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Record W2329559969 · doi:10.1021/cb300187t

Quantitative Synthesis of Genetically Encoded Glycopeptide Libraries Displayed on M13 Phage

2012· article· en· W2329559969 on OpenAlexaff
Simon Ng, Mohammad Reza Jafari, Wadim L. Matochko, Ratmir Derda

Bibliographic record

VenueACS Chemical Biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsAlberta Glycomics CentreUniversity of Alberta
Fundersnot available
KeywordsPhage displayGlycopeptideCombinatorial chemistryPhagemidPeptide libraryMoietyPeptideChemistryComputational biologyBiologyBiochemistryPeptide sequenceBacteriophageGeneStereochemistryEscherichia coli

Abstract

fetched live from OpenAlex

Phage display is a powerful technology that enables the discovery of peptide ligands for many targets. Chemical modification of phage libraries have allowed the identification of ligands with properties not encountered in natural polypeptides. In this report, we demonstrated the synthesis of 2 × 10(8) genetically encoded glycopeptides from a commercially available phage-displayed peptide library (Ph.D.-7) in a two-step, one-pot reaction in <1.5 h. Unlike previous reports, we bypassed genetic engineering of phage. The glycan moiety was introduced via an oxime ligation following oxidation of an N-terminal Ser/Thr; these residues are present in the peptide libraries at 20-30% abundance. The construction of libraries was facilitated by simple characterization, which directly assessed the yield and regioselectivity of chemical reactions performed on phage. This quantification method also allowed facile yield determination of reactions in 10(9) distinct molecules. We envision that the methodology described herein will find broad application in the synthesis of custom chemically modified phage libraries.

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)0.001
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 score0.008
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.021
GPT teacher head0.303
Teacher spread0.281 · 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

Citations102
Published2012
Admission routes1
Has abstractyes

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