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Record W4313288624 · doi:10.1021/acs.jmedchem.2c01803

Development of a Potent Cyclic Peptide Inhibitor of the nNOS/PSD-95 Interaction

2022· article· en· W4313288624 on OpenAlexfundno aff
Javier Rodríguez, Dominik J. Essig, Sana Ma, Nichlas Karer, Louise S. Clemmensen, Søren W. Pedersen, Andreas C. Joerger, Stefan Knapp, Søren Østergaard, Kristian Strømgaard

Bibliographic record

VenueJournal of Medicinal Chemistry · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsnot available
FundersJanssen BiotechH2020 Marie Skłodowska-Curie ActionsGenentechDeutsche ForschungsgemeinschaftOntario Genomics InstituteTakeda Pharmaceutical CompanyEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAOntario GenomicsGenome CanadaBayerBristol-Myers SquibbPfizerMcGill UniversityBoehringer Ingelheim
KeywordsChemistryPDZ domainPeptidePostsynaptic densityNMDA receptorCyclic peptidePeptide ConformationAmino acidBiophysicsBiochemistryReceptor

Abstract

fetched live from OpenAlex

-methyl-d-aspartate receptor (NMDAR), neuronal nitric oxide synthase (nNOS), and the postsynaptic density protein-95 (PSD-95) is an attractive therapeutic target for the treatment of acute ischemic stroke. The complex is formed via the PDZ protein domains of PSD-95, and efforts to disrupt the complex have generally been based on C-terminal peptides derived from the NMDAR. However, nNOS binds PSD-95 through a β-hairpin motif, providing an alternative starting point for developing PSD-95 inhibitors. Here, we designed a cyclic nNOS β-hairpin mimetic peptide and generated cyclic nNOS β-hairpin peptide arrays with natural and unnatural amino acids (AAs), which provided molecular insights into this interaction. We then optimized cyclic peptides and identified a potent inhibitor of the nNOS/PSD-95 interaction, with the highest affinity reported thus far for a peptide macrocycle inhibitor of PDZ domains, which serves as a template for the development of treatment for acute ischemic stroke.

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 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 score0.011
Threshold uncertainty score0.305

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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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