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Record W3006224482 · doi:10.1016/j.celrep.2020.01.029

Molecular Insight into Recognition of the CGRPR Complex by Migraine Prevention Therapy Aimovig (Erenumab)

2020· article· en· W3006224482 on OpenAlexaff
Fernando Garcés, Christopher Mohr, Li Zhang, Ching-Shin Huang, Qing Chen, Chadwick King, Cen Xu, Zhulun Wang

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsAmgen (Canada)
FundersBasic Energy SciencesNational Institute of General Medical SciencesHoward Hughes Medical InstituteOffice of ScienceNational Institutes of HealthU.S. Department of Energy
KeywordsMigraineCalcitonin gene-related peptidePharmacologyG protein-coupled receptorReceptorMedicineComputational biologyBiologyInternal medicineNeuropeptide

Abstract

fetched live from OpenAlex

Calcitonin-gene-related peptide (CGRP) plays a key role in migraine pathophysiology. Aimovig (erenumab; erenumab-aooe in the United States) is the only US Food and Drug Administration (FDA)-approved monoclonal antibody (mAb) therapy against the CGRP receptor (CGRPR) for the prevention of migraine. Aimovig is also the first FDA-approved mAb against a G-protein-coupled receptor (GPCR). Here, we report the architecture and functional attributes of erenumab critical for its potent antagonism against CGRPR. The crystal structure of erenumab in complex with CGRPR reveals a direct ligand-blocking mechanism, enabled by a remarkable 21-residue-long complementary determining region (CDR)-H3 loop, which adopts a tyrosine-rich helix-turn tip and projects into the deep interface of the calcitonin receptor-like receptor (CLR) and RAMP1 subunits of CGRPR. Furthermore, erenumab engages with residues specific to CLR and RAMP1, providing the molecular basis for its exquisite selectivity. Such structural insights reveal the drug action mechanism of erenumab and shed light on developing antibody therapeutics targeting GPCRs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.281
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations52
Published2020
Admission routes1
Has abstractyes

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