A method for automatic analysis of high-resolution mass spectra was developed in ZIOC

Mass spectrometry is a convenient, highly sensitive and reliable method for the analysis of complex mixtures, which is of key importance for the making functional materials, the study of chemical reactions mechanisms, and the development of other areas of the life sciences. High-resolution electrospray ionization mass spectrometry is one of the most versatile methods of physical and chemical analysis today due to its excellent resolution and sensitivity. Modern instruments are capable of detecting compounds at low concentrations down to trace amounts. However, the main strengths of this method (sensitivity and resolution) significantly complicate the spectra, which significantly increases the time for their interpretation and requires high qualifications from the scientist.

Researchers from the Laboratory of Metal Complex and Nanoscale Catalysts of the ZIOC have proposed a Python software package for fully automated analysis of mass spectra. The method combines a new approach to deisotoping spectra and analyzing the fine isotopic structure of ions using neural networks to obtain molecular formulas. It should be especially noted that the proposed approach does not impose restrictions on the chemical elements present in the analyzed mixture. The developed method was successfully tested on three examples: the analysis of fragment ions for peptide sequencing, the study of natural objects, and the study of the catalytic system in the cross-coupling reaction. The developed software for mass spectrometric analysis is freely available and can be used by anyone in their research.

The results of the study were published in one of the most prestigious journals in the field of chemistry, the Journal of the American Chemical Society.

Source:

Daniil A. Boiko, Konstantin S. Kozlov, Julia V. Burykina, Valentina V. Ilyushenkova, Valentine P. Ananikov Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning J. Am. Chem. Soc. 2022, 144, 14590−14606. DOI: 10.1021/jacs.2c03631.