Effects of Protein–Ligand Interactions on Hydrogen/Deuterium Exchange Kinetics: Canonical and Noncanonical Scenarios
Bibliographic record
Abstract
Hydrogen/deuterium exchange (HDX) methods are widely used for monitoring protein-ligand interactions. This approach relies on the fact that ligand binding can modulate the extent of protein structural fluctuations that transiently disrupt hydrogen bonds and expose backbone amides to the solvent. It is commonly observed that ligand binding causes a reduction of HDX rates. This reduction can be restricted to elements adjacent to the binding site, but other regions can be affected as well. Qualitatively, ligand-induced HDX protection can be rationalized on the basis of two-state models that equate structural dynamics with global unfolding/refolding. Unfortunately, such models tend to be unrealistic because the dynamics of native proteins are dominated by subglobal transitions and local fluctuations. Ligand binding lowers the ground-state free energy. It is not obvious why this should necessarily be accompanied by a depletion of excited-state occupancies, which would be required for a reduction of HDX rates. Here, we propose a framework that implies that ligand binding can either slow or accelerate amide deuteration throughout the protein. These scenarios are referred to as "type 1" and "type 2", respectively. Evidence for type 1 binding is abundant in the literature, whereas the viability of type 2 interactions is less clear. Using HDX mass spectrometry (MS), we demonstrate that the oxygenation of hemoglobin (Hb) provides a dramatic example of a type 2 scenario. The observed behavior is consistent with cooperative T → R switching, where part of the intrinsic O2 binding energy is reinvested for destabilization of the ground state. This destabilization increases the Boltzmann occupancy of unfolded conformers, thereby enhancing HDX rates. Surprisingly, O2 binding to myoglobin (Mb) also induces elevated HDX rates. These Mb data reveal that type 2 behavior is not limited to cooperative multisubunit systems. Although enhanced protection from deuteration is widely considered to be a hallmark of protein-ligand interactions, this work establishes that an overall deuteration increase also represents a viable outcome. HDX-based ligand screening assays, therefore, have to allow for canonical as well as noncanonical effects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".