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Title: Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

Journal Article · · Genomics, Proteomics & Bioinformatics
 [1];  [1];  [1];  [2];  [3];  [3];  [4];  [4];  [4];  [5];  [3];  [6]
  1. Vanderbilt Univ. Medical Center, Nashville, TN (United States). Dept. of Biomedical Informatics
  2. Vanderbilt Univ. Medical Center, Nashville, TN (United States). Dept. of Biomedical Informatics; Mayo Clinic, Rochester, NY (United States). Div. of Biomedical Statistics and Informatics
  3. Vanderbilt Univ. Medical Center, Nashville, TN (United States). Dept. of Biochemistry
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Environmental Molecular Sciences Lab. (EMSL)
  5. Vanderbilt Univ. Medical Center, Nashville, TN (United States). Dept. of Pharmacology
  6. Vanderbilt Univ. Medical Center, Nashville, TN (United States). Depts. of Biomedical Informatics, Biochemistry and Vanderbilt-Ingram Cancer Center
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In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (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 fragmentation 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.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Environmental Molecular Sciences Laboratory (EMSL)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1095420
Report Number(s):
PNNL-SA-98549; 47418; KP1601010
Journal Information:
Genomics, Proteomics & Bioinformatics, Vol. 11, Issue 2; ISSN 1672-0229
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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