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Record W1933389414 · doi:10.1186/s12934-015-0315-4

Enhanced secretion of a methyl parathion hydrolase in Pichia pastoris using a combinational strategy

2015· article· en· W1933389414 on OpenAlexaff
Ping Wang, Lu Huang, Hu Lin Jiang, Jian Tian, Xiaoyu Chu, Ningfeng Wu

Bibliographic record

VenueMicrobial Cell Factories · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsBiotechnology Research Institute
FundersNational High-tech Research and Development ProgramNational Natural Science Foundation of China
KeywordsPichia pastorisSecretionHeterologousSecretory proteinSignal peptideBiochemistryEndoplasmic reticulumPichiaProtein precursorHydrolaseBiologyRecombinant DNAChemistryEnzymeGene

Abstract

fetched live from OpenAlex

BACKGROUND: Although Pichia pastoris has been successfully used to produce various recombinant heterologous proteins, the efficiency varies. In this study, we used methyl parathion hydrolase (MPH) from Ochrobactrum sp. M231 as an example to study the effect of protein amino acid sequence on secretion from P. pastoris. RESULTS: The results indicated that the protein N-terminal sequence, the endoplasmic reticulum (ER) retention signal (KKXX) at the protein C-terminus, and the acidic stability of the protein could affect its secretion from P. pastoris. Mutations designed based on these sequence features markedly improved secretion from P. pastoris. In addition, we found that the secretion properties of a protein can be cumulative when all of the above strategies are combined. The final mutant (CHBD-DQR) designed by combining all of the strategies greatly improved secretion and the secreted MPH activity of CHBD-DQR was enhanced up to 195-fold compared with wild-type MPH without loss of catalytic efficiency. CONCLUSIONS: These results demonstrate that the secretion of heterologous proteins from P. pastoris could be improved by combining changes in multiple protein sequence features.

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 score0.004
Threshold uncertainty score0.549

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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