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Martini 3 Coarse-Grained Force Field for cholesterol

2023· preprint· en· W4386965784 on OpenAlexafffund
Luís Borges-Araújo, Ana C. Borges-Araújo, Tuǧba N. Öztürk, Daniel P. Ramirez-Echemendía, Balázs Fábián, Timothy S. Carpenter, Sebastian Thallmair, Jonathan Barnoud, Helgi I. Ingólfsson, Gerhard Hummer, D. Peter Tieleman, ‪Siewert J. Marrink, Paulo C. T. Souza, Manuel N. Melo

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Calgary
FundersLawrence Livermore National LaboratoryAlliance de recherche numérique du CanadaCentre National de la Recherche ScientifiqueNational Supercomputing Center, Korea Institute of Science and Technology InformationGrand Équipement National De Calcul IntensifMax-Planck-GesellschaftCanada Research ChairsLaboratory Directed Research and DevelopmentFundação para a Ciência e a TecnologiaU.S. Department of Energy
KeywordsRigidity (electromagnetism)Force balanceCholesterolPermeability (electromagnetism)Force field (fiction)NanotechnologyLipid bilayerChemistryComputer scienceBiophysicsMaterials scienceMembraneMechanicsComposite materialPhysicsBiochemistryArtificial intelligenceBiology

Abstract

fetched live from OpenAlex

Cholesterol plays a crucial role in biomembranes by regulating various properties such as fluidity, rigidity, permeability, and organization of lipid bilayers. The latest version of the Martini model, Martini 3, offers significant improvements in interaction balance, molecular packing, and the inclusion of new bead types and sizes. However, the release of the new model resulted in the need to re-parameterize many core molecules, including cholesterol. Here, we describe the development and validation of a Martini 3 cholesterol model, addressing issues related to its bonded setup, shape, volume and hydrophobicity. The proposed model mitigates some limitations of its Martini 2 predecessor while maintaining or improving overall behavior.

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)0.000
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 score0.022
Threshold uncertainty score1.000

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 integrity0.0010.000
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 head0.027
GPT teacher head0.294
Teacher spread0.267 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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