Oxidative footprinting in the study of structure and function of membrane proteins: current state and perspectives
Bibliographic record
Abstract
Membrane proteins, such as receptors, transporters and ion channels, control the vast majority of cellular signalling and metabolite exchange processes and thus are becoming key pharmacological targets. Obtaining structural information by usage of traditional structural biology techniques is limited by the requirements for the protein samples to be highly pure and stable when handled in high concentrations and in non-native buffer systems, which is often difficult to achieve for membrane targets. Hence, there is a growing requirement for the use of hybrid, integrative approaches to study the dynamic and functional aspects of membrane proteins in physiologically relevant conditions. In recent years, significant progress has been made in the field of oxidative labelling techniques and in particular the X-ray radiolytic footprinting in combination with mass spectrometry (MS) (XF-MS), which provide residue-specific information on the solvent accessibility of proteins. In combination with both low- and high-resolution data from other structural biology approaches, it is capable of providing valuable insights into dynamics of membrane proteins, which have been difficult to obtain by other structural techniques, proving a highly complementary technique to address structure and function of membrane targets. XF-MS has demonstrated a unique capability for identification of structural waters and conformational changes in proteins at both a high degree of spatial and a high degree of temporal resolution. Here, we provide a perspective on the place of XF-MS among other structural biology methods and showcase some of the latest developments in its usage for studying water-mediated transmembrane (TM) signalling, ion transport and ligand-induced allosteric conformational changes in membrane proteins.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".