A structural basis for interferon‐α‐receptor interactions
Bibliographic record
Abstract
Interferon (IFN)-alpha subtypes exhibit differences in biological potencies based on their affinity interactions with the IFN receptor subunits, IFNAR1 and IFNAR2. Using available three-dimensional structural information and computational biology, homology models of human IFN-alpha1, human IFN-alpha8, IFN alfacon-1, and murine IFN-alpha4 were derived and docked with the extracellular region of human IFNAR2 to evaluate the behavior of potential interacting residue pairs and characterize the nature of the IFN-IFNAR2 binding interfaces. The data suggest that IFN afacon-1 has 9 optimal interactions with IFNAR2, comprising hydrophobic, electrostatic, and hydrogen bonding. Human IFN-alpha2 exhibits 8 optimal interactions, human IFN-alpha1, 7, and murine IFN-alpha4 exhibits the least number of optimal interactions, at 5. A model of IFNAR1 was generated, taking into consideration the IFNAR1 extracellular domain interaction with cell surface glycosphingolipids, putative ligand interaction residues, and residues stabilizing the structural integrity of IFNAR. IFNAR1 was then docked with the various IFN-IFNAR2 complexes to describe the complete extracellular receptor pocket with bound IFN. These data provide insights into the species specificity of IFN-alphas: residues in murine IFN-alpha4 that preclude strong affinity interactions with human IFNAR because of steric crowding and residues in human IFN-alpha8 that resemble a receptor interactive domain in murine IFN-alpha4, are described.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".