A Perl procedure for protein identification by Peptide Mass Fingerprinting

Tiengo Alessandra, Barbarini Nicola, Troiani Sonia, Rusconi Luisa, Magni, Magni Paolo

Description

One of the topics of major interest in proteomics is protein identification. Protein identification can be achieved by analyzing the mass spectrum of a protein sample through different approaches. One of them, called Peptide Mass Fingerprinting (PMF), combines mass spectrometry (MS) data with searching strategies in a suitable database of known protein to provide a list of candidate proteins ranked by a score. To this aim, several algorithms and software tools have been proposed. However, the scoring methods and mainly the statistical evaluation of the results can be significantly improved. Results In this work, a Perl procedure for protein identification by PMF, called MsPI (Mass spectrometry Protein Identification), is presented. The implemented scoring methods were derived from the literature. MsPI implements a strategy to remove the contaminant masses present in the acquired spectra. Moreover, MsPI includes a statistical method to assign to each candidate protein, in addition to the scoring value, a p-value. Results obtained by MsPI on a dataset of 10 protein samples were compared with those achieved using two other software tools, i.e. Piums and Mascot. Piums implements one of the scoring methods available in MsPI, while Mascot is one of the most frequently used software tools in the protein identification field. MsPI scripts are available for downloading on the web site http://aimed11.unipv.it/MsPI . Conclusion The performances of MsPI seem to be better than those of Piums and Mascot. In fact, on the considered dataset, MsPI includes in its candidate proteins list, the "true" proteins nine times over ten, whereas Piums includes in its list the "true" proteins only four time over ten. Even if Mascot also correctly includes in the candidates list the "true" proteins nine times over ten, it provides longer candidate lists, therefore increasing the number of false positives when the molecular weight of the proteins in the sample is approximatively known (e.g. by the 1-D/2-D electrophoresis gel). Moreover, being MsPI a Perl tool, it can be easily extended and customized by the final users.

Informations

• Publié par: biomed
• Publié le: 01 janvier 2009
• Nombre de lectures: 15
• Langue: English

Extrait

Results:work, a Perl procedure for protein identification by PMF, called MsPI (MassIn this spectrometry Protein Identification), is present ed. The implemented scori ng methods were derived from the literature. MsPI implements a strategy t o remove the contaminant masses present in the acquired spectra. Moreover, MsPI includes a statistical method to assign to each candidate protein, in addition to the scoring value, a p-value. Results obtained by MsPI on a dataset of 10 protein samples were compared with those achieved using t wo other software tools, i.e. Piums and Mascot. Piums implements one of the scoring methods available in MsPI, while Mascot is one of the most frequently used software tools in the protein identification field. MsPI scripts are available for downloading on the web site http://aimed11.unipv.it/MsPI.

Abstract Background:One of the topics of major interest in proteomics is protein identification. Protein identification can be achieved by analyzing the mass spectrum of a protein sample through different approaches. One of them, called Peptide Mass Fingerprinting (PMF), combines mass spectrometry (MS) data with searching strategies in a suitable database of known protein to provide a list of candidate proteins ranked by a score. To this aim, several algorithms and software tools have been proposed. However, the scoring methods and mainly the statistical evaluation of the results can be significantly improved.

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Conclusion:than those of Piums and Mascot. In fact, onThe performances of MsPI seem to be better the considered dataset, MsPI includes in its candidate proteins list, the“true”proteins nine times over ten, whereas Piums includes in its list the“true”proteins only four time over ten. Even if Mascot also correctly includes in the candidates list the“true”proteins nine times over ten, it provides longer candidate lists, therefore increasing the number of false positives when the molecular weight of the proteins in the sample is approximatively known (e.g. by the 1-D/2-D electrophoresis gel). Moreover, being MsPI a Perl tool, it can be easily extended and customized by the final users.

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Address: 1 Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Via Ferrata 1, I-27100 Pavia, Italy and 2 Biotechnology dept., Nerviano Medical Sciences, Via Pasteur 10, I-20014 Nerviano, Italy E-mail: Alessandra Tiengo - [e-mail]; Nicola Barbarini - [e-mail]; Sonia Troiani - [e-mail]; Luisa Rusconi - [e-mail]; Paolo Magni* - [e-mail] *Corresponding author

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Research A Perl procedure for protein identification by Peptide Mas Fingerprinting Alessandra Tiengo 1 , Nicola Barbarini 1 , Sonia Troiani 2 , Luisa Rusco and Paolo Magni* 1

Published: 15 October 2009 BMC Bioinformatics2009,10 10.1186/1471-2105-10-S12-S11(Suppl 12):S11 doi:

This article is available from: http://www.biomedcentral.com/1471-2105/10/S12/S11 ©2009 Tiengo et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Open Access

fromBioinformatics Methods for Biomedical Complex Systems Applications (NETTAB2008) Varenna, Italy 19–21 May 2008

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