Expanding the Reach of Membrane Protein-Ligand Interaction Studies through the integration of Mass Spectrometry and Membrane Mimetics
Bibliographic record
Abstract
Mass spectrometry (MS) offers robust, label-free approaches for characterizing ligand-protein interactions through two main strategies: affinity-based and stability-based assays. Affinity-based methods, such as affinity-selection MS (AS-MS) and mass spectrometry binding assays (MSBA), detect ligand-target interactions by identifying bound ligands or measuring displacement of a reporter ligand. Stability-based techniques, including thermal proteome profiling (TPP) and limited proteolysis-MS (LiP-MS), infer interactions based on changes in a protein’s thermal or proteolytic stability. These MS-based workflows enable proteome-wide analyses with high specificity and throughput. However, their application to membrane proteins (MPs)—a major class of drug targets—has been limited by challenges such as structural complexity, low native expression, and poor compatibility with detergent-based MS protocols. Recent advances in membrane mimetic (MM) systems, including nanodiscs, Peptidiscs, and styrene-maleic acid (SMA) polymers, help address these barriers by maintaining native-like lipid environments and preserving functional MP conformations. These mimetics facilitate proteome-scale solubilization of MPs in forms compatible with MS screening. This review outlines key affinity- and stability-based MS approaches and examines their adaptation for MPs. It also highlights how combining MS techniques with MM systems is expanding the reach of high-resolution, functional analysis of MP–ligand interactions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".