The phosphoinositide code is read by a plethora of protein domains
Bibliographic record
Abstract
INTRODUCTION: The proteins that decipher nucleic acid- and protein-based information are well known, however, those that read membrane-encoded information remain understudied. Here, we report 70 different human, microbial and viral protein folds that recognize phosphoinositides (PIs), comprising the readers of a vast membrane code. AREAS COVERED: Membrane recognition is best understood for FYVE, PH and PX domains, which exemplify hundreds of PI code readers. Comparable lipid interaction mechanisms may be mediated by kinases, adjacent C1 and C2 domains, trafficking arrestins, GAT and VHS modules, membrane-perturbing annexins, BAR, CHMP, ENTH, HEAT, syntaxin and Tubby helical bundles, multipurpose FERM, EH, MATH, PHD, PDZ, PROPPIN, PTB and SH2 domains, as well as systems that regulate receptors, GTPases and actin filaments, transfer lipids, and assemble bacterial and viral particles. EXPERT OPINION: The elucidation of how membranes are recognized has extended the genetic code to the PI code. Novel discoveries include PIP-stop and MET-stop residues to which phosphates and metabolites are attached to block phosphatidylinositol phosphate (PIP) recognition, memteins as functional membrane protein apparatuses and lipidons as lipid 'codons' recognized by membrane readers. At least 5% of the human proteome senses such membrane signals and allows eukaryotic organelles and pathogens to operate and replicate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".