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

Molecular Models of Nanodiscs

2015· article· en· W2269150528 on OpenAlexafffund
Iwona Siuda, D. Peter Tieleman

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.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
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

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 designSimulation or modeling
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

Citations47
Published2015
Admission routes2
Has abstractyes

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