Journal of Proteomics & Bioinformatics
Open Access

Carsten Henneges

Publications

• 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 Zell Carsten 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

  Abstract PDF

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