Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
+44 1223 790975
ISSN: 0974-276X
+44 1223 790975
Carsten Henneges
Wilhelm-Schickard Institute, Eberhardt Karls Universität,
Tübingen, Sand 1, 72076 Tübingen
Germany
Research Article
Ranking Methods for the Prediction of Frequent Top Scoring Peptides from Proteomics Data
Author(s): Carsten Henneges, Georg Hinselmann, Stephan Jung, Johannes Madlung, Wolfgang Schütz, Alfred Nordheim and Andreas ZellCarsten Henneges, Georg Hinselmann, Stephan Jung, Johannes Madlung, Wolfgang Schütz, Alfred Nordheim and Andreas Zell
Proteomics facilities accumulate large amounts of proteomics data that are archived for documentation purposes. Since proteomics search engines, e.g. Mascot or Sequest, are used for peptide sequencing resulting in peptide hits that are ranked by a score, we apply ranking algorithms to combine archived search results into predictive models. In this way peptide sequences can be identified that frequently achieve high scores. Using our approach they can be predicted directly from their molecular structure and then be used to support protein identification or perform experiments that require reliable peptide identification. We prepared all peptide sequences and Mascot scores from a four year period of proteomics experiments on Homo sapiens of the Proteome Center Tuebingen for training. To encode the peptides MacroModel and DragonX were used for molecular descriptor comput.. View More»
DOI:
10.4172/jpb.1000081