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Predicting the number of sulfur atoms in peptides and small proteins based on the observed aggregated isotope distribution
Author(s) -
Claesen Jürgen,
Valkenborg Dirk,
Burzykowski Tomasz
Publication year - 2021
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.9162
Subject(s) - monoisotopic mass , chemistry , mahalanobis distance , isotope , mass spectrometry , natural abundance , stable isotope ratio , analytical chemistry (journal) , chromatography , artificial intelligence , physics , quantum mechanics , computer science
Rationale Identification of peptides and proteins is a challenging task in mass spectrometry–based proteomics. Knowledge of the number of sulfur atoms can improve the identification of peptides and proteins. Methods In this article, we propose a method for the prediction of S‐atoms based on the aggregated isotope distribution. The Mahalanobis distance is used as dissimilarity measure to compare mass‐ and intensity‐based features from the observed and theoretical isotope distributions. Results The relative abundance of the second and the third aggregated isotopic variants (as compared to the monoisotopic one) and the mass difference between the second and third aggregated isotopic variants are the most important features to predict the number of S‐atoms. Conclusions The mass and intensity accuracies of the observed aggregated isotopic variants are insufficient to accurately predict the number of atoms. However, using a limited set of predictions for a peptide, rather than predicting a single number of S‐atoms, has a reasonably high prediction accuracy.