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Record W2508402358 · doi:10.1002/bip.22939

<scp>N</scp>omenclature of homodetic cyclic peptides produced from ribosomal precursors: <scp>A</scp>n IUPAC task group interim report

2016· article· en· W2508402358 on OpenAlexaff
David J. Craik, Youn Young Shim, Ulf Göransson, Gerard P. Moss, Ning‐Hua Tan, Pramodkumar D. Jadhav, Jianheng Shen, Martin J. T. Reaney

Bibliographic record

VenueBiopolymers · 2016
Typearticle
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsGenome PrairieUniversity of Saskatchewan
FundersAustralian Research CouncilInternational Fund for Agricultural DevelopmentInternational Union of Pure and Applied Chemistry
KeywordsChemical nomenclatureInterimNumberingNomenclatureChemistryTask (project management)Task groupGroup (periodic table)AmideCyclic peptideStereochemistryComputer sciencePeptideBiochemistryOrganic chemistryEngineeringProgramming languageBiologyEngineering managementPolitical science

Abstract

fetched live from OpenAlex

In 2015, an International Union of Pure and Applied Chemistry (IUPAC) Task Group was formed to develop nomenclature recommendations for homodetic cyclic peptides produced from ribosomal precursors. Delegates of the 2015 International Conference on Circular Proteins (ICCP) were presented with the strengths and weaknesses of four published approaches to homodetic cyclic peptide nomenclature, and a summary of the ensuing discussion is presented here. This interim report presents a potentially novel suggestion-the use of Cahn-Ingold-Prelog rules to specify amino acid priority in homodetic peptides for consistent numbering. Indeed, this might be the first extension of the Cahn-Ingold-Prelog rules in five decades. The authors invite interested parties to contact the corresponding author with suggestions for the improvement of the proposed nomenclature; these ideas will be discussed and considered for inclusion in the final report.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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.

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

Citations7
Published2016
Admission routes1
Has abstractyes

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