Cytokine Activation by Antibody Fragments Targeted to Cytokine-Receptor Signaling Complexes
Bibliographic record
Abstract
Exogenous cytokine therapy can induce systemic toxicity, which might be prevented by activating endogenously produced cytokines in local cell niches. Here we developed antibody-based activators of cytokine signaling (AcCS), which recognize cytokines only when they are bound to their cell surface receptors. AcCS were developed for type I interferons (IFNs), which induce cellular activities by binding to cell surface receptors IFNAR1 and IFNAR2. As a potential alternative to exogenous IFN therapy, AcCS were shown to potentiate the biological activities of natural IFNs by ∼100-fold. Biochemical and structural characterization demonstrates that the AcCS stabilize the IFN-IFNAR2 binary complex by recognizing an IFN-induced conformational change in IFNAR2. Using IFN mutants that disrupt IFNAR1 binding, AcCS were able to enhance IFN antiviral potency without activating antiproliferative responses. This suggests AcCS can be used to manipulate cytokine signaling for basic science and possibly for therapeutic applications. Background: Cytokines are administered to patients to eliminate viral infections and cancer yet often have side effects. Results: Antibody fragments have been designed that recognize cytokine-receptor complexes and change cytokine biological activity profiles. Conclusion: Designed proteins enhance interferon antiviral activity without inducing antiproliferative signaling pathways. Significance: Antibody fragments targeted to cytokine-receptor complexes provide new tools for manipulating cytokine signaling.
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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 | 0.001 |
| 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".