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. 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, incomplete trypsin digestion, and poor compatibility with detergent-based MS protocols. Recent progress has advanced the field along two complementary fronts. First, innovations in MS methodology, including native MS, nativeomics, solution-phase thermochemistry, and ion mobility-mass spectrometry (IM-MS), have improved the ability to preserve intact assemblies, capture co-bound lipids and ligands, and resolve conformational and energetic landscapes of MPs. Second, advances in MP solubilization and stabilization, through tailored detergent architectures, MS-compatible detergents, and membrane mimetic (MM) systems-such as nanodiscs, peptidiscs, and styrene-maleic acid (SMA) polymers-have created more native-like environments that maintain functional conformations and ligand-binding sites, enabling integration of MPs into high-throughput MS platforms for ligand screening. This review outlines key affinity- and stability-based MS approaches for MPs and highlights how advances in MS methodology and solubilization strategies are extending their scope, positioning MS and MM as an increasingly powerful platform for high-throughput discovery of MP-ligand interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".