Author Archives: Vera

Peptide metabolism: High resolution Mass Spectrometry tool to investigate Peptide structure and amine bond metabolic susceptibility

American Peptide Symposium, Whistler (Canada)… 17 June 2017 

Abstract

Several in-silico approaches have been developed such as PeptideCutter to predict peptide cleavage sites for different proteases. Moreover, several databases exist where this information is collected and stored such as MEROPS. Despite these new methodologies there are still some limitations in their usage: inability to handle unnatural amino acids and cyclic peptides. The aim of this work is to develop a new methodology to analyze the mass spectrometry driven experimental data to find those metabolites, then determine their structures, database all the results in a chemistry aware manner and finally to compute the peptide bond susceptibility by using a frequency analysis of the metabolic liability.

This approach uses ultra-performance liquid chromatography with high resolution mass spectrometry to obtain the analytical data from incubations of peptides with different enzyme matrices. Metabolite identification was performed on 13 commercial peptide compounds and 4 positive substrates for the four selected proteases (serine and aspartic). The peptides were incubated for three hours with five time points being taken during the experiment. The compounds were diverse with respect to linear and cyclic structure, containing natural and unnatural amino acids and ranged in molecular weight.

The analysis of this data set resulted in 45 metabolites that were annotated in the database. The frequency analysis revealed 26 site of cleavage and the Trp-Ser being the most frequently cleaved bond for all cases. Selectivity was identified for pancreatic elastase and trypsin/chymotrypsin because the Ser-Tyr and Leu-Ser were revealed as a most frequently cleaved bond, respectively. These results agreed with previous studies. 

Peptide catabolite identification using HDMSE data and MassMetaSite processing

66th ASMS Conference on Mass Spectrometry and Allied Topics, San Diego (United States of America) … 06 June 2018 

Abstract

The study of peptide metabolism is needed to understand the structural elements that contribute to their clearance. Most of the MS techniques available today in peptide Metabolite Identification are based on DDA methods, often with a pre-defined list of ions. This methodology has the limitation that good quality MS 2 spectra are obtained for known metabolites, but possibility missing the unknown ones. Moreover, when the number of possible metabolites is high the preferred list would be too large to be effectively used. DIA methods have less potential to miss metabolites but do not have formally quad-resolved product ion spectra. In this study we report the comparison of DDA and DIA methods – MS Eand ion mobility-MS method (HDMS E) using Mass-MetaSite/WebMetabase processing.

Software assisted analysis for Peptide Catabolism

Institute for Research in Biomedicine (IRB Barcelona) 35 EPS European Peptide Symposium, Dublin (Ireland) 

Anna Escolà (1,2), Antoni Riera (1,2), Aurora Valeri (3), Ismael Zamora (4,5), Tatiana Radchenko (4,5) 1. 

Abstract

The interest in using peptide molecules as therapeutic agents is due to their high selectivity and efficacy. However, most peptide-derived drugs cannot be administered orally because of their instability in the gastrointestinal tract. To achieve better ADME properties the following chemical modifications are typically applied: substitution of the common L-amino acids to D-amino acids, cyclization of the peptide and others. Somatostatin or Somatotropin release-inhibiting factor (SRIF14) is a natural hormone that is being used as gastric anti-secretory drug as well as to treat growth hormone secretion disorders and endocrine tumors. The substitution of phenylalanine, using non-natural aromatic amino acids to enhance the aromatic interactions, naturally present in the hormone between Phe6, Phe7 and Phe11 has been studied before. 

We used a new approach implemented in MassMetaSite to process data-dependent acquisition (DDA) liquid chromatography high-resolution mass spectrometry (LC-HRMS) analytical data. This data was collected for a set of eight peptide drugs (somatostatin and seven synthetic analogues, containing non-standard amino acids) incubated with human serum. The samples obtained were used to perform metabolite identification, to reveal potential cleavage sites and to store the processed information in a searchable format within a database (WMB). During the metabolite identification study in total 17 metabolites were found resulting in 8 distinct cleavage sites. We compared the percent of remaining parent peptide with respect to the time for all investigated peptides to compute the half-life for each case. Moreover, we evaluated the influence of the chemical modifications on the half-life time of the investigated compounds comparing to the value obtained for somatostatin. All compounds from the dataset were hydrolyzed with the different velocity. More stable compounds were the compounds where following replacements were done both Phe7 to Msa7, Trp8 to D-Trp8 and/or both Cys3 and Cys14 to D-Cys. 

We demonstrate that the developed approach can elucidate metabolite structure of cyclic peptides and those containing unnatural amino acids. The processed information obtained could be stored in a searchable format within a database leading to frequency analysis of the labile sites for the analyzed peptides. This new algorithm may be useful to optimize peptide drug properties with regards to cleavage sites, stability, metabolism, and degradation products in drug discovery. 

Software-aided approach to investigate peptide structure and metabolic susceptibility of amide bonds in peptide drugs based on high resolution mass spectrometry

November 2017

Radchenko T; Brink A; Siegrist Y; Kochansky C; Bateman A; Fontaine F; Morettoni L; Zamora I

Abstract

WebMetabase: cleavage sites analysis tool for natural and unnatural substrates from diverse data source

February 2019.

Radchenko T; Fontaine F; Morettoni L; Zamora I

Abstract