Membrane mimetic thermal proteome profiling (MM-TPP) towards mapping membrane protein-ligand dynamics
Bibliographic record
Abstract
Abstract Integral membrane proteins (IMPs) remain the principal target of small-molecule therapeutics, and yet modalities towards probing on and off-target hits against this protein class in a robust, unbiased, and detergent-free manner remain starkly underdeveloped. Previously, we introduced the Peptidisc membrane mimetic (MM) for the water-soluble stabilization of the Escherichia coli membrane proteome and interactome (Carlson et al., 2019). Herein, we implement the Peptidisc into thermal proteome profiling (TPP), enabling for the first time a broad-scale level characterization of membrane protein-ligand interactions while completely circumventing structural perturbations invoked by detergents. Using a library prepared from the whole mouse liver, we determine the influence of ATP and orthovanadate on the thermal stability of IMPs, including pharmaceutically relevant ATP-binding cassette ABC transporters and G-protein coupled receptors. MM-TPP also detects thermal stability changes driven by ATP by-products, where non-canonical ATP binders can be validated with next-generation computational tools. MM-TPP thus offers a robust platform for identifying on- and off-target ligand effects, providing insights into the druggable membrane proteome and its stability as a consequence of changing and often dynamic small molecules.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".