MétaCan
Menu
Back to cohort
Record W1988254516 · doi:10.1002/prot.21569

Surface recognition elements of membrane protein oligomerization

2007· article· en· W1988254516 on OpenAlexafffund
Arianna Rath, Charles M. Deber

Bibliographic record

VenueProteins Structure Function and Bioinformatics · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsProtein quaternary structureMembraneMembrane proteinMonomerStructural motifSequence (biology)Protein structurePeptide sequencePeripheral membrane proteinChemistryBiochemistryBiophysicsProtein subunitBiologyIntegral membrane protein

Abstract

fetched live from OpenAlex

Although certain membrane proteins are functional as monomeric polypeptides, others must assemble into oligomers to carry out their biological roles. High-resolution membrane protein structures provide a valuable resource for examining the sequence features that facilitate-or preclude-assembly of membrane protein monomers into multimeric structures. Here we have utilized a data set of 28 high-resolution alpha-helical membrane protein structures comprising 32 nonredundant polypeptides to address this issue. The lipid-exposed surfaces of membrane proteins that have reached their fully assembled and functional biological units have been compared with those of the individual subunits that build quaternary structures. Though the overall amino acid composition of each set of surfaces is similar, a key distinction-the distribution of small-xxx-small motifs-delineates subunits from membrane proteins that have reached a functioning oligomeric state. Quaternary structure formation may therefore be dictated by small-xxx-small motifs that are not satisfied by intrachain contacts.

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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations19
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueProteins Structure Function and BioinformaticsSame topicRNA and protein synthesis mechanismsFrench-language works237,207