Use of High Pressure NMR Spectroscopy to Rapidly Identify Proteins with Internal Ligand-Binding Voids
Bibliographic record
Abstract
Abstract Small molecule binding within internal cavities provides a way to control protein function and structure, as exhibited in numerous natural and artificial settings. Unfortunately, most ways to identify suitable cavities require high-resolution structures a priori and may miss potential sites. Here we address this limitation via high-pressure solution NMR spectroscopy, taking advantage of the distinctive nonlinear pressure-induced chemical shift changes observed in proteins containing internal cavities and voids. We developed a method to rapidly characterize such nonlinearity among backbone 1 H and 15 N amide signals without needing to have sequence-specific chemical shift assignments, taking advantage of routinely available 15 N-labeled samples, instrumentation, and 2D 1 H/ 15 N HSQC experiments. From such data, we find a strong correlation in the site-to-site variability in such nonlinearity with the total void volume within proteins, providing insights useful for prioritizing domains for ligand binding and indicating mode-of-action among such protein/ligand systems. We suggest that this experimental approach is a rapid and useful probe of otherwise hidden dynamic architectures of proteins, providing novel insights and opportunities into ligand binding and control. Significance Statement Many proteins can be regulated by internally binding small molecule ligands, but it is often not clear a priori which proteins are controllable in such a way. Here we describe a rapid method to address this challenge, using solution NMR spectroscopy to monitor the response of proteins to the application of high pressure. While the locations of NMR signals from many proteins respond to high pressure with linear chemical shift changes, proteins containing internal cavities that can bind small molecule ligands respond with easily identified non-linear changes. We demonstrate this approach on several proteins and protein/ligand complexes, suggesting that it has general utility.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".