Transmembrane Membrane Readers form a Novel Class of Proteins That Include Peripheral Phosphoinositide Recognition Domains and Viral Spikes
Bibliographic record
Abstract
Membrane proteins are broadly classified as transmembrane (TM) or peripheral, with functions that pertain to only a single bilayer at a given time. Here, we explicate a class of proteins that contain both transmembrane and peripheral domains, which we dub transmembrane membrane readers (TMMRs). Their transmembrane and peripheral elements anchor them to one bilayer and reversibly attach them to another section of bilayer, respectively, positioning them to tether and fuse membranes while recognizing signals such as phosphoinositides (PIs) and modifying lipid chemistries in proximity to their transmembrane domains. Here, we analyze full-length models from AlphaFold2 and Rosetta, as well as structures from nuclear magnetic resonance (NMR) spectroscopy and X-ray crystallography, using the Membrane Optimal Docking Area (MODA) program to map their membrane-binding surfaces. Eukaryotic TMMRs include phospholipid-binding C1, C2, CRAL-TRIO, FYVE, GRAM, GTPase, MATH, PDZ, PH, PX, SMP, StART and WD domains within proteins including protrudin, sorting nexins and synaptotagmins. The spike proteins of SARS-CoV-2 as well as other viruses are also TMMRs, seeing as they are anchored into the viral membrane while mediating fusion with host cell membranes. As such, TMMRs have key roles in cell biology and membrane trafficking, and include drug targets for diseases such as COVID-19.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".