ArticleAnalytical chemistry2025
Fast and Accurate Charge State Deconvolution of Protein Mass Spectra.
Article in Analytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Automating Middle-Down Mass Spectrometry Analysis for Extensive Antibody Characterization.Analytical chemistry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.