title: The C-score: a Bayesian framework to sharply improve proteoform scoring in high-throughput top down proteomics.
abstract: The automated processing of data generated by top down proteomics would benefit from improved scoring for protein identification and characterization of highly related protein forms (proteoforms). Here we propose the "C-score" (short for Characterization Score), a Bayesian approach to the proteoform identification and characterization problem, implemented within a framework to allow the infusion of expert knowledge into generative models that take advantage of known properties of proteins and top down analytical systems (e.g., fragmentation propensities, "off-by-1 Da" discontinuous errors, and intelligent weighting for site-specific modifications). The performance of the scoring system based on the initial generative models was compared to the current probability-based scoring system used within both ProSightPC and ProSightPTM on a manually curated set of 295 human proteoforms. The current implementation of the C-score framework generated a marked improvement over the existing scoring system as measured by the area under the curve on the resulting ROC chart (AUC of 0.99 versus 0.78).
authors.affiliation: National Center for Genome Analysis Support, Indiana University , 2709 E. 10th Street, Bloomington, Indiana 47408, United States.
authors.forename: Richard D
authors.initials: RD
authors.lastname: LeDuc
authors.forename: Ryan T
authors.initials: RT
authors.lastname: Fellers
authors.forename: Bryan P
authors.initials: BP
authors.lastname: Early
authors.forename: Joseph B
authors.initials: JB
authors.lastname: Greer
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thomas
authors.forename: Neil L
authors.initials: NL
authors.lastname: Kelleher
mesh_terms: D000595:Amino Acid Sequence; D019540:Area Under Curve; D001426:Bacterial Proteins / Q000737:chemistry; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D006367:HeLa Cells; D006801:Humans; D008969:Molecular Sequence Data; D010449:Peptide Mapping / Q000379:methods* / Q000592:standards; D020543:Proteome / Q000737:chemistry; D040901:Proteomics; D011550:Pseudomonas aeruginosa; D012372:ROC Curve; D053719:Tandem Mass Spectrometry
chemical_list: D001426:Bacterial Proteins; D020543:Proteome