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Record W2316085316 · doi:10.1021/bc500097q

Design of Heat Shock-Resistant Surfaces to Prevent Protein Aggregation: Enhanced Chaperone Activity of Immobilized α-Crystallin

2014· article· en· W2316085316 on OpenAlexaff
Namrata Ray, Sarita Roy, Santiswarup Singha, Bappaditya Chandra, Anjan Kr. Dasgupta, Amitabha Sarkar

Bibliographic record

VenueBioconjugate Chemistry · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsCalgary General HospitalUniversity of Calgary
Fundersnot available
KeywordsChemistryCrystallinHeat shock proteinChaperone (clinical)Protein aggregationBiophysicsBiochemistry

Abstract

fetched live from OpenAlex

α-Crystallin is a multimeric protein belonging to the family of small heat shock proteins, which function as molecular chaperones by resisting heat and oxidative stress induced aggregation of other proteins. We immobilized α-Crystallin on a self-assembled monolayer on glass surface and studied its activity in terms of the prevention of aggregation of aldolase. We discovered that playing with grafted protein density led to interesting variations in the chaperone activity of immobilized α-Crystallin. This result is in accordance with the hypothesis that dynamicity of subunits plays a vital role in the functioning of α-Crystallin and might be able to throw light on the structure-activity relationship. We showed that the chaperone activity of a certain number of immobilized α-Crystallins was superior compared to a solution containing an equivalent number of the protein and 10 times the number of the protein at temperatures >60 °C. The α-Crystallin grafted surfaces retained activity on reuse. This could also lead to the design of potent heat-shock resistant surfaces that can find wide applications in storage and shipping of protein based biopharmaceuticals.

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)0.001
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.268
Teacher spread0.253 · 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.

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
Published2014
Admission routes1
Has abstractyes

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