MétaCan
Menu
Back to cohort
Record W2002195651 · doi:10.1007/s10295-015-1613-x

Investigation into the misincorporation of norleucine into a recombinant protein vaccine candidate

2015· article· en· W2002195651 on OpenAlexaff
Joyce Ni, Meg Gao, Andrew M. James, Jiansheng Yao, Tao Yuan, Bruce Carpick, Tony D’Amore, Paul J. Farrell

Bibliographic record

VenueJournal of Industrial Microbiology & Biotechnology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsSanofi (Canada)
FundersSanofi Pasteur
KeywordsNorleucineRecombinant DNABiologyMicrobiologyVirologyBiochemistryGene

Abstract

fetched live from OpenAlex

A high level of norleucine misincorporation was detected in a recombinant methionine-rich protein vaccine candidate expressed in E. coli K12. An investigation was conducted to evaluate a simple remediation strategy to reduce norleucine misincorporation and to determine if the phenomenon was either (a) due to the depletion of methionine during fermentation, (b) a result of the cultivation environment, or (c) a strain-specific effect. While supplementation with exogenous methionine improved product quality, the undesirable biosynthesis of non-standard amino acids such as norleucine and norvaline persisted. In contrast, non-standard amino acid biosynthesis was quickly minimized upon selection of an appropriate fed-batch process control strategy, fermentation medium, and nutrient feed. By expressing the same protein in E. coli BL21(DE3), it was determined that the biosynthesis of norleucine and norvaline, and the misincorporation of norleucine into the protein were primarily attributed to the use of E. coli K12 as the host for protein expression.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Industrial Microbiology & BiotechnologySame topicViral Infectious Diseases and Gene Expression in InsectsFrench-language works237,207