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Record W4400807094 · doi:10.1021/acs.jmedchem.4c00922

Rational <i>In Silico</i> Design of Selective TMPRSS6 Peptidomimetic Inhibitors via Exploitation of the S2 Subpocket

2024· article· en· W4400807094 on OpenAlexafffund
Michael Desgagné, Antoine Désilets, Sára Ferková, Matthieu Lepage, Olivier Perreault, Alexandre Joushomme, Gabriel Lemieux, Walid Guerrab, Ulrike Froehlich, Christian Comeau, Philippe Sarret, Richard Leduc, Pierre‐Luc Boudreault

Bibliographic record

VenueJournal of Medicinal Chemistry · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversité de Sherbrooke
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsPeptidomimeticIn silicoChemistryRational designComputational biologyCombinatorial chemistryStructure–activity relationshipStereochemistryPharmacologyBiochemistryPeptideIn vitroGeneticsGeneBiology

Abstract

fetched live from OpenAlex

TMPRSS6 is a potential therapeutic target for the treatment of iron overload due to its role in regulating levels of hepcidin. Although potent TMPRSS6 inhibitors have been previously developed, their lack of specificity requires optimization to avoid potential side effects before pursuing preclinical development with in vivo models. Here, using computer-aided drug design based on a TMPRSS6 homology model, we reveal that the S2 position of TMPRSS6 offers a potential avenue to achieve selectivity against other members of the TTSP family. Accordingly, we synthesized novel peptidomimetic molecules containing lipophilic amino acids at the P2 position to exploit this unexplored pocket. This enabled us to identify TMPRSS6-selective small molecules with low nanomolar affinity. Finally, pharmacokinetic parameters were determined, and a compound was found to be potent in cellulo toward its primary target while retaining TTSP-subtype selectivity and showing no signs of alteration in in vitro TEER experiments.

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)0.000
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 score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations9
Published2024
Admission routes2
Has abstractyes

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