Popitam: towards new heuristic strategies to improve protein identification from tandem mass spectrometry data.

Abstract:

:In recent years, proteomics research has gained importance due to increasingly powerful techniques in protein purification, mass spectrometry and identification, and due to the development of extensive protein and DNA databases from various organisms. Nevertheless, current identification methods from spectrometric data have difficulties in handling modifications or mutations in the source peptide. Moreover, they have low performance when run on large databases (such as genomic databases), or with low quality data, for example due to bad calibration or low fragmentation of the source peptide. We present a new algorithm dedicated to automated protein identification from tandem mass spectrometry (MS/MS) data by searching a peptide sequence database. Our identification approach shows promising properties for solving the specific difficulties enumerated above. It consists of matching theoretical peptide sequences issued from a database with a structured representation of the source MS/MS spectrum. The representation is similar to the spectrum graphs commonly used by de novo sequencing software. The identification process involves the parsing of the graph in order to emphasize relevant sections for each theoretical sequence, and leads to a list of peptides ranked by a correlation score. The parsing of the graph, which can be a highly combinatorial task, is performed by a bio-inspired algorithm called Ant Colony Optimization algorithm.

journal_name

Proteomics

journal_title

Proteomics

authors

Hernandez P,Gras R,Frey J,Appel RD

doi

10.1002/pmic.200300402

subject

Has Abstract

pub_date

2003-06-01 00:00:00

pages

870-8

issue

6

eissn

1615-9853

issn

1615-9861

journal_volume

3

pub_type

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