title: Improved validation of peptide MS/MS assignments using spectral intensity prediction.
abstract: A major limitation in identifying peptides from complex mixtures by shotgun proteomics is the ability of search programs to accurately assign peptide sequences using mass spectrometric fragmentation spectra (MS/MS spectra). Manual analysis is used to assess borderline identifications; however, it is error-prone and time-consuming, and criteria for acceptance or rejection are not well defined. Here we report a Manual Analysis Emulator (MAE) program that evaluates results from search programs by implementing two commonly used criteria: 1) consistency of fragment ion intensities with predicted gas phase chemistry and 2) whether a high proportion of the ion intensity (proportion of ion current (PIC)) in the MS/MS spectra can be derived from the peptide sequence. To evaluate chemical plausibility, MAE utilizes similarity (Sim) scoring against theoretical spectra simulated by MassAnalyzer software (Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908-3922) using known gas phase chemical mechanisms. The results show that Sim scores provide significantly greater discrimination between correct and incorrect search results than achieved by Sequest XCorr scoring or Mascot Mowse scoring, allowing reliable automated validation of borderline cases. To evaluate PIC, MAE simplifies the DTA text files summarizing the MS/MS spectra and applies heuristic rules to classify the fragment ions. MAE output also provides data mining functions, which are illustrated by using PIC to identify spectral chimeras, where two or more peptide ions were sequenced together, as well as cases where fragmentation chemistry is not well predicted.
authors.affiliation: Department of Computer Science and Engineering, University of Colorado at Denver and Health Sciences Center, Denver, Colorado 80217-3364, USA.
authors.forename: Shaojun
authors.initials: S
authors.lastname: Sun
authors.forename: Karen
authors.initials: K
authors.lastname: Meyer-Arendt
authors.forename: Brian
authors.initials: B
authors.lastname: Eichelberger
authors.forename: Robert
authors.initials: R
authors.lastname: Brown
authors.forename: Chia-Yu
authors.initials: CY
authors.lastname: Yen
authors.forename: William M
authors.initials: WM
authors.lastname: Old
authors.forename: Kevin
authors.initials: K
authors.lastname: Pierce
authors.forename: Krzysztof J
authors.initials: KJ
authors.lastname: Cios
authors.forename: Natalie G
authors.initials: NG
authors.lastname: Ahn
authors.forename: Katheryn A
authors.initials: KA
authors.lastname: Resing
mesh_terms: D000595:Amino Acid Sequence; D030562:Databases, Protein; D006801:Humans; D020014:K562 Cells; D013058:Mass Spectrometry / Q000379:methods*; D008969:Molecular Sequence Data; D009363:Neoplasm Proteins / Q000737:chemistry; D010455:Peptides / Q000032:analysis* / Q000737:chemistry*; D040081:Protein Array Analysis; D040901:Proteomics; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software
chemical_list: D009363:Neoplasm Proteins; D010455:Peptides