MétaCan
Menu
Back to cohort
Record W2417133368 · doi:10.1021/jacs.5b06220

Paramagnetic Ligand Tagging To Identify Protein Binding Sites

2015· article· en· W2417133368 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Chemical Society · 2015
Typearticle
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsUniversity of British Columbia
FundersEuropean Research CouncilVetenskapsrådetSwedish Foundation for International Cooperation in Research and Higher EducationEuropean Commission
KeywordsChemistryParamagnetismLigand (biochemistry)Computational biologyBiochemistryReceptor

Abstract

fetched live from OpenAlex

Transient biomolecular interactions are the cornerstones of the cellular machinery. The identification of the binding sites for low affinity molecular encounters is essential for the development of high affinity pharmaceuticals from weakly binding leads but is hindered by the lack of robust methodologies for characterization of weakly binding complexes. We introduce a paramagnetic ligand tagging approach that enables localization of low affinity protein-ligand binding clefts by detection and analysis of intermolecular protein NMR pseudocontact shifts, which are invoked by the covalent attachment of a paramagnetic lanthanoid chelating tag to the ligand of interest. The methodology is corroborated by identification of the low millimolar volatile anesthetic interaction site of the calcium sensor protein calmodulin. It presents an efficient route to binding site localization for low affinity complexes and is applicable to rapid screening of protein-ligand systems with varying binding affinity.

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.

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 score0.004
Threshold uncertainty score0.388

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 science0.0010.000
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 head0.026
GPT teacher head0.310
Teacher spread0.284 · 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