Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates
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 a...
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Published in | Genomics, proteomics & bioinformatics Vol. 11; no. 2; pp. 86 - 95 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
China
Elsevier Ltd
01.04.2013
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA%Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA%Department of Biochemistry, Vanderbilt University Medical Center, Nashville, TN 37232, USA%Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland,WA 99354, USA Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | 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. 11-4926/Q Fragmentation;Basicity;Fragment size;Ordinal regression ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 USDOE AC05-76RL01830 PNNL-SA-98549 Equal contribution. |
ISSN: | 1672-0229 2210-3244 |
DOI: | 10.1016/j.gpb.2012.11.004 |