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Record W2800041442 · doi:10.1021/acs.jpca.8b02523

Revealing the Conformational Preferences of Proteinogenic Glutamic Acid Derivatives in Solution by <sup>1</sup>H NMR Spectroscopy and Theoretical Calculations

2018· article· en· W2800041442 on OpenAlexaff
Weslley G. D. P. Silva, Cláudio F. Tormena, Roberto Rittner

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2018
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsUniversity of Manitoba
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsNatural bond orbitalChemistryConformational isomerismSteric effectsPolarizable continuum modelComputational chemistryNuclear magnetic resonance spectroscopyCovalent bondSolvent effectsMoleculeSpectroscopyStereochemistryCrystallographySolventDensity functional theoryOrganic chemistry

Abstract

fetched live from OpenAlex

The conformational preferences of proteinogenic glutamic acid esterified (GluOMe) and N-acetylated (AcGluOMe) derivatives have been determined in solution for the first time. Theoretical calculations at the ωB97X-D/aug-cc-pVTZ made possible the assignment of six and eight stable conformers for GluOMe and AcGluOMe, respectively. The conformational equilibrium of the studied compounds was evaluated in different organic solvents using a combination of the integral equation formalism polarizable continuum model (IEF-PCM) and 1H NMR spectroscopy data. The results showed that the conformational equilibrium of both derivatives change in the presence of solvent. According to the quantum theory of atoms in molecules (QTAIM), non-covalent interactions (NCI), and natural bond orbitals (NBO) analyses, the conformational preferences observed for GluOMe and AcGluOMe are not dictated by the presence of a specific interaction but are due to a combination of hyperconjugative and steric effects.

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 score0.014
Threshold uncertainty score0.283

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 studies0.0000.001
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 head0.254
Teacher spread0.247 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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