Investigating pancreatic β cell membrane epitopes using unbiased cell-based Fab-phage display
Bibliographic record
Abstract
Abstract The phenotypic and functional changes of cells in response to physiological and pathological conditions are strongly influenced by the roles of plasma membrane proteins. Recombinant Fab antibody-based phage display for an unbiased antigen-driven affinity selection is a suitable approach for identifying novel membrane proteins. Alterations in the function and distribution of cell membrane proteins in pancreatic β cells have been observed in pathological conditions like diabetes. In this study, we integrated an unbiased cell-based Fab-phage display screening method with bioinformatics tools to identify and characterize Fabs that selectively bind to pancreatic β cells in conditions simulated by a hyperglycemic environment. We isolated three Fab-phages, namely Fab_53, 538, and 54.68, that have binding properties matching specific epitopes on the MIN6 membrane. These Fabs are part of the immunoglobulin G groups that contain Kappa light chains. Bioinformatics analysis of the variable domains of their light and heavy chains (VL and VH) revealed that the potential epitope binding sites on the β cell membrane are associated with pathways involved in insulin activity. Through FACS and IF analysis, we found that, of the three Fabs, Fab_538 exhibited the strongest binding to MIN6 cells. The use of InterProScan software resulted in the generation of 344 potential Fab_538 binding epitopes, and from these, AF2Complex predicted 10 interacting antigens. Our goal in combining Fab-phage display with bioinformatic tools is to develop a more effective, specific, and streamlined method for identifying disease-modifying membrane epitopes for monoclonal antibodies (mAbs).
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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 | 0.000 | 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) | 0.001 | 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 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".