title: Deriving statistical models for predicting peptide tandem MS product ion intensities.
abstract: Improved search algorithms and scoring functions are required before the identification of peptide tandem MS data can be considered to be fully reliable and automatable. The development of models that can accurately predict product ion spectra from a peptide sequence would certainly help achieve this goal, but this firstly requires a better understanding of the process of fragmentation of peptides in the gas-phase. We summarize recent developments in this area and show that the prediction of product ion spectra is feasible and should improve the identification of peptide tandem MS data, especially for peptides that currently give low or insignificant scores with current search algorithms.
authors.affiliation: Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville 3050, Victoria, Australia. schutz@wehi.edu.au
authors.forename: F
authors.initials: F
authors.lastname: Schütz
authors.forename: E A
authors.initials: EA
authors.lastname: Kapp
authors.forename: R J
authors.initials: RJ
authors.lastname: Simpson
authors.forename: T P
authors.initials: TP
authors.lastname: Speed
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D010455:Peptides / Q000737:chemistry*
chemical_list: D010455:Peptides