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Record W2903910853 · doi:10.1021/acs.jmedchem.8b01310

Hydroxamic Acid Inhibitors Provide Cross-Species Inhibition of <i>Plasmodium</i> M1 and M17 Aminopeptidases

2018· article· en· W2903910853 on OpenAlexafffund
Natalie B. Vinh, Nyssa Drinkwater, Tess R. Malcolm, Michael Kassiou, Leonardo Lucantoni, Peter M. Grin, Georgina S. Butler, Sandra Duffy, Christopher M. Overall, Vicky M. Avery, Peter J. Scammells, Sheena McGowan

Bibliographic record

VenueJournal of Medicinal Chemistry · 2018
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsUniversity of British Columbia
FundersNational Health and Medical Research CouncilCanadian Institutes of Health ResearchCanada Foundation for Innovation
KeywordsHydroxamic acidPlasmodium falciparumChemistryEnzymeBiochemistryPlasmodium vivaxAntimalarial AgentMolecular modelStereochemistryCombinatorial chemistryMalariaBiology

Abstract

fetched live from OpenAlex

There is an urgent clinical need for antimalarial compounds that target malaria caused by both Plasmodium falciparum and Plasmodium vivax. The M1 and M17 metalloexopeptidases play key roles in Plasmodium hemoglobin digestion and are validated drug targets. We used a multitarget strategy to rationally design inhibitors capable of potent inhibition of the M1 and M17 aminopeptidases from both P. falciparum ( Pf-M1 and Pf-M17) and P. vivax ( Pv-M1 and Pv-M17). The novel chemical series contains a hydroxamic acid zinc binding group to coordinate catalytic zinc ion/s, and a variety of hydrophobic groups to probe the S1' pockets of the four target enzymes. Structural characterization by cocrystallization showed that selected compounds utilize new and unexpected binding modes; most notably, compounds substituted with bulky hydrophobic substituents displace the Pf-M17 catalytic zinc ion. Excitingly, key compounds of the series potently inhibit all four molecular targets and show antimalarial activity comparable to current clinical candidates.

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 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 score0.005
Threshold uncertainty score0.682

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 studies0.0000.001
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 head0.013
GPT teacher head0.273
Teacher spread0.260 · 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.

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

Citations51
Published2018
Admission routes2
Has abstractyes

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