Machine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution

Abstract

Rationale:The observed isotope distribution is an important attribute for the identification of peptides and proteins in mass spectrometry-based proteomics.Sulphur atoms have a very distinctive elemental isotope definition, and therefore, the presence of sulphur atoms has a substantial effect on the isotope distribution of biomolecules. Hence, knowledge of the number of sulphur atoms can improve the identification of peptides and proteins.Methods: In this paper, we conducted a theoretical investigation on the isotope properties of sulphur-containing peptides. We proposed a gradient boosting approach to predict the number of sulphur atoms based on the aggregated isotope distribution. We compared prediction accuracy and assessed the predictive power of the features using the mass and isotope abundance information from the first three, five and eight aggregated isotope peaks.Results: Mass features alone are not sufficient to accurately predict the number of sulphur atoms. However, we reach near-perfect prediction when we include isotope abundance features. The abundance ratios of the eighth and the seventh, the fifth and the fourth, and the third and the second aggregated isotope peaks are the most important abundance features. The mass difference between the eighth, the fifth or the third aggregated isotope peaks and the monoisotopic peak are the most predictive mass features.Conclusions: Based on the validation analysis it can be concluded that the prediction of the number of sulphur atoms based on the isotope profile fails, because the isotope ratios are not measured accurately. These results indicate that it is valuable

A compositional data model to predict the isotope distribution for average peptides using a compositional spline model

“…However, knowledge on the number of sulphur atoms in a peptide is required to apply the correct model since we propose different models for each sulphur category, respectively. Recently, a machine learning approach was proposed to predict the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution [14]. However, this method uses information on the masses and isotope probabilities of the observed isotope distribution.…”