Fast and Accurate Charge State Deconvolution of Protein Mass Spectra
Bibliographic record
Abstract
Charge state deconvolution is essential for efficient and effective protein mass spectrometry analysis. High-quality mass profiling is necessary to determine which proteoforms are present in protein samples and their relative abundances. In the pursuit of a well-rounded deconvolution solution, we detail an iterative charge state deconvolution algorithm named kDecon that has been tuned to provide high accuracy in its mass results while also delivering superb sensitivity toward lower abundance proteoforms in complex spectra. Here, the performance of kDecon as a mass determination algorithm for both targeted antibody and high-throughput proteomics analysis was benchmarked against existing deconvolution solutions. While the different deconvolution routines all proved robust for detecting the highest abundance protein species, kDecon ultimately showcased best-in-class precision for lower abundance proteoform mass profiling. Furthermore, kDecon results had up to 7-fold fewer false positives and simultaneously exhibited at least 20-fold speed improvements over the other algorithms. Overall, these deconvolution advances will contribute to enabling both routine and thorough intact mass profiling studies for biotherapeutics as well as improving the proteome coverage of top-down proteomics experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".