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Record W4402678926 · doi:10.1088/2399-1984/ad7d7e

Walking by design: how to build artificial molecular motors made of proteins

2024· article· en· W4402678926 on OpenAlexafffund
Patrik Felix Felix Nilsson, Anna Zink, Olivier M.C Laprévote, Chapin S. Korosec, Nils Gustafsson, Neil O. Robertson, Nancy R. Forde, Ralf Eichhorn, Birte Höcker, Paul M. G. Curmi, Heiner Linke

Bibliographic record

VenueNano Futures · 2024
Typearticle
Languageen
FieldChemistry
TopicSupramolecular Chemistry and Complexes
Canadian institutionsSimon Fraser UniversityYork University
FundersNatural Sciences and Engineering Research Council of CanadaVetenskapsrådetElitenetzwerk BayernAustralian Research CouncilEuropean Commission
KeywordsMolecular motorEngineeringComputer scienceNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Abstract To design an artificial protein-based molecular motor that can autonomously step along a track is a key challenge of protein design and synthetic biology. We lay out a roadmap for how to achieve this aim, based on a modular approach that combines the use of natural, non-motor proteins with de novo design. We define what can be considered to constitute a successful artificial protein motor, identify key steps along the path to achieve these designs, and provide a vision for the future beyond this aim.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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