Improved machine learning method for analysis of gas phase chemistry of peptides

Background: Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra. Results: We employed a robust data mining strategy using new feature annotation functions of MAE software, which revealed under-prediction of the frequency of occurrence in fragmentation of the second peptide bond. We applied methods of exploratory data analysis to pre-process the information in the MS/MS spectra, including data normalization and attribute selection, to reduce the attributes to a smaller, less correlated set for machine learning studies. We then compared o...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Allison Gehrke, Shaojun Sun, Lukasz A. Kurgan, Natalie Ahn, Katheryn Resing, Karen Kafadar, Krzysztof J. Cios
• Keywords: BMCBI 2008, Gas Phase, Peptide, Peptide Bond