1932

Abstract

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen–deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-physchem-082720-123928
2022-04-20
2024-06-23
Loading full text...

Full text loading...

/deliver/fulltext/physchem/73/1/annurev-physchem-082720-123928.html?itemId=/content/journals/10.1146/annurev-physchem-082720-123928&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Leelananda SP, Lindert S. 2016. Computational methods in drug discovery. Beilstein J. Org. Chem. 12:2694–718
    [Google Scholar]
  2. 2. 
    Smyth M, Martin J. 2000. x Ray crystallography. Mol. Pathol. 53:8
    [Google Scholar]
  3. 3. 
    Cavalli A, Salvatella X, Dobson CM, Vendruscolo M. 2007. Protein structure determination from NMR chemical shifts. PNAS 104:9615–20
    [Google Scholar]
  4. 4. 
    Costa TR, Ignatiou A, Orlova EV 2017. Structural analysis of protein complexes by cryo electron microscopy. In Bacterial Protein Secretion Systems. . Methods in Molecular BiologyVol. 1615: L Journet, E Cascales 377–413 New York: Humana
    [Google Scholar]
  5. 5. 
    Limpikirati P, Liu T, Vachet RW. 2018. Covalent labeling-mass spectrometry with non-specific reagents for studying protein structure and interactions. Methods 144:79–93
    [Google Scholar]
  6. 6. 
    Kiselar JG, Chance MR. 2010. Future directions of structural mass spectrometry using hydroxyl radical footprinting. J. Mass Spectrom. 45:1373–82
    [Google Scholar]
  7. 7. 
    Liu XR, Zhang MM, Gross ML. 2020. Mass spectrometry-based protein footprinting for higher-order structure analysis: fundamentals and applications. Chem. Rev. 120:4355–454
    [Google Scholar]
  8. 8. 
    Sinz A. 2006. Chemical cross-linking and mass spectrometry to map three-dimensional protein structures and protein–protein interactions. Mass Spectrom. Rev. 25:663–82
    [Google Scholar]
  9. 9. 
    O'Reilly FJ, Rappsilber J. 2018. Cross-linking mass spectrometry: methods and applications in structural, molecular and systems biology. Nat. Struct. Mol. Biol. 25:1000–8
    [Google Scholar]
  10. 10. 
    Konermann L, Pan J, Liu Y-H. 2011. Hydrogen exchange mass spectrometry for studying protein structure and dynamics. Chem. Soc. Rev. 40:1224–34
    [Google Scholar]
  11. 11. 
    Huang W, Ravikumar KM, Chance MR, Yang S 2015. Quantitative mapping of protein structure by hydroxyl radical footprinting-mediated structural mass spectrometry: a protection factor analysis. Biophys. J. 108:107–15
    [Google Scholar]
  12. 12. 
    Xu G, Chance MR. 2007. Hydroxyl radical-mediated modification of proteins as probes for structural proteomics. Chem. Rev. 107:3514–43
    [Google Scholar]
  13. 13. 
    Hager-Braun C, Tomer KB. 2005. Determination of protein-derived epitopes by mass spectrometry. Expert Rev. Proteom. 2:745–56
    [Google Scholar]
  14. 14. 
    Jurneczko E, Barran PE 2011. How useful is ion mobility mass spectrometry for structural biology? The relationship between protein crystal structures and their collision cross sections in the gas phase. Analyst 136:20–28
    [Google Scholar]
  15. 15. 
    Zhou M, Wysocki VH. 2014. Surface induced dissociation: dissecting noncovalent protein complexes in the gas phase. Acc. Chem. Res. 47:1010–18
    [Google Scholar]
  16. 16. 
    Seffernick JT, Lindert S. 2020. Hybrid methods for combined experimental and computational determination of protein structure. J. Chem. Phys. 153:240901
    [Google Scholar]
  17. 17. 
    Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N et al. 2020. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat. Methods 17:665–80
    [Google Scholar]
  18. 18. 
    Alford RF, Leaver-Fay A, Jeliazkov JR, O'Meara MJ, DiMaio FP et al. 2017. The Rosetta all-atom energy function for macromolecular modeling and design. J. Chem. Theory Comput. 13:3031–48
    [Google Scholar]
  19. 19. 
    Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. 2015. The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12:7–8
    [Google Scholar]
  20. 20. 
    Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. 2015. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10:845–58
    [Google Scholar]
  21. 21. 
    Russel D, Lasker K, Webb B, Velázquez-Muriel J, Tjioe E et al. 2012. Putting the pieces together: integrative modeling platform software for structure determination of macromolecular assemblies. PLOS Biol 10:e1001244
    [Google Scholar]
  22. 22. 
    Dominguez C, Boelens R, Bonvin AM. 2003. HADDOCK: a protein−protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc. 125:1731–37
    [Google Scholar]
  23. 23. 
    Webb B, Sali A. 2016. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformat. 54:5.6.1–37
    [Google Scholar]
  24. 24. 
    Schneider M, Belsom A, Rappsilber J, Brock O 2016. Blind testing of cross-linking/mass spectrometry hybrid methods in CASP11. Proteins: Struct. Funct. Bioinformat. 84:152–63
    [Google Scholar]
  25. 25. 
    Kahraman A, Herzog F, Leitner A, Rosenberger G, Aebersold R, Malmström L 2013. Cross-link guided molecular modeling with ROSETTA. PLOS ONE 8:e73411
    [Google Scholar]
  26. 26. 
    Kahraman A, Malmström L, Aebersold R. 2011. Xwalk: computing and visualizing distances in cross-linking experiments. Bioinformatics 27:2163–64
    [Google Scholar]
  27. 27. 
    Lössl P, Kölbel K, Tänzler D, Nannemann D, Ihling CH et al. 2014. Analysis of nidogen-1/laminin γ1 interaction by cross-linking, mass spectrometry, and computational modeling reveals multiple binding modes. PLOS ONE 9:e112886
    [Google Scholar]
  28. 28. 
    Piotrowski C, Moretti R, Ihling CH, Haedicke A, Liepold T et al. 2020. Delineating the molecular basis of the calmodulin–bMunc13–2 interaction by cross-linking/mass spectrometry—evidence for a novel CaM binding motif in bMunc13–2. Cells 9:136
    [Google Scholar]
  29. 29. 
    Belsom A, Schneider M, Fischer L, Brock O, Rappsilber J 2016. Serum albumin domain structures in human blood serum by mass spectrometry and computational biology. Mol. Cell. Proteom. 15:1105–16
    [Google Scholar]
  30. 30. 
    dos Santos RN, Ferrari AJ, de Jesus HC, Gozzo FC, Morcos F, Martínez L 2018. Enhancing protein fold determination by exploring the complementary information of chemical cross-linking and coevolutionary signals. Bioinformatics 34:2201–8
    [Google Scholar]
  31. 31. 
    Hauri S, Khakzad H, Happonen L, Teleman J, Malmström J, Malmström L 2019. Rapid determination of quaternary protein structures in complex biological samples. Nat. Commun. 10:192
    [Google Scholar]
  32. 32. 
    Khakzad H, Happonen L, Van Nhieu GT, Malmström J, Malmström L. 2020. In vivo cross-linking MS of the complement system MAC assembled on live Gram-positive bacteria. Front. Genet. 11:612475
    [Google Scholar]
  33. 33. 
    Ferrari AJ, Gozzo FC, Martínez L. 2019. Statistical force-field for structural modeling using chemical cross-linking/mass spectrometry distance constraints. Bioinformatics 35:3005–12
    [Google Scholar]
  34. 34. 
    Degiacomi MT, Schmidt C, Baldwin AJ, Benesch JL. 2017. Accommodating protein dynamics in the modeling of chemical crosslinks. Structure 25:1751–57.e5
    [Google Scholar]
  35. 35. 
    Mintseris J, Gygi SP. 2020. High-density chemical cross-linking for modeling protein interactions. PNAS 117:93–102
    [Google Scholar]
  36. 36. 
    Pan J, Han J, Borchers CH, Konermann L. 2009. Hydrogen/deuterium exchange mass spectrometry with top-down electron capture dissociation for characterizing structural transitions of a 17 kDa protein. J. Am. Chem. Soc. 131:12801–8
    [Google Scholar]
  37. 37. 
    Zhang Y, Majumder EL-W, Yue H, Blankenship RE, Gross ML. 2014. Structural analysis of diheme cytochrome c by hydrogen–deuterium exchange mass spectrometry and homology modeling. Biochemistry 53:5619–30
    [Google Scholar]
  38. 38. 
    Marzolf DR, Seffernick JT, Lindert S. 2021. Protein structure prediction from NMR hydrogen–deuterium exchange data. J. Chem. Theory Comput. 17:2619–29
    [Google Scholar]
  39. 39. 
    Mohammadiarani H, Shaw VS, Neubig RR, Vashisth H. 2018. Interpreting hydrogen–deuterium exchange events in proteins using atomistic simulations: case studies on regulators of G-protein signaling proteins. J. Phys. Chem. B 122:9314–23
    [Google Scholar]
  40. 40. 
    Martens C, Shekhar M, Lau AM, Tajkhorshid E, Politis A 2019. Integrating hydrogen–deuterium exchange mass spectrometry with molecular dynamics simulations to probe lipid-modulated conformational changes in membrane proteins. Nat. Protoc. 14:3183–204
    [Google Scholar]
  41. 41. 
    Makarov AA, Iacob RE, Pirrone GF, Rodriguez-Granillo A, Joyce L et al. 2020. Combination of HDX-MS and in silico modeling to study enzymatic reactivity and stereo-selectivity at different solvent conditions. J. Pharmaceut. Biomed. Anal. 182:113141
    [Google Scholar]
  42. 42. 
    Zhang MM, Beno BR, Huang RY-C, Adhikari J, Deyanova EG et al. 2019. An integrated approach for determining a protein–protein binding interface in solution and an evaluation of hydrogen–deuterium exchange kinetics for adjudicating candidate docking models. Anal. Chem. 91:15709–17
    [Google Scholar]
  43. 43. 
    Huang RY-C, Krystek SR Jr., Felix N, Graziano RF, Srinivasan M et al. 2018. Hydrogen/deuterium exchange mass spectrometry and computational modeling reveal a discontinuous epitope of an antibody/TL1A interaction. mAbs 10:95–103
    [Google Scholar]
  44. 44. 
    Jeliazkov JR, Frick R, Zhou J, Gray JJ. 2021. Robustification of RosettaAntibody and Rosetta SnugDock. PLOS ONE 16:e0234282
    [Google Scholar]
  45. 45. 
    Xie B, Sood A, Woods RJ, Sharp JS. 2017. Quantitative protein topography measurements by high resolution hydroxyl radical protein footprinting enable accurate molecular model selection. Sci. Rep. 7:4552
    [Google Scholar]
  46. 46. 
    Aprahamian ML, Chea EE, Jones LM, Lindert S. 2018. Rosetta protein structure prediction from hydroxyl radical protein footprinting mass spectrometry data. Anal. Chem. 90:7721–29
    [Google Scholar]
  47. 47. 
    Aprahamian ML, Lindert S. 2019. Utility of covalent labeling mass spectrometry data in protein structure prediction with Rosetta. J. Chem. Theory Comput. 15:3410–24
    [Google Scholar]
  48. 48. 
    Biehn SE, Lindert S. 2021. Accurate protein structure prediction with hydroxyl radical protein footprinting data. Nat. Commun. 12:341
    [Google Scholar]
  49. 49. 
    Manzi L, Barrow AS, Scott D, Layfield R, Wright TG et al. 2016. Carbene footprinting accurately maps binding sites in protein–ligand and protein–protein interactions. Nat. Commun. 7:13288
    [Google Scholar]
  50. 50. 
    Manzi L, Barrow AS, Hopper JT, Kaminska R, Kleanthous C et al. 2017. Carbene footprinting reveals binding interfaces of a multimeric membrane-spanning protein. Angew. Chem. Int. Ed. 56:14873–77
    [Google Scholar]
  51. 51. 
    Cheng M, Zhang B, Cui W, Gross ML 2017. Laser-initiated radical trifluoromethylation of peptides and proteins: application to mass-spectrometry-based protein footprinting. Angew. Chem. Int. Ed. 56:14007–10
    [Google Scholar]
  52. 52. 
    Cheng M, Asuru A, Kiselar J, Mathai G, Chance MR, Gross ML. 2020. Fast protein footprinting by X-ray mediated radical trifluoromethylation. J. Am. Soc. Mass Spectrom. 31:1019–24
    [Google Scholar]
  53. 53. 
    Limpikirati P, Pan X, Vachet RW. 2019. Covalent labeling with diethylpyrocarbonate: sensitive to the residue microenvironment, providing improved analysis of protein higher order structure by mass spectrometry. Anal. Chem. 91:8516–23
    [Google Scholar]
  54. 54. 
    Biehn SE, Limpikirati P, Vachet RW, Lindert S. 2021. Utilization of hydrophobic microenvironment sensitivity in diethylpyrocarbonate labeling for protein structure prediction. Anal. Chem. 93:8188–95
    [Google Scholar]
  55. 55. 
    Fontana A, de Laureto PP, Spolaore B, Frare E 2012. Identifying disordered regions in proteins by limited proteolysis. Intrinsically Disordered Protein Analysis, Vol. 896 V Uversky, A Dunker 297–318 New York: Springer
    [Google Scholar]
  56. 56. 
    Hennig J, de Vries SJ, Hennig KDM, Randles L, Walters KJ et al. 2012. MTMDAT-HADDOCK: high-throughput, protein complex structure modeling based on limited proteolysis and mass spectrometry. BMC Struct. Biol. 12:29
    [Google Scholar]
  57. 57. 
    Proctor EA, Fee L, Tao Y, Redler RL, Fay JM et al. 2016. Nonnative SOD1 trimer is toxic to motor neurons in a model of amyotrophic lateral sclerosis. PNAS 113:614–19
    [Google Scholar]
  58. 58. 
    Mesleh M, Hunter J, Shvartsburg A, Schatz GC, Jarrold M. 1996. Structural information from ion mobility measurements: effects of the long-range potential. J. Phys. Chem. 100:16082–86
    [Google Scholar]
  59. 59. 
    Ewing SA, Donor MT, Wilson JW, Prell JS 2017. Collidoscope: an improved tool for computing collisional cross-sections with the trajectory method. J. Am. Soc. Mass Spectrom. 28:587–96
    [Google Scholar]
  60. 60. 
    Bleiholder C, Wyttenbach T, Bowers MT. 2011. A novel projection approximation algorithm for the fast and accurate computation of molecular collision cross sections (I). Method. Int. J. Mass Spectrom. 308:1–10
    [Google Scholar]
  61. 61. 
    Marklund EG, Degiacomi MT, Robinson CV, Baldwin AJ, Benesch JL. 2015. Collision cross sections for structural proteomics. Structure 23:791–99
    [Google Scholar]
  62. 62. 
    Bleiholder C, Liu FC. 2019. Structure relaxation approximation (SRA) for elucidation of protein structures from ion mobility measurements. J. Phys. Chem. B 123:2756–69
    [Google Scholar]
  63. 63. 
    Hall Z, Politis A, Robinson CV 2012. Structural modeling of heteromeric protein complexes from disassembly pathways and ion mobility-mass spectrometry. Structure 20:1596–609
    [Google Scholar]
  64. 64. 
    Eschweiler JD, Farrugia MA, Dixit SM, Hausinger RP, Ruotolo BT. 2018. A structural model of the urease activation complex derived from ion mobility-mass spectrometry and integrative modeling. Structure 26:599–606.e3
    [Google Scholar]
  65. 65. 
    Wang H, Eschweiler J, Cui W, Zhang H, Frieden C et al. 2019. Native mass spectrometry, ion mobility, electron-capture dissociation, and modeling provide structural information for gas-phase apolipoprotein E oligomers. J. Am. Soc. Mass Spectrom. 30:876–85
    [Google Scholar]
  66. 66. 
    Turzo SMBA, Seffernick JT, Rolland AD, Donor MT, Heinze S et al. 2021. Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction. bioRxiv 2021.05.27.445812. https://doi.org/10.1101/2021.05.27.445812
    [Crossref]
  67. 67. 
    Romano CA, Zhou M, Song Y, Wysocki VH, Dohnalkova AC et al. 2017. Biogenic manganese oxide nanoparticle formation by a multimeric multicopper oxidase Mnx. Nat. Commun. 8:746
    [Google Scholar]
  68. 68. 
    Song Y, Nelp MT, Bandarian V, Wysocki VH. 2015. Refining the structural model of a heterohexameric protein complex: Surface induced dissociation and ion mobility provide key connectivity and topology information. ACS Central Sci. 1:477–87
    [Google Scholar]
  69. 69. 
    Quintyn RS, Yan J, Wysocki VH 2015. Surface-induced dissociation of homotetramers with D2 symmetry yields their assembly pathways and characterizes the effect of ligand binding. Chem. Biol. 22:583–92
    [Google Scholar]
  70. 70. 
    Harvey SR, Seffernick JT, Quintyn RS, Song Y, Ju Y et al. 2019. Relative interfacial cleavage energetics of protein complexes revealed by surface collisions. PNAS 116:8143–48
    [Google Scholar]
  71. 71. 
    Seffernick JT, Harvey SR, Wysocki VH, Lindert S. 2019. Predicting protein complex structure from surface-induced dissociation mass spectrometry data. ACS Central Sci 5:1330–41
    [Google Scholar]
  72. 72. 
    Seffernick JT, Canfield SM, Harvey SR, Wysocki VH, Lindert S. 2021. Prediction of protein complex structure using surface-induced dissociation and cryo-EM. Anal. Chem. 93:7596–605
    [Google Scholar]
  73. 73. 
    Berman H, Henrick K, Nakamura H. 2003. Announcing the worldwide Protein Data Bank. Nat. Struct. Mol. Biol. 10:980
    [Google Scholar]
  74. 74. 
    Lawson CL, Patwardhan A, Baker ML, Hryc C, Garcia ES et al. 2016. EMDataBank unified data resource for 3DEM. Nucleic Acids Res. 44:D396–403
    [Google Scholar]
  75. 75. 
    Pancsa R, Varadi M, Tompa P, Vranken WF. 2016. Start2Fold: a database of hydrogen/deuterium exchange data on protein folding and stability. Nucleic Acids Res. 44:D429–34
    [Google Scholar]
  76. 76. 
    Bergendahl LT, Gerasimavicius L, Miles J, Macdonald L, Wells JN et al. 2019. The role of protein complexes in human genetic disease. Protein Sci. 28:1400–11
    [Google Scholar]
  77. 77. 
    Callaway E. 2020.. ‘ It will change everything’: DeepMind's AI makes gigantic leap in solving protein structures. Nature 588:203–4
    [Google Scholar]
  78. 78. 
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M et al. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596:583–89
    [Google Scholar]
  79. 79. 
    Service RF. 2020.. ‘ The game has changed.’ AI triumphs at protein folding. Science 370:1144–45
    [Google Scholar]
  80. 80. 
    Kleffner R, Flatten J, Leaver-Fay A, Baker D, Siegel JB et al. 2017. Foldit Standalone: a video game-derived protein structure manipulation interface using Rosetta. Bioinformatics 33:2765–67
    [Google Scholar]
/content/journals/10.1146/annurev-physchem-082720-123928
Loading
/content/journals/10.1146/annurev-physchem-082720-123928
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error