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Record W2966719369 · doi:10.1002/chem.201901664

Targeting a Large Active Site: Structure‐Based Design of Nanomolar Inhibitors of <i>Trypanosoma brucei</i> Trypanothione Reductase

2019· article· en· W2966719369 on OpenAlexafffund
Raoul De Gasparo, Ondrej Halgas, D. Harangozo, Marcel Kaiser, E.F. Pai, R. Luise Krauth‐Siegel, François Diederich

Bibliographic record

VenueChemistry - A European Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsCanada Research ChairsUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer ResearchUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoDeutsche Forschungsgemeinschaft
KeywordsTrypanosoma bruceiActive siteIC50BiochemistryHEPESBinding siteEnzymeChemistryIn vivoIn vitroGlutathioneBiologyStereochemistry

Abstract

fetched live from OpenAlex

Abstract Trypanothione reductase (TR) plays a key role in the unique redox metabolism of trypanosomatids, the causative agents of human African trypanosomiasis (HAT), Chagas’ disease, and leishmaniases. Introduction of a new, lean propargylic vector to a known class of TR inhibitors resulted in the strongest reported competitive inhibitor of Trypanosoma ( T .) brucei TR, with an inhibition constant K i of 73 n m , which is fully selective against human glutathione reductase (hGR). The best ligands exhibited in vitro IC 50 values (half‐maximal inhibitory concentration) against the HAT pathogen, T. brucei rhodesiense , in the mid‐nanomolar range, reaching down to 50 n m. X‐Ray co‐crystal structures confirmed the binding mode of the ligands and revealed the presence of a HEPES buffer molecule in the large active site. Extension of the propargylic vector, guided by structure‐based design, to replace the HEPES buffer molecule should give inhibitors with low nanomolar K i and IC 50 values for in vivo studies.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.255
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 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

Citations19
Published2019
Admission routes2
Has abstractyes

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