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Record W1992985355 · doi:10.1002/cbic.201300700

Using <sup>15</sup>N‐Ammonium to Characterise and Map Potassium Binding Sites in Proteins by NMR Spectroscopy

2014· article· en· W1992985355 on OpenAlexfundno aff
Nicolas D. Werbeck, John Kirkpatrick, Jochen Reinstein, D. Flemming Hansen

Bibliographic record

VenueChemBioChem · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Molecular Research
Canadian institutionsnot available
FundersMedical Research CouncilBiotechnology and Biological Sciences Research CouncilDirectorate for Biological SciencesUniversity of TorontoUniversity College London
KeywordsChemistryPotassiumHeteronuclear moleculeBinding siteNuclear magnetic resonance spectroscopyEnzymeAmmoniumActive siteStereochemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A variety of enzymes are activated by the binding of potassium ions. The potassium binding sites of these enzymes are very specific, but ammonium ions can often replace potassium ions in vitro because of their similar ionic radii. In these cases, ammonium can be used as a proxy for potassium to characterise potassium binding sites in enzymes: the (1) H,(15) N spin-pair of enzyme-bound (15) NH4 (+) can be probed by (15) N-edited heteronuclear NMR experiments. Here, we demonstrate the use of NMR spectroscopy to characterise binding of ammonium ions to two different enzymes: human histone deacetylase 8 (HDAC8), which is activated allosterically by potassium, and the bacterial Hsp70 homologue DnaK, for which potassium is an integral part of the active site. Ammonium activates both enzymes in a similar way to potassium, thus supporting this non-invasive approach. Furthermore, we present an approach to map the observed binding site onto the structure of HDAC8. Our method for mapping the binding site is general and does not require chemical shift assignment of the enzyme resonances.

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 categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

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 head0.014
GPT teacher head0.273
Teacher spread0.258 · 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.

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

Citations25
Published2014
Admission routes1
Has abstractyes

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