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Record W2053698356 · doi:10.1021/ja910544p

Macrocyclization of Linear Peptides Enabled by Amphoteric Molecules

2010· article· en· W2053698356 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Chemical Society · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChemistryEpimerElectrophileNucleophileCyclic peptideAmino acidMoleculeCombinatorial chemistryNucleophilic additionPeptideStereochemistryOrganic chemistryBiochemistryCatalysis

Abstract

fetched live from OpenAlex

There has been enormous interest in both naturally occurring and synthetic cyclic peptides as scaffolds that preorganize a given amino acid sequence into a rigid conformation. Such molecules have been employed as nanomaterials, imaging agents, and therapeutics. Unfortunately, the laboratory synthesis of cyclic peptides directly from linear precursors is afflicted by several thermodynamic and kinetic challenges, resulting in low chemical yields and poor chemo- and stereoselectivities. Here we report that amphoteric amino aldehydes can be used for efficient syntheses of cyclic peptides in high yields and selectivities starting from alpha-amino acids or linear peptides. The cyclizations effectively operate at unusually high molar concentrations (0.2 M), while side processes such as epimerization and cyclodimerization are not observed. The products are equipped with sites that allow for a highly specific, late-stage structural modification. The overall efficiency of the macrocyclization is due to the coexistence of nucleophilic and electrophilic reaction centers in amphoteric amino aldehydes.

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.

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 score0.003
Threshold uncertainty score0.273

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 head0.003
GPT teacher head0.226
Teacher spread0.223 · 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