Peptide catabolite identification using HDMSE data and MassMetaSite processing

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

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

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

Interest in using peptide molecules as therapeutic agents due to high selectivity and efficacy is increasing within the pharmaceutical industry. However, most peptide-derived drugs cannot be administered orally because of low bioavailability and instability in the gastrointestinal tract due to protease activity. Therefore, structural modifications peptides are required to improve their stability. For this purpose, several in-silico software tools have been developed such as PeptideCutter or PoPS, which aim to predict peptide cleavage sites for different proteases. Moreover, several databases exist where this information is collected and stored from public sources such as MEROPS and ExPASy ENZYME databases. These tools can help design a peptide drug with increased stability against proteolysis, though they are limited to natural amino acids or cannot process cyclic peptides, for example.

We worked to develop a new methodology to analyze peptide structure and amide bond metabolic stability based on the peptide structure (linear/cyclic, natural/unnatural amino acids). This approach used liquid chromatography / high resolution, mass spectrometry to obtain the analytical data from in vitro incubations. We collected experimental data for a set (linear/cyclic, natural/unnatural amino acids) of fourteen peptide drugs and four substrate peptides incubated with different proteolytic media: trypsin, chymotrypsin, pepsin, pancreatic elastase, dipeptidyl peptidase-4 and neprilysin. Mass spectrometry data was analyzed to find metabolites and determine their structures, then all the results were stored in a chemically aware manner, which allows us to compute the peptide bond susceptibility by using a frequency analysis of the metabolic-liable bonds. In total 132 metabolites were found from the various in vitro conditions tested resulting in 77 distinct cleavage sites. The most frequent observed cleavage sites agreed with those reported in the literature. The main advantages of the developed approach are the abilities to elucidate metabolite structure of cyclic peptides and those containing unnatural amino acids, store processed information in a searchable format within a database leading to frequency analysis of the labile sites for the analyzed peptides. The presented algorithm may be useful to optimize peptide drug properties with regards to cleavage sites, stability, metabolism and degradation products in drug discovery. 

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

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

More than 150 peptide therapeutics are globally in clinical development. Many enzymatic barriers should be crossed by a successful drug to be prosperous in such a process. Therefore, the new peptide drugs must be designed preventing the potential protease cleavage to make the compound less susceptible to protease reaction. We present a new data analysis tool developed in WebMetabase, an approach that stores the information from liquid chromatography mass spectrometry-based experimental data or from external sources such as the MEROPS database. The tool is a chemically aware system where each peptide substrate is presented as a sequence of structural blocks (SBs) connected by amide bonds and not being limited to the natural amino acids. Each SB is characterized by its pharmacophoric and physicochemical properties including a similarity score that describes likelihood between a SB and each one of the other SBs in the database. This methodology can be used to perform a frequency analysis to discover the most frequent cleavage sites for similar amide bonds, defined based on the similarity of the SB that participate in such a bond within the experimentally derived and/or public database. 

Biomarkers discovery and beyond: the trend analysis

Solutions for OMICS

Since the initial version of Lipostar, there have been significant strides to add features in collaboration with users to enable further data analysis in ways that other software platforms seldom provide. Here we present how the trend analysis, a new tool for global lipid profiling data analysis setup by Sanofi, was implemented and incorporated in Lipostar. The trend analysis is a versatile tool, which finds applications not only in biomarker’s discovery, but also in isotope clustering, in detecting in-source fragmentation or dimerization. In addition, clustering using K-Means or Bisecting K-Means are also available to discover new trends in data beyond what is anticipated at the outset. During this presentation, the various application of trend analysis tool will be described.