Structure-based design of macrocyclic peptides to generate functional antibodies against G protein-coupled receptors
Bibliographic record
Abstract
G protein-coupled receptors (GPCRs) are critical to a variety of pathophysiological processes, making them attractive targets for the development of drugs or relevant diagnostic tools. Although GPCRs have been successfully targeted with small molecules, the production of reliable anti-GPCR antibodies remains a major challenge. To address this issue, we develop a strategy using macrocyclic peptides designed to mimic the three-dimensional structure of GPCR extracellular loops as immunogens and use the chicken, which is genetically distant from mammals, as an immunization host to produce antigen-specific antibodies. The high-affinity neurotensin receptor type 1 (NTS1), overexpressed in many types of human cancer and associated with poor prognosis, is used as a target. Rational design of macrocyclic epitope mimics and linker selection are achieved using modeling and predictive analysis software tools based on available NTS1 crystal structures. This study particularly highlights the critical role of the linker in peptide macrocyclization, which determines whether antibodies can exert antagonistic activity. Overall, this strategy represents a valuable asset to produce effective spatial anti-GPCR antibodies and holds promise for diagnostic and therapeutic applications.
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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".