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Record W2269150528 · doi:10.1021/acs.jctc.5b00668

Molecular Models of Nanodiscs

2015· article· en· W2269150528 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Chemical Theory and Computation · 2015
Typearticle
Languageen
FieldChemistry
TopicFullerene Chemistry and Applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceNanotechnologyData scienceComputational biologyBiologyMaterials science

Abstract

fetched live from OpenAlex

Nanodiscs are discoloidal protein-lipid particles that self-assemble from a mixture of lipids and membrane scaffold proteins. They form a highly soluble membrane mimetic that closely resembles a native-like lipid environment, unlike micelles. Nanodiscs are widely used for experimental studies of membrane proteins. In this work, we present a new method for building arbitrary nanodiscs using a combination of the Martini coarse-grained and all-atom force fields. We model the basic membrane scaffold protein MSP1 and its extended versions, such as MSP1E1 and MSP1E2, using a crystal structure of human apolipoprotein Apo-I. We test our method by generating nanodiscs of different sizes and compositions, including nanodiscs with embedded membrane proteins, such as bacteriorhodopsin, outer membrane protein X, and the glucose transporter. We show that properties of our nanodiscs are in general agreement with experimental data and previous computational studies.

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.

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 score0.270
Threshold uncertainty score0.218

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 head0.022
GPT teacher head0.267
Teacher spread0.245 · 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