Using dissociation energies to predict observability of b‐ and y‐peaks in mass spectra of short peptides. II. Results for hexapeptides with non‐polar side chains

Abstract

RATIONALE The hypothesis that dissociation energies can serve as a predictor of observability of b- and y-peaks is tested for seven hexapeptides. If the hypothesis holds true for large classes of peptides, one would be able to improve the scoring accuracy of peptide identification tools by excluding theoretical peaks that cannot be observed in practical product ion spectra due to various physical, chemical or thermodynamic considerations. METHODS Product ion m/z spectra of hexapeptides AAAAAA, AAAFAA, AAAVAA, AAFAAA, AAVAAA, AAFFAA and AAVVAA have been acquired on a Finnigan LTQ XL mass spectrometer 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%. Dissociation energies were calculated for all fragmentation channels leading to b- and y-fragments at the TPSS/6–31G(d,p) level of the density functional theory. RESULTS It was demonstrated that the m/z peaks observed in the product ion spectra correspond to the fragmentation channels with dissociation energies below a certain threshold value. However, there is no direct correlation between the most intense m/z peaks and the lowest dissociation energies. Using the dissociation energies, it was predicted that out of 63 theoretically possible peaks in the b- and y-series of the seven hexapeptides, 19 should not be observable in practical spectra. In the experiments, 24 peaks were not observed, including all 19 predicted. CONCLUSIONS Dissociation energies alone are not sufficient for predicting ion intensity relationships in product ion m/z spectra. Nevertheless, the present data suggest that dissociation energies appear to be good predictors of observability of b- and y-peaks and potentially very useful for filtering theoretical peaks of each candidate peptide in peptide identification tools.

Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles

“…The sensitivity and specificity of mass spectrometry-based peptide, protein, and microorganism identification can potentially be aided by employing reliable data for peptide fragmentation profiles. These profiles can be made available not only through physical modeling of peptide fragmentation process − or through building of extensive spectral libraries, − but also by employing deep learning algorithms to predict fragmentation profiles for each precursor on-the-fly. This can be especially useful for data-independent acquisition (DIA) proteomics.…”

Size Dependent Fragmentation Chemistry of Short Doubly Protonated Tryptic Peptides

“…Computational chemistry using density function theory (DFT) to rationalize and predict peptide fragmentation was pioneered by several groups; − much of this early work concerned small singly protonated systems. Later analyses of larger and more highly charged systems followed. ,− Combined guided ion beam MS experimental studies together with theory , have supplemented these data for singly protonated aliphatic di- and tripeptide systems. Single-system, doubly protonated peptide calculations provided evidence that amide bond cleavage barriers vary with position with a balance of charge–solvation and charge–charge repulsion being set. ,, Paizs and coauthors provided evidence from calculations, infrared multiple photon dissociation (IRMPD) spectroscopy, and hydrogen/deuterium exchange experiments which indicated that the b 2 fragments generated from a series of class I tryptic doubly protonated peptides of varying composition generated oxazolone b 2 ion structures. ,,− Other authors provided evidence that the b 2 ion structure could vary with peptide composition. − Haeffner and Irikura developed a threshold peak abundance prediction model based on the relative energies of the various amide nitrogen protonation sites of [A 8 R+2H] 2+ .…”