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Record W1974137328 · doi:10.1073/pnas.1118005109

Entropy-driven binding of opioid peptides induces a large domain motion in human dipeptidyl peptidase III

2012· article· en· W1974137328 on OpenAlexafffund
G.A. Bezerra, E. Dobrovetsky, Roland Viertlmayr, Aiping Dong, Alexandra Binter, Marija Abramić, Peter Macheroux, Sirano Dhe‐Paganon, Karl Gruber

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersCanadian Institutes of Health ResearchWellcome Trust
KeywordsOpioid peptideDipeptidyl peptidaseEnkephalinEntropy (arrow of time)ChemistryPeptideEnzymeDipeptidyl peptidase-4OpioidBiochemistryBiophysicsStereochemistryComputational biologyReceptorBiologyPhysicsEndocrinology

Abstract

fetched live from OpenAlex

Opioid peptides are involved in various essential physiological processes, most notably nociception. Dipeptidyl peptidase III (DPP III) is one of the most important enkephalin-degrading enzymes associated with the mammalian pain modulatory system. Here we describe the X-ray structures of human DPP III and its complex with the opioid peptide tynorphin, which rationalize the enzyme's substrate specificity and reveal an exceptionally large domain motion upon ligand binding. Microcalorimetric analyses point at an entropy-dominated process, with the release of water molecules from the binding cleft ("entropy reservoir") as the major thermodynamic driving force. Our results provide the basis for the design of specific inhibitors that enable the elucidation of the exact role of DPP III and the exploration of its potential as a target of pain intervention strategies.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.057
GPT teacher head0.318
Teacher spread0.261 · 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

Citations70
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicNeuropeptides and Animal PhysiologyFrench-language works237,207