Prediction of peptides observable by mass spectrometry applied at the experimental set level

Background: When proteins are subjected to proteolytic digestion and analyzed by mass spectrometry using a method such as 2D LC MS/MS, only a portion of the proteotypic peptides associated with each protein will be observed. The ability to predict which peptides can and cannot potentially be observed for a particular experimental dataset has several important applications in proteomics research including calculation of peptide coverage in terms of potentially detectable peptides, systems biology analysis of data sets, and protein quantification. Results: We have developed a methodology for constructing artificial neural networks that can be used to predict which peptides are potentially observable for a given set of experimental, instrumental, and analytical conditions for 2D LC MS/MS (a.k.a Multidimensional Protein Identification Technology [MudPIT]) datasets. Neural network classifiers constructed using this procedure for two MudPIT datasets exhibit 10-fold cross validation accuracy...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: William S. Sanders, Susan M. Bridges, Fiona M. McCarthy, Bindu Nanduri, Shane C. Burgess
• Keywords: BMCBI 2007, Detectable Peptides, Peptides, Proteins