Category Archives: Resource Library

Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery

January 2019.

Radchenko T; Fontaine F; Morettoni L; Zamora I

Abstract

Peptide drugs have been used in the treatment of multiple pathologies. During peptide discovery, it is crucially important to be able to map the potential sites of cleavages of the proteases. This knowledge is used to later chemically modify the peptide drug to adapt it for the therapeutic use, making peptide stable against individual proteases or in complex medias. In some other cases it needed to make it specifically unstable for some proteases, as peptides could be used as a system to target delivery drugs on specific tissues or cells. The information about proteases, their sites of cleavages and substrates are widely spread across publications and collected in databases such as MEROPS.

Therefore, it is possible to develop models to improve the understanding of the potential peptide drug proteolysis. We propose a new workflow to derive protease specificity rules and predict the potential scissile bonds in peptides for individual proteases. WebMetabase stores the information from experimental or external sources in a chemically aware database where each peptide and site of cleavage is represented as a sequence of structural blocks connected by amide bonds and characterized by its physicochemical properties described by Volsurf descriptors. Thus, this methodology could be applied in the case of non-standard amino acid. A frequency analysis can be performed in WebMetabase to discover the most frequent cleavage sites.

These results were used to train several models using logistic regression, support vector machine and ensemble tree classifiers to map cleavage sites for several human proteases from four different families (serine, cysteine, aspartic and matrix metalloproteases). Finally, we compared the predictive performance of the developed models with other available public tools PROSPERous and SitePrediction. 

High resolution Mass Spectrometry with automated data analysis to support structural elucidation of degradation impurities of small peptides

AAPS 2019 PHARMSCI 360, San Antonio (United States of America)… 03 November, 2019 

Abstract

Structural elucidation of drug substance related impurities in drug products to identify specific degradation pathways is important for the development of formulated drugs, optimization of manufacturing process and in certain cases a requirement for regulatory submissions. The present work utilized an in-silico data processing tool MassChemSite, which has been developed to automate data analysis and to facilitate the structural elucidation of drug degradants by LC-MS/MS. The software was customized to work on structure modifications introduced by common degradation chemistries for small peptides. 

Automatization of structural elucidation workflow for detecting degradation impurities in Peptides

February 2020

Elisabeth Ortega-Carrasco, Blanca Serra, Ismael Zamora

Abstract

Detection and identification of drug degradation impurities in drug products is important to the development of formulated drugs. Structures and formation mechanisms of degradation impurities need to be identified once the degradants exceed certain specified levels, as required for the regulatory guidelines.  A rapid structure elucidation of those drug substance related impurities is essential to have a clear understanding of the quality of the new drug. 

For this purpose, liquid chromatography-mass spectrometry (LC-MS) techniques are the most frequently used. However, the processing and rationalization of MS/MS data can be quite time consuming, especially in peptide studies due to their size and multiple charge. In this poster we present a fully automatic workflow for structural elucidation of degradation impurities in peptides implemented in MassChemsite (Molecular Discovery, Ltd., London, UK) program.