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Record W2941710462 · doi:10.1021/acs.chemrev.8b00596

Reactions at Biomembrane Interfaces

2019· review· en· W2941710462 on OpenAlexafffund
Ana‐Nicoleta Bondar, M. Joanne Lemieux

Bibliographic record

VenueChemical Reviews · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Alberta
FundersInstitute of GeneticsCanadian Institutes of Health ResearchCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsChemistryMembraneBiological membraneEnergy landscapeMembrane biophysicsBiophysicsNanotechnologyBiochemistry

Abstract

fetched live from OpenAlex

Membranes surrounding the biological cell and its internal compartments host proteins that catalyze chemical reactions essential for the functioning of the cell. Rather than being a passive structural matrix that holds membrane-embedded proteins in place, the membrane can largely shape the conformational energy landscape of membrane proteins and impact the energetics of their chemical reaction. Here, we highlight the challenges in understanding how lipids impact the conformational energy landscape of macromolecular membrane complexes whose functioning involves chemical reactions including proton transfer. We review here advances in our understanding of how chemical reactions occur at membrane interfaces gleaned with both theoretical and experimental advances using simple protein systems as guides. Our perspective is that of bridging experiments with theory to understand general physicochemical principles of membrane reactions, with a long term goal of furthering our understanding of the role of the lipids on the functioning of complex macromolecular assemblies at the membrane interface.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.074
GPT teacher head0.368
Teacher spread0.294 · 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

Citations46
Published2019
Admission routes2
Has abstractyes

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