Direct Injection Mass Spectrometry and iFishMass for the High-Throughput Analysis of Antibody Modifications
Bibliographic record
Abstract
High-throughput analysis has become a critical component in chemical biology and analytical chemistry due to the large libraries of compounds that are screened every day for drug development. Mass spectrometry (MS)-based proteomics is the methodology of choice for large-scale identification and quantification of protein modifications, both chemically deposited and biological post-translational modifications (PTMs). With the advent of antibody drug conjugates (ADCs) and other novel protein-based conjugates, the demand for such an analysis has skyrocketed. Here, we present a new protocol that achieves quantitative data for modified peptides in approximately 30 s of MS acquisition time. This platform includes a direct injection MS approach coupled with new software named iFishMass to extract targeted signals from hundreds of runs. iFishMass automatically generates plots and statistics. This platform will enable a faster analysis of synthetic modifications installed on monoclonal antibodies to create ADCs, and it is potentially scalable to biological PTMs. Sample preparation can be parallelized for 384 samples by using multichannel pipettes and 96-well plates, paving the way to an inexpensive but effective platform for high-throughput screening of conjugation sites on proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".