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Record W2809407504 · doi:10.1021/jacs.8b04412

Development of Endocyclic Control Elements for Peptide Macrocycles

2018· article· en· W2809407504 on OpenAlexafffund
Solomon D. Appavoo, Takuya Kaji, John R. Frost, Conor C. G. Scully, Andrei K. Yudin

Bibliographic record

VenueJournal of the American Chemical Society · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaJapan Society for the Promotion of ScienceCanadian Institutes of Health Research
KeywordsChemistryAmidePeptideMolecular dynamicsCyclic peptideAmine gas treatingStereochemistryMoleculeRing (chemistry)Combinatorial chemistryPeptide bondComputational chemistryOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Synthetic methods that provide control over macrocycle conformation represent valuable tools for the discovery of bioactive molecules. Incorporation of heterocycles into cyclic peptides may offer a way to stabilize their solution conformations. Herein, we used N-(isocyanimino)triphenylphosphorane (Pinc) to install an oxadiazole ring and an endocyclic amine into peptide macrocycles. To elucidate the conformational effect of this constellation of functionalities, we performed synthesis, variable temperature NMR analysis, and NOE-based molecular dynamics simulation of a range of macrocycles in DMSO. As part of this study, we conducted experiments to compare macrocycle conformation in aqueous and DMSO solutions. The obtained solution structures suggest that the reduced amide bond/heterocycle (RAH) motif can stabilize macrocycle conformations in both water and DMSO, which addresses an enduring challenge in molecular design. The conformational effect of the RAH was used in an effort to mimic the biologically relevant secondary structure of MAdCAM-1. This resulted in the discovery of a novel α4β7 integrin antagonist.

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 score0.030
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.008
GPT teacher head0.263
Teacher spread0.255 · 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

Citations58
Published2018
Admission routes2
Has abstractyes

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