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Record W2769073299 · doi:10.1139/bcb-2017-0275

An enhanced chemoenzymatic method for loading substrates onto carrier protein domains

2017· article· en· W2769073299 on OpenAlexvenueno aff
Tiia Kittilä, Max J. Cryle

Bibliographic record

VenueBiochemistry and Cell Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsnot available
FundersMonash UniversityDeutsche ForschungsgemeinschaftDeutscher Akademischer Austauschdienst
KeywordsLinkerCoenzyme AEnzymeSubstrate (aquarium)PhosphataseBiochemistryTransferaseCofactorAdenylylationBiosynthesisChemistryPeptideCombinatorial chemistryBiologyReductaseComputer science

Abstract

fetched live from OpenAlex

Non-ribosomal peptide synthetase (NRPS) machineries produce many medically relevant peptides that cannot be easily accessed by chemical synthesis. Thus, understanding NRPS mechanism is of crucial importance to allow efficient redesign of these machineries to produce new compounds. During NRPS-mediated synthesis, substrates are covalently attached to peptidyl carrier proteins (PCPs), and studies of NRPSs are impeded by difficulties in producing PCPs loaded with substrates. Different approaches to load substrates onto PCP domains have been described, but all suffer from difficulties in either the complexity of chemical synthesis or low enzymatic efficiency. Here, we describe an enhanced chemoenzymatic loading method that combines 2 approaches into a single, highly efficient one-pot loading reaction. First, d-pantetheine and ATP are converted into dephospho-coenzyme A via the actions of 2 enzymes from coenzyme A (CoA) biosynthesis. Next, phosphoadenylates are dephosphorylated using alkaline phosphatase to allow linker attachment to PCP domain by Sfp mutant R4-4, which is inhibited by phosphoadenylates. This route does not depend on activity of the commonly problematic dephospho-CoA kinase and, therefore, offers an improved method for substrate loading onto PCP domains.

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 score0.005
Threshold uncertainty score0.793

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 head0.009
GPT teacher head0.280
Teacher spread0.271 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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