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Record W4392355503 · doi:10.1016/j.isci.2024.109382

Capture of endogenous lipids in peptidiscs and effect on protein stability and activity

2024· article· en· W4392355503 on OpenAlexafffund
Rupinder Singh Jandu, Huaxu Yu, Zhiyu Zhao, Hai Tuong Le, Sehyeon Kim, Tao Huan

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsChemistryMembrane proteinProtein stabilityProtein aggregationFunction (biology)Nucleic acidBiophysicsBiochemistryLipid bilayerMembraneCell biologyBiology

Abstract

fetched live from OpenAlex

Compared to protein-protein and protein-nucleic acid interactions, our knowledge of protein-lipid interactions remains limited. This is primarily due to the inherent insolubility of membrane proteins (MPs) in aqueous solution. The traditional use of detergents to overcome the solubility barrier destabilizes MPs and strips away certain lipids that are increasingly recognized as crucial for protein function. Recently, membrane mimetics have been developed to circumvent the limitations. In this study, using the peptidisc, we find that MPs in different lipid states can be isolated based on protein purification and reconstitution methods, leading to observable effects on MP activity and stability. Peptidisc also enables re-incorporating specific lipids to fine-tune the protein microenvironment and assess the impact on downstream protein associations. This study offers a first look at the illusive protein-lipid interaction specificity, laying the path for a systematic evaluation of lipid identity and contributions to membrane protein function.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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 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

Citations15
Published2024
Admission routes2
Has abstractyes

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