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  Projects lead by the ICAR group

[ Popitam ] [ Aldente ] [ swissPIT ] [ SPUMS ] [ SmileMS ]

Introduction

The Identification and Characterisation (ICAR) axis is one of the three principle research axes of the PIG group. Identification and characterization of all proteins expressed by a genome in biological samples represent major challenges in proteomics. Today commonly used approaches combine protein separation with mass spectrometry (MS) analysis, including peptide mass fingerprinting (PMF) analysis and tandem MS (MS/MS) analysis. Although automation is often possible, a number of limitations still adversely affect the rate of protein identification and annotation in databases. Mass spectrometry produces large volumes of data that can be used, on the one hand to search protein as well as genomic databases in order to identify the analysed proteins, and on the other hand to partially characterize the proteins previously separated, that is to search for possible post-translational modifications. One important activity of the group is the design of new algorithms for the identification and characterisation of proteins from PMF and MS-MS data.

Popitam

web interface

Description

Popitam is a method designed to characterize peptides with mutations or unexpected post-translational modifications using MS/MS data. In short, Popitam reduces the spectrum space by using database information to exclusively extract tags that are consistent with theoretical peptides and constructs several tag scenarios for each theoretical peptide. Each scenario is scored according to a function that has been generated using Genetic Programming. The theoretical peptide with the highest scoring scenario is proposed as identification of the spectrum. Possible modifications on one (or any of several adjacent amino acids) are represented by their delta mass values.

Publications


Aldente

web interface

Description

Aldente is a tool to identify proteins from peptide mass fingerprinting data. Aldente uses the Hough transform to determine the mass spectrometer deviation, to realign the experimental masses and to exclude outliers. This tool has other unique advantages and features: it extensively uses the Swiss-Prot annotations (PTM, alternative splicing, etc.) and it is completely interconnected with other ExPASy proteomics tools, offering the functionality of protein characterization as part of the identification procedure; the scores may be tailored by fully customizable parameters; besides from the usual chemical amino acid modifications, it also considers any user-defined modifications, such as alkylation products on cysteine residues, with the possibility to define their contribution to the score.

Publications


swissPIT

web interface

Description

SwissPIT (swiss Protein Identification Toolbox) is a pipeline for knowledge extraction from MS data. Too often, proteomics scientists multiply manual procedures to efficiently analyse mass spectrometry data. Software tools are run several times in order to empirically discover the best parameter settings. When various strategies of MS analysis are used, the results are manually selected and combined. In most situations though, only one single tool is used for protein identification along with a unique parameter setting. Many spectra are thus missed due to inappropriate parameter values, to inadequate filtering or simply to under-performance of certain scoring schemes for the quality of the spectra at hand. The swissPIT platform aims at providing a flexible automation of computer tasks, as well as a combination of different workflow strategies, which are thus necessary to enhance data analysis, to improve the quality and confidence in the identification and characterization results, to reduce human interaction and to achieve high-throughput analysis.

Publications


Saving Patients using Mass Spectrometry (SPUMS)

Proteomics is part of the array of techniques used for high-throughput screening. It commonly captures circulating proteins in body fluids. While mass spectrometry is being increasingly introduced in clinical settings, no large-scale screening methods of the effect of ingested drugs on circulating proteins is currently available. However, Adverse Drug Responses (ADR) is a major challenge of modern medicine.

In toxico-proteomics, toxicity biomarkers are sought. An automated method detecting most of the drugs that the patient may have taken is a first very useful step towards determining potential adverse drug responses but it might not be sufficient. Converging evidence tends to show that chemicals affect proteins at the molecular level through chemical modifications and these modified proteins reflect the reality of the toxic offence. The detection of drugs in blood then needs to be complemented by that of affected proteins or peptides, i.e., toxicity biomarkers.

Drug-induced posttranslational modifications (PTMs) in blood proteins are not commonly considered though, for instance, drug-induced alkylation of albumin was reported previously. The present study aims at investigating the occurrence of PTMs on selected blood proteins after a drug treatment. Since a protein lifespan is longer than that of a drug in the blood (several days compared to a few hours), toxic interference of different other drugs even days after a first treatment could be explained by the presence of drug-induced modifications. In this study, we focus on alkylating anticancer drugs (cyclophosphamide for breast cancer and lymphoma patients, temozolomide for brain tumour patients), which lead to DNA alkylation and cross-linking in cancerous cells.

From a bioinformatics perspective, the goal of SPUMS is to design and implement an efficient platform (software and database) that can automatically and quickly recognise the effect(s) induced by the variable presence of drug chemical fragments on circulating peptides in mass spectra obtained from patient blood samples.

Read more on this project

SmileMS

web interface

Description

The goal of the SmileMS project is to design and implement an efficient platform (software and database) that can automatically and quickly recognise circulating drug chemical fragments in mass spectra obtained from patient blood samples. It is an innovative solution that targets an unmet yet growing need for efficient and automatable processes using LC-MS techniques for routine daily analysis of small molecules.

SmileMS is developed in collaboration with Geneva Bioinformatics S.A. and Prof. Denis Hochstrasser's group at the Geneva University Hospitals.

Publications



People working at ICAR:

Last modified 20/Mar/2009 by CHH

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