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Record W4394688780 · doi:10.1101/2024.04.09.588654

Structure of Calcineurin bound to PI4KA reveals dual interface in both PI4KA and FAM126A

2024· preprint· en· W4394688780 on OpenAlexafffund
Alexandria L Shaw, Sushant Suresh, Matthew AH Parson, Noah J Harris, Meredith L. Jenkins, Calvin K. Yip, John E. Burke

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchBritish Columbia Knowledge Development FundMichael Smith Health Research BC
KeywordsCalcineurinDual (grammatical number)Interface (matter)Computer scienceParallel computingMedicineArtInternal medicineTransplantation

Abstract

fetched live from OpenAlex

Abstract Phosphatidylinositol 4-kinase alpha (PI4KA) maintains the PI4P and phosphatidylserine pools of the plasma membrane. A key regulator of PI4KA is its association into a complex with TTC7 and FAM126 proteins. This complex can be regulated by the CNAβ1 isoform of the phosphatase Calcineurin. We previously identified that CNAβ1 directly binds to FAM126A. Here, we report a cryo-EM structure of a truncated PI4KA complex bound to Calcineurin, revealing a direct Calcineurin interaction with PI4KA. Additional HDX-MS and computational analysis show that Calcineurin forms a complex with an evolutionarily conserved IKISVT sequence in PI4KA’s horn domain. We also characterised conserved LTLT and PSISIT Calcineurin binding sequences in the C-terminus of FAM126A. These sites are in close proximity to phosphorylation sites in the PI4KA complex, suggesting key roles of Calcineurin-regulated phosphosites in PI4KA regulation. This work reveals novel insight into how Calcineurin can regulate PI4KA activity.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0010.001
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 head0.010
GPT teacher head0.245
Teacher spread0.236 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicSignaling Pathways in DiseaseFrench-language works237,207