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Record W2164655870 · doi:10.1021/bi0264162

Detecting Protein Kinase Recognition Modes of Calmodulin by Residual Dipolar Couplings in Solution NMR

2002· article· en· W2164655870 on OpenAlexaff
Tapas K. Mal, Nikolai R. Skrynnikov, Kyoko L. Yap, Mitsuhiko Ikura

Bibliographic record

VenueBiochemistry · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCalmodulinResidualResidual dipolar couplingDipoleChemistryBiophysicsChemical physicsBiological systemComputational chemistryNuclear magnetic resonanceBiochemistryPhysicsBiologyEnzymeComputer scienceAlgorithmOrganic chemistry

Abstract

fetched live from OpenAlex

Calmodulin-regulated serine/threonine kinases (CaM kinases) play crucial roles in Ca2+-dependent signaling transduction pathways in eukaryotes. Despite having a similar overall molecular architecture of catalytic and regulatory domains, CaM kinases employ different binding modes for Ca2+/CaM recruitment which is required for their activation. Here we present a residual dipolar coupling (RDC)-based NMR approach to characterizing the molecular recognition of CaM with five different CaM kinases. Our analyses indicate that CaM kinase I and likely IV use the same CaM binding mode as myosin light chain kinase (1-14 motif), distinct from those of CaM kinase II (1-10 motif) and CaM kinase kinase (1-16- motif). This NMR approach provides an efficient experimental guide for homology modeling and structural characterization of CaM-target complexes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations33
Published2002
Admission routes1
Has abstractyes

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