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Site-resolved energetic information from HX–MS experiments

Nature Chemical Biology (2025)Cite this article

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Abstract

High-resolution energetic information about protein conformational ensembles is essential for understanding protein function, yet remains challenging to obtain. Here we present PIGEON-FEATHER, a method for calculating ensemble free energies of opening (∆Gop) at single-amino-acid or near-single-amino-acid resolution for proteins of all sizes from hydrogen exchange–mass spectrometry (HX–MS) data. PIGEON-FEATHER disambiguates and reconstructs all experimentally measured HX–MS isotopic mass envelopes using a Bayesian Monte Carlo sampling approach. We applied PIGEON-FEATHER to reveal how Escherichia coli and human dihydrofolate reductases (ecDHFR and hDHFR) have evolved distinct ensembles. We show how two competitive inhibitors bind these orthologs differently, solving the longstanding mystery of why both therapeutic molecules inhibit ecDHFR but only one inhibits hDHFR. Extending PIGEON-FEATHER to a large protein–DNA complex, we mapped ligand-induced ensemble reweighting in the E. coli lac repressor to describe the functional switching mechanism crucial for transcriptional regulation.

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Fig. 1: PIGEON-FEATHER overview.
Fig. 2: MS envelope fitting with FEATHER.
Fig. 3: Site-resolved local energetics of DHFRs.
Fig. 4: Two competitive inhibitors reweight the ecDHFR ensemble differently.
Fig. 5: Comparing the effects of TMP and MTX binding on the hDHFR ensemble.
Fig. 6: IPTG binding shifts the LacI ensemble to the transcriptionally active state.

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Data availability

Data supporting the main findings are included in the article and its Supplementary Information. All data needed to reproduce this work are available from the PRIDE database84 (dataset identifier PXD057539). Source data are provided with this paper.

Code availability

The source code for the software is available from GitHub (https://github.com/glasgowlab/PIGEON-FEATHER), along with the synthetic datasets used for benchmarking, an example HX–MS dataset and a tutorial.

References

  1. Boehr, D. D., Nussinov, R. & Wright, P. E. The role of dynamic conformational ensembles in biomolecular recognition. Nat. Chem. Biol. 5, 789–796 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Cooper, A. Thermodynamic fluctuations in protein molecules. Proc. Natl Acad. Sci. USA 73, 2740–2741 (1976).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hilser, V. J. An ensemble view of allostery. Science 327, 653–654 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Balbach, J. et al. Following protein folding in real time using NMR spectroscopy. Nat. Struct. Biol. 2, 865–870 (1995).

    Article  CAS  PubMed  Google Scholar 

  5. Roder, H., Elöve, G. A. & Englander, S. W. Structural characterization of folding intermediates in cytochrome c by H-exchange labelling and proton NMR. Nature 335, 700–704 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shaw, D. E. et al. Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51, 91–97 (2008).

    Article  Google Scholar 

  7. Kish, M. et al. Online fully automated system for hydrogen/deuterium-exchange mass spectrometry with millisecond time resolution. Anal. Chem. 95, 5000–5008 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Espada, A. et al. A decoupled automation platform for hydrogen/deuterium exchange mass spectrometry experiments. J. Am. Soc. Mass. Spectrom. 30, 2580–2583 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).

    Article  CAS  PubMed  Google Scholar 

  10. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Konermann, L., Pan, J. & Liu, Y.-H. Hydrogen exchange mass spectrometry for studying protein structure and dynamics. Chem. Soc. Rev. 40, 1224–1234 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. Bai, Y., Sosnick, T. R., Mayne, L. & Englander, S. W. Protein folding intermediates: native-state hydrogen exchange. Science 269, 192–197 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Skinner, J. J., Lim, W. K., Bédard, S., Black, B. E. & Englander, S. W. Protein hydrogen exchange: testing current models. Protein Sci. 21, 987–995 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bai, Y., Milne, J. S., Mayne, L. & Englander, S. W. Primary structure effects on peptide group hydrogen exchange. Proteins 17, 75–86 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Masson, G. R. et al. Recommendations for performing, interpreting and reporting hydrogen deuterium exchange mass spectrometry (HDX–MS) experiments. Nat. Methods 16, 595–602 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Weis, D. D. Recommendations for the propagation of uncertainty in hydrogen exchange–mass spectrometric measurements. J. Am. Soc. Mass. Spectrom. 32, 1610–1617 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Hilser, V. J. & Freire, E. Structure-based calculation of the equilibrium folding pathway of proteins. correlation with hydrogen exchange protection factors. J. Mol. Biol. 262, 756–772 (1996).

    Article  CAS  PubMed  Google Scholar 

  18. Liu, T. et al. Quantitative assessment of protein structural models by comparison of H/D exchange MS data with exchange behavior accurately predicted by DXCOREX. J. Am. Soc. Mass. Spectrom. 23, 43–56 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Eggertson, M. J., Fadgen, K., Engen, J. R. & Wales, T. E. Considerations in the analysis of hydrogen exchange mass spectrometry data. In Mass Spectrometry Data Analysis in Proteomics (ed. Matthiesen, R.)(Springer, 2020).

  20. Kan, Z., Ye, X., Skinner, J. J., Mayne, L. & Englander, S. W. ExMS2: an integrated solution for hydrogen–deuterium exchange mass spectrometry data analysis. Anal. Chem. 91, 7474–7481 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Guttman, M., Weis, D. D., Engen, J. R. & Lee, K. K. Analysis of overlapped and noisy hydrogen/deuterium exchange mass spectra. J. Am. Soc. Mass. Spectrom. 24, 1906–1912 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Saltzberg, D. J. et al. A residue-resolved Bayesian approach to quantitative interpretation of hydrogen–deuterium exchange from mass spectrometry: application to characterizing protein–ligand interactions. J. Phys. Chem. B 121, 3493–3501 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Smit, J. H. et al. Probing universal protein dynamics using hydrogen–deuterium exchange mass spectrometry-derived residue-level Gibbs free energy. Anal. Chem. 93, 12840–12847 (2021).

    Article  CAS  PubMed  Google Scholar 

  24. Stofella, M., Skinner, S. P., Sobott, F., Houwing-Duistermaat, J. & Paci, E. High-resolution hydrogen–deuterium protection factors from sparse mass spectrometry data validated by nuclear magnetic resonance measurements. J. Am. Soc. Mass. Spectrom. 33, 813–822 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kan, Z.-Y., Walters, B. T., Mayne, L. & Englander, S. W. Protein hydrogen exchange at residue resolution by proteolytic fragmentation mass spectrometry analysis. Proc. Natl Acad. Sci. USA 110, 16438–16443 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhang, Z., Zhang, A. & Xiao, G. Improved protein hydrogen/deuterium exchange mass spectrometry platform with fully automated data processing. Anal. Chem. 84, 4942–4949 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Ferrari, Á. J. R. et al. Large-scale discovery, analysis, and design of protein energy landscapes. Preprint at bioRxiv https://doi.org/10.1101/2025.03.20.644235 (2025).

  28. Huennekens, F. M. The methotrexate story: a paradigm for development of cancer chemotherapeutic agents. Adv. Enzym. Regul. 34, 397–419 (1994).

    Article  CAS  Google Scholar 

  29. Oyeyemi, O. A. et al. Comparative hydrogen–deuterium exchange for a mesophilic vs thermophilic dihydrofolate reductase at 25 °C: identification of a single active site region with enhanced flexibility in the mesophilic protein. Biochemistry 50, 8251–8260 (2011).

    Article  CAS  PubMed  Google Scholar 

  30. Yamamoto, T. Mass spectrometry on segment-specific hydrogen exchange of dihydrofolate reductase. J. Biochem. 135, 17–24 (2004).

    Article  CAS  PubMed  Google Scholar 

  31. Wales, T. E. et al. Resolving chaperone-assisted protein folding on the ribosome at the peptide level. Nat. Struct. Mol. Biol. 31, 1888–1897 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pancsa, R., Varadi, M., Tompa, P. & Vranken, W. F. Start2Fold: a database of hydrogen/deuterium exchange data on protein folding and stability. Nucleic Acids Res. 44, D429–D434 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. Jones, B. E. & Matthews, C. R. Early intermediates in the folding of dihydrofolate reductase from escherichia coli detected by hydrogen exchange and NMR. Protein Sci. 4, 167–177 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Schnell, J. R., Dyson, H. J. & Wright, P. E. Structure, dynamics, and catalytic function of dihydrofolate reductase. Annu. Rev. Biophys. 33, 119–140 (2004).

    Article  CAS  Google Scholar 

  35. Wan, Q. et al. Toward resolving the catalytic mechanism of dihydrofolate reductase using neutron and ultrahigh-resolution X-ray crystallography. Proc. Natl Acad. Sci. USA 111, 18225–18230 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Vendruscolo, M., Paci, E., Dobson, C. M. & Karplus, M. Rare fluctuations of native proteins sampled by equilibrium hydrogen exchange. J. Am. Chem. Soc. 125, 15686–15687 (2003).

    Article  CAS  PubMed  Google Scholar 

  37. Alford, R. F. et al. The Rosetta All-Atom energy function for macromolecular modeling and design. J. Chem. Theory Comput. 13, 3031–3048 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Nguyen, T. T., Marzolf, D. R., Seffernick, J. T., Heinze, S. & Lindert, S. Protein structure prediction using residue-resolved protection factors from hydrogen-deuterium exchange NMR. Structure 30, 313–320 (2022).

    Article  CAS  PubMed  Google Scholar 

  39. Hollien, J. & Marqusee, S. Structural distribution of stability in a thermophilic enzyme. Proc. Natl Acad. Sci. USA 96, 13674–13678 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Liu, C. T. et al. Functional significance of evolving protein sequence in dihydrofolate reductase from bacteria to humans. Proc. Natl Acad. Sci. USA 110, 10159–10164 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bhabha, G. et al. Divergent evolution of protein conformational dynamics in dihydrofolate reductase. Nat. Struct. Mol. Biol. 20, 1243–1249 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bystroff, C. & Kraut, J. Crystal structure of unliganded Escherichia coli dihydrofolate reductase. Ligand-induced conformational changes and cooperativity in binding. Biochemistry 30, 2227–2239 (1991).

    Article  CAS  PubMed  Google Scholar 

  43. Bhabha, G. et al. A dynamic knockout reveals that conformational fluctuations influence the chemical step of enzyme catalysis. Science 332, 234–238 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Cameron, C. E. & Benkovic, S. J. Evidence for a functional role of the dynamics of glycine-121 of Escherichia coli dihydrofolate reductase obtained from kinetic analysis of a site-directed mutant. Biochemistry 36, 15792–15800 (1997).

    Article  CAS  PubMed  Google Scholar 

  45. Miller, G. P. & Benkovic, S. J. Strength of an interloop hydrogen bond determines the kinetic pathway in catalysis by Escherichia coli dihydrofolate reductase. Biochemistry 37, 6336–6342 (1998).

    Article  CAS  PubMed  Google Scholar 

  46. Appleman, J. R. et al. Atypical transient state kinetics of recombinant human dihydrofolate reductase produced by hysteretic behavior. Comparison with dihydrofolate reductases from other sources. J. Biol. Chem. 264, 2625–2633 (1989).

    Article  CAS  PubMed  Google Scholar 

  47. Blakley, R. L. Eukaryotic dihydrofolate reductase. Adv. Enzymol. Relat. Areas Mol. Biol. 70, 23–102 (1995).

    CAS  PubMed  Google Scholar 

  48. Carroll, M. J. et al. Evidence for dynamics in proteins as a mechanism for ligand dissociation. Nat. Chem. Biol. 8, 246–252 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sawaya, M. R. & Kraut, J. Loop and subdomain movements in the mechanism of Escherichia coli dihydrofolate reductase: crystallographic evidence. Biochemistry 36, 586–603 (1997).

    Article  CAS  PubMed  Google Scholar 

  50. Manna, M. S. et al. A trimethoprim derivative impedes antibiotic resistance evolution. Nat. Commun. 12, 2949 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Mauldin, R. V., Carroll, M. J. & Lee, A. L. Dynamic dysfunction in dihydrofolate reductase results from antifolate drug binding: modulation of dynamics within a structural state. Structure 17, 386–394 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Chen, J., Dima, R. I. & Thirumalai, D. Allosteric communication in dihydrofolate reductase: signaling network and pathways for closed to occluded transition and back. J. Mol. Biol. 374, 250–266 (2007).

    Article  CAS  PubMed  Google Scholar 

  53. Krucinska, J. et al. Structure-guided functional studies of plasmid-encoded dihydrofolate reductases reveal a common mechanism of trimethoprim resistance in Gram-negative pathogens. Commun. Biol. 5, 1–14 (2022).

    Article  Google Scholar 

  54. Rodriguez, D. C. P., Weber, K. C., Sundberg, B. & Glasgow, A. MAGPIE: an interactive tool for visualizing and analyzing protein–ligand interactions. Protein Sci. 33, e5027 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Dion-Schultz, A. & Howell, E. E. Effects of insertions and deletions in a beta-bulge region of Escherichia coli dihydrofolate reductase. Protein Eng. Des. Sel. 10, 263–272 (1997).

    Article  CAS  Google Scholar 

  56. Bullerjahn, A. M. & Freisheim, J. H. Site-directed deletion mutants of a carboxyl-terminal region of human dihydrofolate reductase. J. Biol. Chem. 267, 864–870 (1992).

    Article  CAS  PubMed  Google Scholar 

  57. Ratnam, M., Tan, X., Prendergast, N. J., Smith, P. L. & Freisheim, J. H. Ligand-induced structural constraints in human dihydrofolate reductase revealed by peptide-specific antibodies. Biochemistry 27, 4800–4804 (1988).

    Article  CAS  PubMed  Google Scholar 

  58. Howell, E. E., Booth, C., Farnum, M., Kraut, J. & Warren, M. S. A second-site mutation at phenylalanine-137 that increases catalytic efficiency in the mutant aspartate-27 .fwdarw. serine Escherichia coli dihydrofolate reductase. Biochemistry 29, 8561–8569 (1990).

    Article  CAS  PubMed  Google Scholar 

  59. Brown, K. A., Howell, E. E. & Kraut, J. Long-range structural effects in a second-site revertant of a mutant dihydrofolate reductase. Proc. Natl Acad. Sci. USA 90, 11753–11756 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Dion, A., Linn, C. E., Bradrick, T. D., Georghiou, S. & Howell, E. E. How do mutations at phenylalanine-153 and isoleucine-155 partially suppress the effects of the aspartate-27 .fwdarw. serine mutation in Escherichia coli dihydrofolate reductase?. Biochemistry 32, 3479–3487 (1993).

    Article  CAS  PubMed  Google Scholar 

  61. Watson, M., Liu, J.-W. & Ollis, D. Directed evolution of trimethoprim resistance in Escherichia coli. FEBS J. 274, 2661–2671 (2007).

    Article  CAS  PubMed  Google Scholar 

  62. Toprak, E. Evolutionary paths to strong antibiotic resistance under dynamically sustained drug stress. Nat. Genet. 44, 101–105 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Daber, R., Stayrook, S., Rosenberg, A. & Lewis, M. Structural analysis of lac repressor bound to allosteric effectors. J. Mol. Biol. 370, 609–619 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Glasgow, A. et al. Ligand-specific changes in conformational flexibility mediate long-range allostery in the lac repressor. Nat. Commun. 14, 1179 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Markiewicz, P., Kleina, L. G., Cruz, C., Ehret, S. & Miller, J. H. Genetic studies of the lac repressor. XIV. Analysis of 4000 altered Escherichia coli lac repressors reveals essential and non-essential residues, as well as ‘spacers’ which do not require a specific sequence. J. Mol. Biol. 240, 421–433 (1994).

    Article  CAS  PubMed  Google Scholar 

  66. Suckow, J. et al. Genetic studies of the lac repressor XV: 4000 single amino acid substitutions and analysis of the resulting phenotypes on the basis of the protein structure. J. Mol. Biol. 261, 509–523 (1996).

    Article  CAS  PubMed  Google Scholar 

  67. Romanuka, J. et al. Genetic switching by the lac repressor is based on two-state Monod–Wyman–Changeux allostery. Proc. Natl Acad. Sci. USA 120, e2311240120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Kalodimos, C. G., Folkers, G. E., Boelens, R. & Kaptein, R. Strong DNA binding by covalently linked dimeric Lac headpiece: evidence for the crucial role of the hinge helices. Proc. Natl Acad. Sci. USA 98, 6039–6044 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Zhong, E. D., Bepler, T., Berger, B. & Davis, J. H. CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks. Nat. Methods 18, 176–185 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

    Article  CAS  PubMed  Google Scholar 

  71. van Kempen, M. et al. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. 42, 243–246 (2024).

    Article  PubMed  Google Scholar 

  72. Halabi, N., Rivoire, O., Leibler, S. & Ranganathan, R. Protein sectors: evolutionary units of three-dimensional structure. Cell 138, 774–786 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).

    Article  CAS  PubMed  Google Scholar 

  74. Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Bell, C. E. & Lewis, M. A closer view of the conformation of the lac repressor bound to operator. Nat. Struct. Biol. 7, 209–214 (2000).

    Article  CAS  PubMed  Google Scholar 

  77. Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Goloborodko, A. A., Levitsky, L. I., Ivanov, M. V. & Gorshkov, M. V. Pyteomics—a Python framework for exploratory data analysis and rapid software prototyping in proteomics. J. Am. Soc. Mass. Spectrom. 24, 301–304 (2013).

    Article  CAS  PubMed  Google Scholar 

  79. Levitsky, L. I., Klein, J. A., Ivanov, M. V. & Gorshkov, M. V. Pyteomics 4.0: five years of development of a Python proteomics framework. J. Proteome Res. 18, 709–714 (2019).

    Article  CAS  PubMed  Google Scholar 

  80. Skinner, J. J., Lim, W. K., Bédard, S., Black, B. E. & Englander, S. W. Protein dynamics viewed by hydrogen exchange. Protein Sci. 21, 996–1005 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Smit, J. Jhsmit/HDXrate: HDXrate version 0.2.2. Zenodo https://doi.org/10.5281/zenodo.10022160 (2023).

  82. Nguyen, D., Mayne, L., Phillips, M. C. & Walter Englander, S. Reference parameters for protein hydrogen exchange rates. J. Am. Soc. Mass. Spectrom. 29, 1936–1939 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Röst, H. L., Schmitt, U., Aebersold, R. & Malmström, L. pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics 14, 74–77 (2014).

    Article  PubMed  Google Scholar 

  84. Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We acknowledge A.G. Lab members for requesting, adding, discussing, testing and troubleshooting various features of the software. PIGEON-FEATHER was tested by D. Swingle at the Advanced Science Research Center (ASRC) at the City University of New York. We thank G. Rocklin’s group for providing the EEHEE_rd4_087 sample and NMR dataset and for useful discussions about these data. We thank A. Piserchio, K. Gardner and R. Abzalimov from the ASRC, as well as S. Costello, S. Shoemaker and N. Latorraca, for feedback on the method. We also thank R. Abzalimov in his role as the MS facility manager at the ASRC, where the HX–MS experiments were performed. We are grateful to S. Pratihar, H. Al-Hashimi and A. Palmer at Columbia University Medical Center for valuable discussions and S. Costello, H. Al-Hashimi, R. Abzalimov, R. Azad, J. Glasgow, T. Kortemme and S. Lomvardas for critical feedback on this manuscript. This work was supported by National Institutes of Health grants R00GM135529 and R21EB035208 to A.G. and a National Science Foundation Graduate Research Fellowship to S.K.M.

Author information

Author notes

  1. These authors contributed equally: Chenlin Lu, Malcolm L. Wells.

Authors and Affiliations

  1. Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA

    Chenlin Lu, Malcolm L. Wells, Andrew Reckers, Savannah K. McBride & Anum Glasgow

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Contributions

C.L., M.L.W. and A.G. developed the computational methods. M.L.W., A.R. and S.K.M. collected the HX–MS data. All authors tested the methods and analyzed and interpreted the data. C.L., M.L.W. and A.G. wrote the manuscript with input from all authors. A.G. conceptualized and supervised the study.

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Correspondence to Anum Glasgow.

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Extended data

Extended Data Fig. 1 Peptide disambiguation with PIGEON.

a, HX–MS peptides are frequently degenerate in mass. b, The number of degenerate peptides in E. coli DHFR (ecDHFR) given measurement uncertainty (Δm/z ~0.001 within experiments, ~0.01 between experiments). c, ecDHFR peptides are duplicatively matched to the same peak. d, Disambiguation of peptides in panel c using tandem MS fragment information. The natural isotopic distribution corresponding to a single MS compound is shown, as extracted from the marked portion of the base peak intensity chromatogram. MS2 ions from this compound are compared to theoretical ions for the matched peptides, and the matches scored based on the degree of fragment ion support. e,(i), MS2 data are pooled for all replicates and matched to theoretical peptides for each protein in the dataset. e(ii), Systematic mass errors are corrected by fitting a trendline to the highest scoring matches and re-thresholding around the trendline. All but the best peak for each peptide are discarded. e(iii), Each peptide for which there exists an identical match and which is not adequately supported by MS fragments is dropped. e(iv), Each charge state for which there exists an identical match (as in iii.) but which has fragment support is either kept (‘KEEP’), or discarded (‘DROP’). These peptides co-elute and both contribute to the signal. e(v), The final cleaned dataset contains peptides with scores distributed over the full range. f, Initial score and m/z error distribution for a representative pooled dataset (as in E, i). g, A duplicate match where the fragment ions support one peptide but not the other. h, Impact of each PIGEON step (systematic error correction and disambiguation) and peptide selection mode for four ecDHFR HX–MS datasets (Supplementary Tables 1 and 8). The use of multiple proteases increases peptide coverage: fungal protease (FP), pepsin, nepenthesin (NP), and alanyl aminopeptidase (AP). The m/z and score distribution for one resulting peptide pool (red box) are shown.

Source data

Extended Data Fig. 2 FEATHER benchmarks.

a, Simulated datasets with varying data quality. b, Benchmarks on the simulated datasets. c, Comparison to other methods using the perfect simulated dataset. The raw data are available in Supplementary Tables 6 and 7.

Extended Data Fig. 3 FEATHER benchmark against HX–NMR.

a, Absolute ΔGop for EEHEE_rd4_087 projected onto the AlphaFold3-predicted structure. b, Overlay of FEATHER-derived exchange rates and HX–NMR data, formatted and calculated as in Fig. 2e. HX–MS data were pooled for FEATHER analysis from three technical replicate timecourses (see Methods and Supplementary Table 8). c, Comparison of ΔGop for 16 protons from both HX–MS-PIGEON-FEATHER and HX–NMR. R and r.m.s.e are calculated using only site-resolvable residues. Error bars reflect FEATHER bootstrapped uncertainties as in panel b. Points are colored according to peptide coverage using the scale in b.

Source data

Supplementary information

Supplementary Information

Review of HX theory, Recommendations for PIGEON-FEATHER usage and Supplementary Figs. 1–31, Tables 1–17, Methods and References.

Reporting Summary

Supplementary Data

Experimental deuterium uptake plots and model fitting plots.

Source data

Source Data Fig. 2

FEATHER-derived −log(kex) values versus true values for a representative simulated dataset.

Source Data Fig. 3

FEATHER-derived ΔGop and −log(kex) values for the apo states of ecDHFR and hDHFR. HX–MS datasets include peptide centroid deuterium uptake and isotopic mass envelopes. Reconstruction errors for both isotopic mass envelopes and centroids are also provided.

Source Data Fig. 4

FEATHER-derived ΔGop and −log(kex) values for the apo, TMP and MTX states of ecDHFR.

Source Data Fig. 6

FEATHER-derived ΔGop and −log(kex) values for the apo, DNA and IPTG states of LacI. HX–MS datasets include peptide centroid deuterium uptake and isotopic mass envelopes of LacI.

Source Data Extended Data Fig. 1

Peptide coverage and matching results using PIGEON. Includes statistics on degenerate peptides, illustrated with the ecDHFR dataset as an example.

Source Data Extended Data Fig. 3

FEATHER-derived ΔGop and −log(kex) values for EEHEE_rd4_087.

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Lu, C., Wells, M.L., Reckers, A. et al. Site-resolved energetic information from HX–MS experiments. Nat Chem Biol (2025). https://doi.org/10.1038/s41589-025-02049-1

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