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Using dissociation energies to predict observability of b- and y-peaks in mass spectra of short peptides


  • This article is a U.S. Government work and is in the public domain in the U.S.A.

O. I. Obolensky and Y.-K. Yu, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.




Peptide identification reliability can be improved by excluding from analysis those m/z peaks of candidate peptides which cannot be observed in practice due to various physical, chemical or thermodynamic considerations. We propose using dissociation energies (as opposed to proton affinities) as a predictor of observability of different m/z peaks in spectra of short peptides.


Mass spectra of the tetrapeptides AAAA, AAFA, AAVA, AFAA, AVAA, AFFA, and AVVA were measured in the collision-induced dissociation (CID) activation mode on a grid of activation times 0.05 to 100 ms and normalized collision energy 10 to 35%. The lowest energy geometries and vibrational spectra were calculated for the precursor ions and their charged and neutral fragments using density functional theory (DFT) at the TPSS/6-31G(d,p) level. Dissociation energies were calculated for all fragmentation channels leading to b- or y-fragments.


It is demonstrated that m/z peaks observed in the mass spectra correspond to the fragmentation channels with the lowest dissociation energies. Using 50 kcal/mol as the cut-off value of dissociation energy, it was predicted that 28 out of 42 possible peaks in the b- and y-series of the seven tetrapeptides can be observed in mass spectra. In the experiments, 26 b- or y-peaks were observed, all of which are among the 28 predicted ones.


The use of dissociation energies generalizes the use of proton affinities for semi-quantitative predictions of relative intensities of different m/z peaks of short peptides. Further advances in this direction will pave the way for reliable quantitative predictions and, hence, for a significant improvement in robustness and accuracy of peptide and protein identification tools. Published in 2012 by John Wiley & Sons, Ltd.