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Record W1972506884 · doi:10.1021/jp4123425

Binding of the Antitubercular Pro-Drug Isoniazid in the Heme Access Channel of Catalase-Peroxidase (KatG). A Combined Structural and Metadynamics Investigation

2014· article· en· W1972506884 on OpenAlexaff
Pietro Vidossich, P.C. Loewen, X. Carpena, Giacomo Fiorin, Ignacio Fita, Carme Rovira

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Manitoba
FundersBarcelona Supercomputing Center
KeywordsMetadynamicsChemistryHemeActive siteIsoniazidIsonicotinic acidBinding siteStereochemistryHydrazideDocking (animal)Molecular dynamicsBiochemistryEnzymeComputational chemistryOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

Isonicotinic acid hydrazide (isoniazid or INH) is a front line antitubercular pro-drug that is converted to its active form, isonicotinyl-NAD, by the bacterial catalase-peroxidase KatG. Understanding the role of KatG in the INH activation process has been hampered by a lack of knowledge of the actual drug binding site. In this work, we have investigated the binding of INH in the main access channel of KatG with a combination of molecular dynamics, using an enhanced-sampling technique (metadynamics), X-ray crystallography, and site-directed mutagenesis. The metadynamics simulations show that there are several weak drug binding sites along the access channel. Moreover, the simulations evidence that complete entrance to the heme active site is impeded by an aspartate residue (D141) located above the heme. This has been confirmed by structural and functional analysis of the D141A mutant, leading to the first X-ray crystallography evidence of INH at the heme access channel.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

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 integrity0.0000.001
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 head0.009
GPT teacher head0.248
Teacher spread0.239 · 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 designSimulation or modeling
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

Citations28
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry BSame topicAntibiotic Resistance in BacteriaFrench-language works237,207