In shotgun proteomics, database search algorithms rely on fragmentation models to pre- dict fragment ions that should be observed for a given peptide sequence. The most widely used strat- egy （Naive model） is oversi...In shotgun proteomics, database search algorithms rely on fragmentation models to pre- dict fragment ions that should be observed for a given peptide sequence. The most widely used strat- egy （Naive model） is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmen- tation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher- energy collision induced dissociation （HCD） of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% （on average） over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.展开更多
基金supported by the National Library of Medicine training grant (Grant No. 5T15LM007450-10)
文摘In shotgun proteomics, database search algorithms rely on fragmentation models to pre- dict fragment ions that should be observed for a given peptide sequence. The most widely used strat- egy （Naive model） is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmen- tation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher- energy collision induced dissociation （HCD） of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% （on average） over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.
