The C-Score: A Bayesian Framework to Sharply Improve Proteoform Scoring in High-Throughput Top Down Proteomics
Open accessAbstract
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).
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Proteoforms passing a 1% false-discovery rate cutoff were associated with p-score, E-value, and C-score.
“Proteoforms passing the 1% false-discovery rate (FDR) cutoff41 were associated with a p-score, an E-value, and a C-score.42”
From Proton Transfer Charge Reduction Enables High-Throughput Top-Down Analysis of Large Proteoforms (2019) · cites this paper as [42]
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- Authors
- LeDuc, R. D., Fellers, R. T., Early, B. P., Greer, J. B., Thomas, P. M., & Kelleher, N. L.
- Affiliations
- Indiana University Bloomington (US)
- Northwestern University (US)
- Corresponding author
- LeDuc, Richard D.
- Kelleher, Neil L.
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-06-12
- Volume
- 13
- Issue
- 7
- Pages
- 3231-3240
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- PMC
- PMC4084843
- OpenAlex
- W2000710548
- Cited by
- 85
- References
- 38
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 4 · 2023: 7 · 2022: 6 · 2021: 6 · 2020: 8 · 2019: 13 · 2018: 7 · 2017: 12 · 2016: 10 · 2015: 3 · 2014: 1 · 2013: 1
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Proteome Research (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (ABI-1062432)
- National Institutes of Health (P30 DA018310, R01 GM067193)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- C-score, proteoform identification, top-down proteomics, protein characterization, generative models, expert knowledge integration, site-specific modification
- MeSH terms
- Amino Acid Sequence, Bacterial Proteins, Bayes Theorem, Data Interpretation, Statistical, HeLa Cells, Humans, Molecular Sequence Data, Peptide Mapping, Pseudomonas aeruginosa, ROC Curve, Area Under Curve, Proteome, Proteomics, Tandem Mass Spectrometry
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