Improving metabolite identification for complex peptides using MassMetaSite

70th ASMS Conference on Mass Spectrometry. June 2022

Ismael Zamora; Tatiana Radchenko; Fabien Fontaine; Albert Garriga

Lead Molecular Design, S.L., Sant Cugat del Valles, Spain



A commonly used strategy in the peptide therapeutics field to introduce chemical modifications such as cyclisation, changing the stereochemistry of an amino acid, substitution of natural amino acids to chemically modified ones and others to improve their efficacy and ADME profile. The study of the metabolic degradation products for synthetically modified therapeutic peptides using LC-MS is a challenging issue. Different chemoinformatics approaches are used for automated metabolite identification. These tools propose metabolite structures based on the combination of metabolite prediction and analysis of MS data. This makes building the virtual set of all potential metabolites time and computational intensive. Fragmentation analysis requires even more computation time. Finally, a third challenge related to the depiction of the parent and the metabolites.


New algorithms that address the challenges in highly modified therapeutic peptide structure elucidation have been developed. The peak detection algorithm was improved to use the Most Abundant Isotope for parent and potential metabolites. A new algorithm is producing all virtual metabolites applying the library of chemical reactions to each monomer while maintaining the connection to the atoms. A third improvement is the fragmentation algorithm that has two layers of analysis, one at the monomer level and the other one at the bond level. Finally, we will also show the results of the implemented algorithm for the analysis results visualization.

Preliminary data

Using MassMetaSite we analyzed a collection of experimental data for a set of peptides where the data was collected on a Q-Exactive Thermo instrument. The metabolite identification study was performed using a peptide set that included eight compounds: somatostatin and its seven synthetic analogues. All test compounds were incubated in serum. These peptides are all cyclic peptides and seven of them had unnatural amino acids. The structural assignments were performed for 17 degradation products with high mass accuracy (ppm<3). Most of the metabolites resulting from one or two metabolic modifications were produced by amide hydrolysis and were detected by the new algorithm. Each of these compounds showed a suitable fragmentation pattern that could be compared to the parent by the fragmentation algorithm to assign the shifted and non-shifted fragments. All the metabolites were checked manually including a review of the assigned fragments. Metabolites were considered as reliable because the fragmentation was adequate, isotope pattern was as expected, the small differences between the m/z of observed and theoretical, and the mass score was high. As previously reported in the literature, the first two most abundant metabolites with assigned structure correspond to cleavage of the linear part of the somatostatin. Finally, we analyzed experimental data for the insulin and semaglutide data where the data was collected on a Waters QToF. Insulin is a cyclic peptide that contains 3 disulfide bridges and semaglutide is a linear peptide that contains linkages and fatty acid. The visualization algorithm developed allows to show results for these complex structures in a such a manner that it is easy to interpret due to the constraint structure alignment between the substrate and the metabolite (keeping same orientation), a possibility to combine monomer and atom/bond notation. Therefore, the metabolic changes in the structure can be easily seen by the User.


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