for AqpZ. J.G., I.L. and C.V.R. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Joseph Gault or Carol V. Robinson.

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Competing interests

I.L. and H.-Y.Y. are employees of OMass Therapeutics. J.G. and C.V.R. provide consultancy services to OMass Therapeutics. C.M., P.M.R., R.H., G.M., M.G., R.V. and J.E.P.S. are employees of Thermo Fisher Scientific.

Additional information

Peer review information Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Schematic of the Nativeomics workflow for identification of ligands from soluble and membrane proteins assemblies.

Successive rounds of MS/MS are applied to identify ligands bound to a, soluble, and b, membrane, protein assemblies. For multi-subunit complexes this can be achieved with or without subunit localisation of ligand binding, which if desired, adds at least one further MS/MS stage, as indicated in the flow schemes. (see Supplementary Table 1).

Extended Data Fig. 2 Schematic of the Thermo Scientific Orbitrap Eclipse tribrid mass spectrometer.

Important components for native MS are labelled. Guidance for tuning of important parameters for native MS is provided in Online Methods and Supplementary Figures 1–3 and is applicable to both soluble and membrane proteins assemblies.

Extended Data Fig. 3 Native MS of soluble and membrane protein assemblies performed using the Orbitrap Eclipse tribrid platform.

Soluble protein assemblies a, (17–386 kDa), and membrane protein assemblies b, (22–128 kDa) are ordered with increasing molecular mass and MS conditions were optimised to maximise ion intensity and retain the intact assembly. Charge state (CS) distributions are all approximately Gaussian (Note that DHODH has a slight LDAO effect29 and semiSWEET is mixture of monomer and dimer) and maximum CSs are as expected from native folded protein ions. The glycan profile of antibody Trastuzumab (Herceptin) is shown in the inset. In the cases of myoglobin and DHODH there is complete retention of the non-covalently bound co-factors haem and flavin mononucleotide (FMN) respectively. Most membrane proteins harbour residual detergent adducts - adduct peaks in the spectra of semiSWEET and AmtB, for example. All membrane proteins were electrosprayed from buffers containing ‘charge reducing’29,34 detergents. This, together with the reduction in the number of ionisable residues and reduced exposed surface area for charging, means that CSs are shifted to lower m/z values compared to soluble protein assemblies of similar mass. (c) A plot of mass against expected m/z of the average charge state (Zave) based on previously observed empirical relationships29 indicates good agreement between the size of complexes predicted, and those achieved experimentally, up to the m/z 8,000 limit of the instrument. Arbitrary bounds of ±7% are included to represent the approximate width of the CS envelope. Curves are Zave = a MW^b where a = 0.0467 and b = 0.533 for soluble proteins and a = 0.0036, b = 0.71 for membrane proteins29. HSP16.5 oligomers usually appear at > m/z 8,000 but have been supercharged with meta nitro benzyl alcohol47 to shift the CS envelope into a range below m/z 8,000.

Extended Data Fig. 4 Nativeomics defines lipid, peptide and drug bound to the trimeric membrane porin OmpF through progressive dissection using multiple stages of MSn.

a, OmpF bound to lipids is released from detergent micelles (pMS2), the 16+ charge state isolated (red), with lipids released from the complex (pMS3), the peak (m/z 760.5) is then isolated and fragmented to yield fragments at m/z 504.3 and 478.1 (pMS4). Spectral matching assigns this lipid as PC 16:0/18:1 b, OmpF bound to peptide OBS1 is released from detergent micelle (pMS2) and a charge state assigned to the OmpF trimer and three OBS1 peptides was isolated (17+, orange) for pMS3. m/z 777.3 is isolated and fragmented (pMS4) to yield b and y ions that enable peptide sequence determination. c, OmpF bound to ampicillin. The 17+ charge state (green) was isolated for activation and the ligand released at m/z 350.5 (pMS3). Fragmentation (pMS4) yields characteristic ions that identify ampicillin following database searching or spectral matching. For MS parameters see Online Methods.

Extended Data Fig. 5 Nativeomics applied to AqpZ tetramer to progressively dissect the assembly and identify multiple proteoforms.

a, pMS2 effects the removal of the C8E4 detergent micelle in the source region (in-source activation 136 V, source compensation voltage 10%) b, the 16+ charge state (m/z 6,183) is then selected using the ion trap and c, dissociated (MS3) in the HCD cell (NCE 22%) ejecting monomers. d, Isolation of the monomer (8+) (m/z 3,090) is performed in the ion trap, the inset (expansion) shows additional species at ~ +29 Da and ~ +46 Da. e, pMS4 top-down fragmentation (CID NCE 20%) of these species in the ion trap and detection at high-resolution in the Orbitrap reveals predominantly b and y fragment ions in the low m/z region. f, Assignment of the 1+ charge, b16 ion at m/z 1,900.96, and +28 Da partner at m/z 1,928.96 is consistent with assignment of the +28 Da modification to N-terminal formylation as previously suggested15. Percentage formylation of 30–40% estimated based on b ion intensity ratios. g, Coverage of non-modified AqpZ identified with E value of 1.8e-20. h) Coverage map of N-terminally formylated AqpZ identified with E value of 1.6e-11. The top-down data presented here, does not represent a fully optimised top-down experiment for proteoform ID. Longer acquisition times and other complimentary fragmentation methods would likely improve sequence coverage, however, this data clearly demonstrates the capability of the modified ion trap on the Orbitrap Eclipse platform for isolation and fragmentation of high m/z ions (m/z 3,000–8,000). Furthermore it showcases the application of protein-centric Nativeomics to isolate individual membrane protein assemblies from mixtures, dissect them into subunits and identify proteoforms through top-down pMS4. Data is representative of two biological repeats.

Extended Data Fig. 6 MD simulations of AqpZ in mixed lipid bilayers.

a, Side view snapshots of aquaporin in the POPE-POPG bilayer after 3780 ns of simulation time. Snapshots show the protein backbone-bonding scheme (left) and using a RWB colour bar, protein residues are coloured according to the number of times they come into contact with POPG lipids (based on a 0.6 nm cutoff), during the last 1000 ns of simulation time (right). The inset figure shows a representative top view snapshot of aquaporin. b, Side view snapshots of aquaporin in the POPE-DPPG bilayer after 3780 ns of simulation time. Snapshots show the protein backbone-bonding scheme (left) and using a RWB colour bar, protein residues are coloured according to the number of times they come into contact with DPPG lipids, during the last 1000 ns of simulation time (right). c, The number of aquaporin-DPPG contacts is subtracted from the number of aquaporin-POPG contacts (during the last 1000 ns of simulation time) and the resulting quantities (per residue) are assigned a colour based on the asymmetric RWB colour bar provided. In other words, the differences in lipid binding per residue (based on a 0.6 nm cut-off) are depicted using a RWB colour scale for clarity. d, The average number of contacts for each residue of aquaporin (based on a 0.6 nm cut-off) during the last 1000 ns of simulation time. Data are shown for the simulations with POPG (blue) and DPPG (red) lipids.

Extended Data Fig. 7 Comparison of TSPO lipid structures after fitting lipids into the electron density map of TSPO structure (PDB 4UC1).

The FEM omit map, is shown in blue and is contoured at 1.0 s. a, shows the previous fitting where DG(16:0/18:1) was modelled (yellow sticks), b, shows PE(16:0/18:1) fitting, which is accommodated extremely well (red sticks) c, shows PG(16:0/18:1) fitting (orange sticks) with quite poor matching between the PG headgroup and electron density in that region d, displays the favourable interactions between PE (16:0/18:1) and the protein, notably the hydrogen bond between the terminal amine and the aspartic acid Asp4. Hydrophobic and hydrogen bonding interactions are indicated (green and black dots respectively).

Supplementary information

Supplementary information

Supplementary Table 1 and Figs. 1–9.

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Gault, J., Liko, I., Landreh, M. et al. Combining native and ‘omics’ mass spectrometry to identify endogenous ligands bound to membrane proteins. Nat Methods 17, 505–508 (2020).

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