TY - JOUR
T1 - Machine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution
AU - Agten, Annelies
AU - Claesen, Jurgen
AU - Burzykowski, Tomasz
AU - Valkenborg, Dirk
N1 - Funding Information: The authors gratefully acknowledge funding from FWO‐PAS grant VS02819N entitled “Computational methods for high‐resolution mass spectrometry data and massive parallel sequencing.” D.V. acknowledges funding from Flanders AI research. Funding Information: The authors thank Geert Baggerman and Daniel Flender for providing the experimental data. The authors gratefully acknowledge funding from FWO-PAS grant VS02819N entitled “Computational methods for high-resolution mass spectrometry data and massive parallel sequencing.” D.V. acknowledges funding from Flanders AI research. Publisher Copyright: © 2023 John Wiley & Sons Ltd.
PY - 2023/3/15
Y1 - 2023/3/15
N2 - 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 for future instrument developments to focus more on improving spectral accuracy to measure peak intensities of higher-order isotope peaks more accurately.
AB - 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 for future instrument developments to focus more on improving spectral accuracy to measure peak intensities of higher-order isotope peaks more accurately.
UR - http://www.scopus.com/inward/record.url?scp=85151542260&partnerID=8YFLogxK
U2 - https://doi.org/10.1002/rcm.9480
DO - https://doi.org/10.1002/rcm.9480
M3 - Article
C2 - 36798055
SN - 0951-4198
VL - 37
JO - Rapid communications in mass spectrometry
JF - Rapid communications in mass spectrometry
IS - 9
M1 - e9480
ER -