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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. 

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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.

 

Solutions for flux analysis in lipidomics

Solutions for OMICS

Lipid biomarker research represents one of the most widespread applications in lipidomics.  In this context, stable isotope labelling has become a staple technique in the study of lipid metabolism and dynamics, as it allows to directly measuring biosynthesis, remodeling and degradation of biomolecules.  The application of stable isotope labelling to lipidomics is still considered a challenging task, especially for the complexity of the necessary data analysis. Lipostar offers data analysis solutions for flux analysis experiments based on stable isotope labelling. During this presentation, we will describe both the Lipostar data analysis workflow for flux analysis and how flux analysis data can be easily integrated in untargeted studies.

 

Untargeted and targeted lipidomics: from raw data to bio-pathways

Solutions for OMICS

Over the last two decades, lipids have come to be understood as far more than merely components of cellular membranes and forms of energy storage. Indeed, their pivotal role in a variety of diseases including diabetes, obesity, heart diseases, cancer, or neurodegenerative diseases, is now commonly accepted.  Consequently, lipidomics represents an emerging field with the aim of unravelling diagnostic biomarkers, new drug targets, and of rationalizing toxicity effects. Mass spectrometry, due to its sensitivity and selectivity, is the elected method for qualitative and quantitative lipidomics analysis, and the recent improvements in MS technologies have moved interest from targeted to untargeted approaches. However, untargeted lipidomics requires tailored software solutions for data analysis. To this aim, we recently developed Lipostar, a vendor-neutral high-throughput software to support targeted and untargeted LC-MS lipidomics. Lipostar supports the overall data analysis steps including raw files import, data mining, statistical analysis (including prediction), lipid identification, data interpretation of bio-pathways. During this presentation, a general overview of the software will be provided, together to hints and tips to adapt the software to your experimental protocols.

 

Metabolite ID for peptides made simple

Biopharma Solutions

Peptides therapeutics are becoming increasingly important on the pharmaceutical market. However, it is known that peptide drugs bioavailability and stability are lower than for small molecules. It is highly important to understand peptide drug metabolism and optimize its clearance as it influences the drug safety and efficacy. Because peptides are mainly cleaved by peptidases, every new candidate must be designed considering the localization of potential protease sites of cleavage. We will demonstrate how to use Mass-MetaSite and WebMetabase to process HRMS data from in vitro incubation samples, to elucidate metabolites structures, to predict cleavage sites and to store the results in a chemically aware database. We will use examples with cyclic and linear peptides, containing nonstandard amino acids collected from different sources to demonstrate that there are no limitations related to peptide structure or chemical modifications of amino acids.

 

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