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Record W4376127368 · doi:10.1021/acsomega.3c01764

Computational Insights into the Formation and Structure of S–N Containing Cyclic Peptides

2023· article· en· W4376127368 on OpenAlexafffund
Sedat Karabulut, Dananjana V. Wijerathne, James W. Gauld

Bibliographic record

VenueACS Omega · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCyclic peptideChemistryPeptideBiochemistry

Abstract

fetched live from OpenAlex

Cyclic peptides are known to have biologically important roles and may also be applicable to the pharmaceutical and other industries. Furthermore, thiols and amines, which are found throughout biological systems, can react to form S-N bonds and to date, ∼100 biomolecules containing such a bond have been identified. However, while there are in principle numerous S-N containing peptide-derived rings possible, only a few are presently known to occur in biochemical systems. Density functional theory-based calculations have been used to consider the formation and structure of S-N containing cyclic peptides from systematic series of linear peptides in which a cysteinyl has first been oxidized to a sulfenic or sulfonic acid. In addition, the possible effect of the cysteine's vicinal residue on the free energy of formation has also been considered. In general, when the cysteine is first oxidized to a sulfenic acid, only the formation of smaller S-N containing rings is calculated to be exergonic in aqueous solution. In contrast, when the cysteine is first oxidized to a sulfonic acid, the formation of all rings considered (with one exception) is calculated to be endergonic in aqueous solution. The nature of vicinal residue can influence ring formation through stabilizing or destabilizing intramolecular interactions.

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 score0.023
Threshold uncertainty score0.115

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 head0.007
GPT teacher head0.233
Teacher spread0.227 · 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
Published2023
Admission routes2
Has abstractyes

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