Structure-based Design of Mimetics for Granulocyte-macrophage Colony Stimulating Factor (GM-CSF)
Bibliographic record
Abstract
Granulocyte-macrophage colony stimulating factor (GM-CSF) activity has been linked to pro-inflammatory effects in autoimmune syndromes, such as rheumatoid arthritis. Thus GM-CSF mimetics with antagonist activity might play a therapeutic role in these diseases. The human GM-CSF core structure consists of a four alpha-helix bundle, and GM-CSF activity is controlled by its binding to a two-subunit receptor. A number of residues located on the B and C helices of GM-CSF are postulated to interact with the alpha chain of the GM-CSF receptor (GM-CSFR). Several approaches have been successfully utilized to develop peptide mimetics of this site, including peptides from the native sequence, a peptide derived from a recombinant antibody (rAb) light chain which mimicked GM-CSF receptor binding activity, and structurally guided de novo design. Analysis of the rAb light chain had suggested mimicry of GM-CSF with residues mostly contributed by the CDR I region. Key residues involved in CDR I peptide/GM-CSFR binding were identified by truncation and alteration of individual residues, while the structural elements required to antagonize the biological action of GM-CSF were separately tested in binding and inhibitory activity assays of multiple cyclic analogues. A peptide designed to retain the loop conformation of the CDR I region of the rAb light chain competed with GM-CSF for both antibody and receptor binding, but the role of specific residues in antibody versus receptor binding differed markedly. These studies suggest that structural analysis of peptide mimetics can reveal differences in receptor and antibody binding, perhaps including key interactions that impact binding kinetics. Peptide mimetics of other four-helix bundle cytokines are reviewed, including helical and reverse turn mimetics of helical structures. Use of peptide mimetics coupled with structural and kinetic analysis provides a powerful approach to identifying important receptor-ligand interactions, which implications for rational design of novel therapeutics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".