Membrane-mimetic thermal proteome profiling (MM-TPP) toward mapping membrane protein–ligand dynamic interactions
Bibliographic record
Abstract
Integral membrane proteins (IMPs) are central targets for small-molecule therapeutics, yet robust, unbiased, and detergent-free approaches to assess their on- and off-target interactions remain limited. Previously, we introduced the Peptidisc membrane mimetic (MM) for water-soluble stabilization of the membrane proteome and interactome (Carlson et al., eLife, 2019). In this work, we combine the Peptidisc with thermal proteome profiling (TPP) to establish membrane-mimetic thermal proteome profiling (MM-TPP), a method that enables proteome-wide mapping of membrane protein–ligand interactions. Using a membrane protein library derived from mouse liver tissue, we detected the specific effects of ATP and orthovanadate on the thermal stability of ATP-binding cassette (ABC) transporters, as well as stability shifts driven by the hydrotropic effect of ATP and its by-products on G protein-coupled receptors (GPCRs). In contrast, detergent-based TPP (DB-TPP) with ATP–VO 4 failed to yield specific enrichment of ATP-binding proteins, underscoring the unique capacity of MM-TPP. To further validate the approach, we demonstrated the ability of MM-TPP to detect specific ligand-induced stabilization of cognate targets, exemplified by the selective thermal stabilization of the P2RY12 receptor by 2-methylthio-ADP. Together, these findings position MM-TPP as a robust platform for uncovering both on- and off-target effects of small molecules, providing insights into the druggable membrane proteome and its stability in consequence of changing dynamic ligands.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".