Collision‐induced thermochemistry of reactions of dissociation of glycyl–homopeptides—An experimental and theoretical analysis

Abstract

The research draws on experimental and theoretical data about energetics and kinetics of mass spectrometric (MS) reactions of glycyl homopenta- (G5) and glycyl homohexapeptides (G6). It shows the great applicability of the methods of quantum chemistry to predict MS profile of peptides using energetics of collision induced dissociation (CID) fragment species. Mass spectrometry is among irreplaceable methods, providing unambiguous qualitative, quantitative and structural information about analytes, applicable to many scientific areas like environmental chemistry; food chemistry; medicinal chemistry; and more. Our study could be considered of substantial interdisciplinary significance, where MS proteomics is widely used. The experimental design involves electrospray ionization (ESI) and CID MS/MS. Theoretical design is based on ab initio and density functional theory (DFT) methods. Experimental MS and theoretical free Gibbs energies as well as rate constants of fragment reactions are compared. The thermodynamic encompasses gas-phase and polar continuum analysis, including polar protic and aprotic solvents within temperature T = 10-500 K; dielectric constant ε = 0-78, pH, and ionic strengths μ = 0.001-1.0 mol dm . There are computed and discussed 39 protonated forms of peptides at amide N- and -(NHC)=O centers; corresponding fragment ions studying their thermodynamic stability depending on experimental conditions. A correlation analysis between molecular conformations of parent ions and fragment species; their proton accepting ability and internal energy distribution is carried out. Data about ionization potentials (IPs) and electron affinities (EAs) are discussed, as well.

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.…”

“…To make a comprehensive assessment, we conducted a literature search to find MS/MS spectrum prediction methods. The search was not limited to a specific publication time period, but we selected only methods that (1) utilize deep learning, (2) can be applied to a wide range of data obtained using various instruments, and (3) accept as input only peptide sequence (possibly with some post-translational modifications, PTMs) and charge and optionally MS experiment-related parameters such as collision energy. Based on these criteria, in this study, we included six MS/MS spectrum prediction methods, namely AlphaPeptDeep 16 (PeptDeep for short), Prosit Transformer, 17 DeepMass:Prism 18 (Prism for short), pDeep3, 19 Prosit, 12 and the method proposed by Guan et al 14 On the other hand, we excluded MS/MSCNN 20 and the latter is recommended to be retrained for use in each lab, i.e.…”