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Record W2103107485 · doi:10.1039/c4cs00048j

The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems

2014· review· en· W2103107485 on OpenAlexafffund
Fabio Sterpone, Simone Melchionna, Pierre Tufféry, Samuela Pasquali, Normand Mousseau, Tristan Cragnolini, Yassmine Chebaro, Jean‐François St‐Pierre, Maria Kalimeri, Alessandro Barducci, Yoann Laurin, Alex Tek, Marc Baaden, Phuong H. Nguyen, Philippe Derreumaux

Bibliographic record

VenueChemical Society Reviews · 2014
Typereview
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversité de Montréal
FundersGrand Équipement National De Calcul IntensifInstitut Universitaire de FranceCentre National de la Recherche ScientifiqueAlzheimer SocietyInfrastructures en Biologie Santé et AgronomieFudan UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversité Paris DiderotAgence Nationale de la RechercheNational Science Foundation
KeywordsAmyloid fibrilRNADNABiophysicsAmyloid (mycology)CrowdingChemistryComputational biologyNanotechnologyBiological systemAmyloid βMaterials scienceBiologyBiochemistryNeuroscienceMedicine

Abstract

fetched live from OpenAlex

The OPEP coarse-grained protein model has been applied to a wide range of applications since its first release 15 years ago. The model, which combines energetic and structural accuracy and chemical specificity, allows the study of single protein properties, DNA-RNA complexes, amyloid fibril formation and protein suspensions in a crowded environment. Here we first review the current state of the model and the most exciting applications using advanced conformational sampling methods. We then present the current limitations and a perspective on the ongoing developments.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.030
GPT teacher head0.274
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 designNot applicable
Domainnot available
GenreReview

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

Citations154
Published2014
Admission routes2
Has abstractyes

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