Advertisement

Fragmentation Spectra Prediction and DNA Adducts Structural Determination

  • Andrea Carrà
  • Veronica Macaluso
  • Peter W. Villalta
  • Riccardo SpeziaEmail author
  • Silvia BalboEmail author
Research Article

Abstract

In this work, chemical dynamics simulations were optimized and used to predict fragmentation mass spectra for DNA adduct structural determination. O6-methylguanine (O6-Me-G) was used as a simple model adduct to calculate theoretical spectra for comparison with measured high-resolution fragmentation data. An automatic protocol was established to consider the different tautomers accessible at a given energy and obtain final theoretical spectra by insertion of an initial tautomer. In the work reported here, the most stable tautomer was chosen as the initial structure, but in general, any structure could be considered. Allowing for the formation of the various possible tautomers during simulation calculations was found to be important to getting a more complete fragmentation spectrum. The calculated theoretical results reproduce the experimental peaks such that it was possible to determine reaction pathways and product structures. The calculated tautomerization network was crucial to correctly identifying all the observed ion peaks, showing that a mobile proton model holds not only for peptide fragmentation but also for nucleobases. Finally, first principles results were compared to simple machine learning fragmentation models.

Keywords

Adductomics Collision-induced dissociation Fragmentation prediction Mobile proton model 

Notes

Acknowledgements

We thank ANR DynBioReact (Grant No. ANR-14-CE06-0029-01) and ASMS award for financial support. A.C. was partially supported by the 2017 American Society for Mass Spectrometry Postdoctoral Career Development Award. Mass spectrometry was carried out in the Analytical Biochemistry Shared Resource of the Masonic Cancer Center, University of Minnesota, funded in part by Cancer Center Support Grant CA-077598.

Supplementary material

13361_2019_2348_MOESM1_ESM.pdf (279 kb)
ESM 1 (PDF 278 kb)

References

  1. 1.
    Roukos, D.H.: Genome-wide association studies: how predictable is a person’s cancer risk? Expert. Rev. Anticancer. Ther. 9, 389–392 (2009)PubMedCrossRefPubMedCentralGoogle Scholar
  2. 2.
    Balbo, S., Hecht, S.S., Upadhyaya, P., Villalta, P.W.: Application of a high-resolution mass-spectrometry-based dna adductomics approach for identification of dna adducts in complex mixtures. Anal. Chem. 86, 1744–1752 (2014)PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Balbo, S., Turesky, R.J., Villalta, P.W.: DNA Adductomics. Chem. Res. Toxicol. 27, 356–366 (2014)PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Song, K., Spezia, R.: Theoretical Mass Spectrometry. De Gruyter, Berlin (2018)CrossRefGoogle Scholar
  5. 5.
    de Sainte Claire, P., Hase, W.L.: Thresholds for the collision-induced dissociation of clusters by rare gas impact. J. Phys. Chem. 100, 8190–8196 (1996)CrossRefGoogle Scholar
  6. 6.
    Liu, J., Song, K., Hase, W.L., Anderson, S.L.: Direct dynamics study of energy transfer and collision-induced dissociation: effects of impact energy, geometry, and reactant vibrational mode in H2CO+–Ne collisions. J. Chem. Phys. 119, 3040–3050 (2003)CrossRefGoogle Scholar
  7. 7.
    Martínez-Núñez, E., Fernández-Ramos, A., Vázquez, S.A., Marques, J.M.C., Xue, M., Hase, W.L.: Quasiclassical dynamics simulation of the collision-induced dissociation of Cr(CO)6 + with Xe. J. Chem. Phys. 123(15), 154311 (2005)PubMedCrossRefPubMedCentralGoogle Scholar
  8. 8.
    Meroueh, S.O., Wang, Y., Hase, W.L.: Direct dynamics simulations of collision- and surface-induced dissociation of n-protonated glycine. Shattering fragmentation. J. Phys. Chem. A. 106, 9983–9992 (2002)CrossRefGoogle Scholar
  9. 9.
    Spezia, R., Salpin, J.-Y., Gaigeot, M.-P., Hase, W.L., Song, K.: Protonated urea collision-induced dissociation. comparison of experiments and chemical dynamics simulations. J. Phys. Chem. A. 113, 13853–13862 (2009)Google Scholar
  10. 10.
    Spezia, R., Martens, J., Oomens, J., Song, K.: Collision-induced dissociation pathways of protonated Gly2NH2 and Gly3NH2 in the short time-scale limit by chemical dynamics and ion spectroscopy. Int. J. Mass Spectrom. 388, 40–52 (2015)CrossRefGoogle Scholar
  11. 11.
    Spezia, R., Lee, S.B., Cho, A., Song, K.: Collision-induced dissociation mechanisms of protonated penta- and octa-glycine as revealed by chemical dynamics simulations. Int. J. Mass Spectrom. 392, 125–138 (2015)CrossRefGoogle Scholar
  12. 12.
    Rossich Molina, E., Eizaguirre, A., Haldys, V., Urban, D., Doisneau, G., Bourdreux, Y., Beau, J.-M., Salpin, J.-Y., Spezia, R.: Characterization of protonated model disaccharides from tandem mass spectrometry and chemical dynamics simulations. ChemPhysChem. 18, 2812–2823 (2017)PubMedCrossRefPubMedCentralGoogle Scholar
  13. 13.
    Ortiz, D., Salpin, J.-Y., Song, K., Spezia, R.: Galactose-6-sulfate collision induced dissociation using QM+MM chemical dynamics simulations and ESI-MS/MS experiments. Int. J. Mass Spectrom. 358, 25–35 (2014)CrossRefGoogle Scholar
  14. 14.
    Rossich Molina, E., Ortiz, D., Salpin, J.-Y., Spezia, R.: Elucidating collision induced dissociation products and reaction mechanisms of protonated uracil by coupling chemical dynamics simulations with tandem mass spectrometry experiments. J. Mass Spectrom. 50, 1340–1351 (2015)PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Rodriguez-Fernandez, R., Vazquez, S.A., Martinez-Nunez, E.: Collision-induced dissociation mechanisms of [Li(uracil)]+. Phys. Chem. Chem. Phys. 15, 7628–7637 (2013)PubMedCrossRefPubMedCentralGoogle Scholar
  16. 16.
    Spezia, R., Martin-Somer, A., Macaluso, V., Homayoon, Z., Pratihar, S., Hase, W.L.: Unimolecular dissociation of peptides: statistical vs. non-statistical fragmentation mechanisms and time scales. Faraday Discuss. 195, 599–618 (2016)PubMedCrossRefPubMedCentralGoogle Scholar
  17. 17.
    Homayoon, Z., Pratihar, S., Dratz, E., Snider, R., Spezia, R., Barnes, G.L., Macaluso, V., Martin-Somer, A., Hase, W.L.: Model simulations of the thermal dissociation of the TIK(H+)2 tripeptide. Mechanisms and kinetic parameters. J. Phys. Chem. A. 120, 8211–8227 (2016)PubMedCrossRefPubMedCentralGoogle Scholar
  18. 18.
    Martin-Somer, A., Martens, J., Grzetic, J., Hase, W.L., Oomens, J., Spezia, R.: Unimolecular fragmentation of deprotonated diproline [Pro2-H]- studied by chemical dynamics simulations and IRMPD spectroscopy. J. Phys. Chem. A. 122, 2612–2625 (2018)PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    Macaluso, V., Scuderi, D., Crestoni, M.E., Fornarini, S., Corinti, D., Dalloz, E., Martinez-Nunez, E., Hase, W.L., Spezia, R.: L-Cysteine modified by S-sulfation: consequence on fragmentation processes elucidated by tandem mass spectrometry and chemical dynamics simulations. J. Phys. Chem. A. 123, 3685–3696 (2019)PubMedCrossRefGoogle Scholar
  20. 20.
    Park, K., Deb, B., Song, K., Hase, W.L.: Importance of shattering fragmentation in the surface-induced dissociation of protonated octaglycine. J. Am. Soc. Mass Spectrom. 20, 939–948 (2009)PubMedCrossRefPubMedCentralGoogle Scholar
  21. 21.
    Barnes, G.L., Hase, W.L.: Energy transfer, unfolding, and fragmentation dynamics in collisions of N-protonated octaglycine with an H-SAM surface. J. Am. Chem. Soc. 131, 17185–17,193 (2009)PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Gregg, Z., Ijaz, W., Jannetti, S., Barnes, G.L.: The role of proton transfer in surface-induced dissociation. J. Phys. Chem. C. 118(22), 149–22,155 (2014)Google Scholar
  23. 23.
    Bauer, C.A., Grimme, S.: Elucidation of electron ionization induced fragmentations of adenine by semiempirical and density functional molecular dynamics. J. Phys. Chem. A. 118(11), 479–11,484 (2014)Google Scholar
  24. 24.
    Bauer, C.A., Grimme, S.: How to compute electron ionization mass spectra from first principles. J. Phys. Chem. A. 120, 3755–3766 (2016)PubMedCrossRefPubMedCentralGoogle Scholar
  25. 25.
    Asgeirsson, V., Bauer, C.A., Grimme, S.: Quantum chemical calculation of electron ionization mass spectra for general organic and inorganic molecules. Chem. Sci. 8, 4879–4895 (2017)PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Hecht, S.S.: DNA adduct formation from tobacco-specific N-nitrosamines. Mut. Res.-Fund. Mol. M. 424, 127–142 (1999)CrossRefGoogle Scholar
  27. 27.
    Peterson, L.A., Hecht, S.S.: O6-Methylguanine is a critical determinant of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone tumorigenesis in A/J mouse lung. Cancer Res. 51, 5557–5564 (1991)PubMedPubMedCentralGoogle Scholar
  28. 28.
    Becke, A.D.: A new mixing of Hartree–Fock and local density-functional theories. J. Chem. Phys. 98, 1372–1377 (1993)CrossRefGoogle Scholar
  29. 29.
    Lee, C., Yang, W., Parr, R.G.: Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B. 37, 785–789 (1988)CrossRefGoogle Scholar
  30. 30.
    Dewar, M.J.S., Zoebisch, E.G., Healy, E.F., Stewart, J.J.P.: Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc. 107, 3902–3909 (1985)CrossRefGoogle Scholar
  31. 31.
    McNamara, J.P., Hillier, I.H.: Semi-empirical molecular orbital methods including dispersion corrections for the accurate prediction of the full range of intermolecular interactions in biomolecules. Phys. Chem. Chem. Phys. 9, 2362–2370 (2007)PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    Rocha, G.B., Freire, R.O., Simas, A.M., Stewart, J.J.P.: RM1: A reparameterization of AM1 for H, C, N, O, P, S, F, Cl, Br, and I. J. Comput. Chem. 27, 1101–1111 (2006)PubMedCrossRefPubMedCentralGoogle Scholar
  33. 33.
    Stewart, J.J.P.: Optimization of parameters for semiempirical methods I. Method. J. Comput. Chem. 10, 209–220 (1989)CrossRefGoogle Scholar
  34. 34.
    Stewart, J.J.P.: Optimization of parameters for semiempirical methods V: modification of NDDO approximations and application to 70 elements. J. Mol. Model. 13, 1173–1213 (2007)PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Grimme, S.: Accurate description of van der Waals complexes by density functional theory including empirical corrections. J. Comput. Chem. 25, 1463–1473 (2004)PubMedCrossRefPubMedCentralGoogle Scholar
  36. 36.
    Frisch, M.J., Trucks, G.W., Schlegel, H.B., Scuseria, G.E., Robb, M.A., Cheeseman, J.R., Scalmani, G., Barone, V., Mennucci, B., Petersson, G.A., Nakatsuji, H., Caricato, M., Li, X., Hratchian, H.P., Izmaylov, A.F., Bloino, J., Zheng, G., Sonnenberg, J.L., Hada, M., Ehara, M., Toyota, K., Fukuda, R., Hasegawa, J., Ishida, M., Nakajima, T., Honda, Y., Kitao, O., Nakai, H., Vreven, T., Montgomery Jr., J.A., Peralta, J.E., Ogliaro, F., Bearpark, M., Heyd, J.J., Brothers, E., Kudin, K.N., Staroverov, V.N., Kobayashi, R., Normand, J., Raghavachari, K., Rendell, A., Burant, J.C., Iyengar, S.S., Tomasi, J., Cossi, M., Rega, N., Millam, J.M., Klene, M., Knox, J.E., Cross, J.B., Bakken, V., Adamo, C., Jaramillo, J., Gomperts, R., Stratmann, R.E., Yazyev, O., Austin, A.J., Cammi, R., Pomelli, C., Ochterski, J.W., Martin, R.L., Morokuma, K., Zakrzewski, V.G., Voth, G.A., Salvador, P., Dannenberg, J.J., Dapprich, S., Daniels, A.D., Farkas, Ö., Foresman, J.B., Ortiz, J.V., Cioslowski, J., Fox, D.J.: Gaussian 09, Revision D.01. Gaussian, Inc., Wallingford (2009)Google Scholar
  37. 37.
    Stewart, J.J.P., Fiedler, L.J., Zhang, P., Zheng, J., Rossi, I., Hu, W.-P., Lynch, G.C., Liu, Y.-P., Chuang, Y.-Y., Pu, J., Li, J., Cramer, C.J., Fast, P.L., Truhlar, D.G.: MOPAC 5.022mn. Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis (2015)Google Scholar
  38. 38.
    Hase, W.L., Duchovic, R.J., Hu, X., Komornicki, A., Lim, K.F., Lu, D.-H., Peslherbe, G.H., Swamy, K.N., Vande Linde, S.R., Zhu, L., Varandas, A., Wang, H., Wolf, R.: VENUS96: A general chemical dynamics computer program. QCPE Bull. 16, 43 (1996)Google Scholar
  39. 39.
    Hase, W.L., Buckowski, D.G.: Monte carlo sampling of a microcanonical ensemble of classical harmonic oscillators. Chem. Phys. Lett. 74, 284–287 (1980)CrossRefGoogle Scholar
  40. 40.
    Schlier, C., Seiter, A.: Symplectic integration of classical trajectories: a case study. J. Phys. Chem. A. 102, 9399–9404 (1998)CrossRefGoogle Scholar
  41. 41.
    Schlier, C., Seiter, A.: High-order symplectic integration: an assessment. Comput. Phys. Commun. 130, 176–189 (2000)CrossRefGoogle Scholar
  42. 42.
    Jeanvoine, Y., Largo, A., Hase, W.L., Spezia, R.: gas phase synthesis of protonated glycine by chemical dynamics simulations. J. Phys. Chem. A. 122, 869–877 (2018)Google Scholar
  43. 43.
    Carpenter, J.E., Weinhold, F.: Analysis of the geometry of the hydroxymethyl radical by the different hybrids for different spins natural bond orbital procedure. J. Mol. Struct. (THEOCHEM). 139, 41–62 (1988)CrossRefGoogle Scholar
  44. 44.
    Jeanvoine, Y., Spezia, R.: The formation of urea in space. II. MP2 vs PM6 dynamics in determining bimolecular reaction products. Theor. Chem. Accounts. 138, 1 (2019)CrossRefGoogle Scholar
  45. 45.
    Dongré, A.R., Jones, J.L., Somogyi, Á., Wysocki, V.H.: Influence of peptide composition, gas-phase basicity, and chemical modification on fragmentation efficiency: an evidence for the mobile proton model. J. Am. Chem. Soc. 118, 8365–8374 (1996)CrossRefGoogle Scholar
  46. 46.
    Allen, F., Greiner, R., Wishart, D.: Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics. 11, 98–110 (2015)CrossRefGoogle Scholar
  47. 47.
    Allen, F., Pon, A., Wilson, M., Greiner, R., Wishart, D.: CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Nucleic Acids Res. 42, W94–W99 (2014)PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Kempkes, L.J.M., Martens, J., Berden, G., Houthuijs, K.J., Oomens, J.: Investigation of the position of the radical in z3-ions resulting from electron transfer dissociation using infrared ion spectroscopy. Faraday Discuss. 217, 434–452 (2019)PubMedCrossRefPubMedCentralGoogle Scholar
  49. 49.
    Fu, W., Hopkins, W.S.: Applying machine learning to vibrational spectroscopy. J. Phys. Chem. A. 122, 167–171 (2018)PubMedCrossRefPubMedCentralGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2019

Authors and Affiliations

  1. 1.Masonic Cancer CenterUniversity of MinnesotaMinneapolisUSA
  2. 2.Laboratoire de Chimie Théorique, LCT, CNRSSorbonne UniversitéParisFrance
  3. 3.Laboratoire Analyse et Modélisation pour la Biologie et l’Environnement, Université d’Evry, CEA, CNRSUniversité Paris SaclayEvry CedexFrance

Personalised recommendations