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Record W3172960179 · doi:10.3390/membranes11060451

Structures and Dynamics of Native-State Transmembrane Protein Targets and Bound Lipids

2021· review· en· W3172960179 on OpenAlexafffund
Michael Overduin, Catharine A. Trieber, R. Scott Prosser, Louis-Philippe Picard, Joey Sheff

Bibliographic record

VenueMembranes · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsNational Research Council CanadaUniversity of TorontoUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsNanodiscTransmembrane proteinBiophysicsMembrane proteinOrganelleDissipative particle dynamicsMembraneChemistryIntegral membrane proteinStructural biologyMembrane biologyBiochemistryBiologyPolymerReceptor

Abstract

fetched live from OpenAlex

Membrane proteins work within asymmetric bilayers of lipid molecules that are critical for their biological structures, dynamics and interactions. These properties are lost when detergents dislodge lipids, ligands and subunits, but are maintained in native nanodiscs formed using styrene maleic acid (SMA) and diisobutylene maleic acid (DIBMA) copolymers. These amphipathic polymers allow extraction of multicomponent complexes of post-translationally modified membrane-bound proteins directly from organ homogenates or membranes from diverse types of cells and organelles. Here, we review the structures and mechanisms of transmembrane targets and their interactions with lipids including phosphoinositides (PIs), as resolved using nanodisc systems and methods including cryo-electron microscopy (cryo-EM) and X-ray diffraction (XRD). We focus on therapeutic targets including several G protein-coupled receptors (GPCRs), as well as ion channels and transporters that are driving the development of next-generation native nanodiscs. The design of new synthetic polymers and complementary biophysical tools bodes well for the future of drug discovery and structural biology of native membrane:protein assemblies (memteins).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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 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

Citations18
Published2021
Admission routes2
Has abstractyes

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