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Record W2014738804 · doi:10.1021/jf010839d

Engineering Nutritious Proteins:  Improvement of Stability in the Designer Protein MB-1 via Introduction of Disulfide Bridges

2001· article· en· W2014738804 on OpenAlexaff
Alain Doucet, Martin W. Williams, Mylène Gagnon, Maxime Sasseville, Marc Beauregard

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTransgenic Plants and Applications
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsMutantProtein foldingProtein engineeringCircular dichroismProtein designFolding (DSP implementation)Disulfide bondBiochemistryAmino acidChemistryMutationDenaturation (fissile materials)Protein structureBiophysicsBiologyEnzyme

Abstract

fetched live from OpenAlex

Protein design is currently used for the creation of new proteins with desirable traits. In this laboratory the focus has been on the synthesis of proteins with high essential amino acid content having potential applications in animal nutrition. One of the limitations faced in this endeavor is achieving stable proteins despite a highly biased amino acid content. Reported here are the synthesis and characterization of two disulfide-bridged mutants derived from the MB-1 designer protein. Both mutants outperformed their parent protein MB-1 with their bridge formed, as shown by circular dichroism, size exclusion chromatography, thermal denaturation, and proteolytic degradation experiments. When the disulfide bridges were cleaved, the mutants' behavior changed: the mutants significantly unfolded, suggesting that the introduction of Cys residues was deleterious to MB-1-folding. In an attempt to compensate for the mutations used, a Tyr62-Trp mutation was performed, leading to an increase in bulk and hydrophobicity in the core. The Trp-containing disulfide-bridged mutants did not behave as well as the original MB-1Trp, suggesting that position 62 might not be adequate for a compensatory mutation.

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 score0.018
Threshold uncertainty score0.156

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 head0.006
GPT teacher head0.182
Teacher spread0.176 · 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
Published2001
Admission routes1
Has abstractyes

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