An integrated protocol based on workflows of imaged capillary isoelectric focusing (icIEF) for in-depth protein heterogenous characterization: High-efficient fractionation and online mass spectrometry detection
Bibliographic record
Abstract
• An integrated icIEF protocol to study the charge heterogeneity of proteins • The protocol includes two workflows of icIEF fractionation and MS online • Established protocol with “Green Chemistry Concept” is robust for biopharma We present an integrated protocol for studying the charge heterogeneity of therapeutic proteins combining two workflows: imaged capillary isoelectric focusing (icIEF) with fractionation and online mass spectrometry (MS) detection on a single platform. This protocol enables both intact MS-based protein charge variant characterization and in-depth peptide mapping of collected fractions via high-performance liquid chromatography (HPLC) tandem mass spectrometry. Through systematic methodology validation, the platform is demonstrated to be robust, with step-by-step method development, standardized operating procedures (SOPs), exceptional reproducibility, and high sensitivity. Notably, this protocol facilitates icIEF-UV separation followed by either a fractionation scheme or MS online detection within a single platform, providing simplified workflows with reduced reagent consumption compared to traditional techniques. Embracing the "Green Chemistry" concept, the protocol addresses key challenges in biopharmaceutical discovery, quality control, and manufacturing, promoting sustainability throughout the process.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".