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Record W3165421854 · doi:10.1002/anbr.202100028

There Is Plenty of Room in The Folded Globular Proteins: Tandem Modular Elastomeric Proteins Offer New Opportunities in Engineering Protein‐Based Biomaterials

2021· article· en· W3165421854 on OpenAlexafffund
Hongbin Li

Bibliographic record

VenueAdvanced NanoBiomed Research · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSelf-healing hydrogelsElastomerGlobular proteinMaterials scienceModular designNanotechnologyComputer scienceChemistryComposite materialPolymer chemistry

Abstract

fetched live from OpenAlex

Shock absorber‐like elastomeric (SAE) proteins have become attractive building blocks to engineer protein hydrogels with tailored mechanical properties. These elastomeric proteins are tandem modular proteins consisting of individually folded globular domains and exhibit distinct mechanical properties. In response to stretching, folded globular domains can undergo force‐induced unfolding, leading to a significant change in protein stiffness and energy dissipation. These molecular behaviors of individual proteins can be harnessed and translated into macroscopic mechanical traits of protein hydrogels when such SAE proteins are used as building blocks to engineer protein hydrogels. These SAE proteins offer unique opportunities to rationally design protein‐based hydrogels by programming the mechanical properties of individual proteins at the molecular level, and thus help bridge the gap between biomechanics of macroscopic biomaterials and nanomechanics of single molecules.

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)0.003
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.008
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.002

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 head0.057
GPT teacher head0.321
Teacher spread0.264 · 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
Published2021
Admission routes2
Has abstractyes

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