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Record W2195989424 · doi:10.1007/s00249-015-1093-y

The styrene–maleic acid copolymer: a versatile tool in membrane research

2015· review· en· W2195989424 on OpenAlexfundno aff
Jonas M. Dörr, Stefan Scheidelaar, Martijn C. Koorengevel, Juan J. Dominguez, Marre Schäfer, Cornelis A. van Walree, J. Antoinette Killian

Bibliographic record

VenueEuropean Biophysics Journal · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsnot available
FundersSeventh Framework ProgrammeUniversity of TorontoNederlandse Organisatie voor Wetenschappelijk OnderzoekEuropean CommissionTechnische Universität KaiserslauternBusiness Events Perth
KeywordsMembraneMaleic acidCopolymerMembrane proteinAmphiphileStyreneChemistryLipid bilayerNanodiscPeripheral membrane proteinPolymerBiophysicsIntegral membrane proteinBiochemistryOrganic chemistryBiology

Abstract

fetched live from OpenAlex

A new and promising tool in membrane research is the detergent-free solubilization of membrane proteins by styrene-maleic acid copolymers (SMAs). These amphipathic molecules are able to solubilize lipid bilayers in the form of nanodiscs that are bounded by the polymer. Thus, membrane proteins can be directly extracted from cells in a water-soluble form while conserving a patch of native membrane around them. In this review article, we briefly discuss current methods of membrane protein solubilization and stabilization. We then zoom in on SMAs, describe their physico-chemical properties, and discuss their membrane-solubilizing effect. This is followed by an overview of studies in which SMA has been used to isolate and investigate membrane proteins. Finally, potential future applications of the methodology are discussed for structural and functional studies on membrane proteins in a near-native environment and for characterizing protein-lipid and protein-protein interactions.

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)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.085
GPT teacher head0.378
Teacher spread0.293 · 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

Citations416
Published2015
Admission routes1
Has abstractyes

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Same venueEuropean Biophysics JournalSame topicLipid Membrane Structure and BehaviorFrench-language works237,207