Fragmentation Spectra Prediction and DNA Adducts Structural Determination

Andrea Carrà, Veronica Macaluso, Peter W. Villalta, Riccardo Spezia, Silvia Balbo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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. [Figure not available: see fulltext.].

Original languageEnglish (US)
Pages (from-to)2771-2784
Number of pages14
JournalJournal of the American Society for Mass Spectrometry
Volume30
Issue number12
DOIs
StatePublished - Dec 1 2019

Bibliographical note

Funding Information:
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.

Keywords

  • Adductomics
  • Collision-induced dissociation
  • Fragmentation prediction
  • Mobile proton model

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