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Record W4310466682 · doi:10.1093/protein/gzac010

Engineering the enzyme toolbox to tailor glycosylation in small molecule natural products and protein biologics

2022· review· en· W4310466682 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProtein Engineering Design and Selection · 2022
Typereview
Languageen
FieldChemistry
TopicCarbohydrate Chemistry and Synthesis
Canadian institutionsUniversity of AlbertaConcordia UniversityPROTEO
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGlycosylationProtein engineeringRational designEnzymeBiochemistryGlycoproteinChemistryComputational biologyDirected evolutionCombinatorial chemistryBiology

Abstract

fetched live from OpenAlex

Many glycosylated small molecule natural products and glycoprotein biologics are important in a broad range of therapeutic and industrial applications. The sugar moieties that decorate these compounds often show a profound impact on their biological functions, thus biocatalytic methods for controlling their glycosylation are valuable. Enzymes from nature are useful tools to tailor bioproduct glycosylation but these sometimes have limitations in their catalytic efficiency, substrate specificity, regiospecificity, stereospecificity, or stability. Enzyme engineering strategies such as directed evolution or semi-rational and rational design have addressed some of the challenges presented by these limitations. In this review, we highlight some of the recent research on engineering enzymes to tailor the glycosylation of small molecule natural products (including alkaloids, terpenoids, polyketides, and peptides), as well as the glycosylation of protein biologics (including hormones, enzyme-replacement therapies, enzyme inhibitors, vaccines, and antibodies).

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.219
Teacher spread0.187 · 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