The C‑Score: A Bayesian Framework to Sharply Improve Proteoform Scoring in High-Throughput Top Down Proteomics

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).