Ligand hydration and bridging waters molecules
Bibliographic record
Abstract
<p>Water is the most abundant molecule in biological systems and is ubiquitous in nature. Despite this prevalence, water has long been considered a backdrop for bio-processes. However, with increasing sophistication in experimental and computational methods, water has now been recognized to interact and influence the behaviour of biomolecules in complex, subtle, and essential ways. The structure of water is unique in that it is small yet able to form multiple hydrogen bonded interactions. Water molecules are thus ideally suited to bridge interactions on biomolecular surfaces. Specifically, they have been identified to play an important role in mediating protein-ligand interactions. A methodology for the inclusion of these mediating waters has often been stated to be a major challenge facing the field of rational drug design. While notable successes exist in the design of better binders using water molecules, no single method has been able completely predict the locations of water molecules and its propensity to interact with the binding ligand.</p> <p>This thesis is intended to explore the possibilities of using the hydration of ligands as a pathway to study bridging water molecules in holo structures. To this end, a method of discretizing and scoring water-sites around ligands from their Molecular Dynamics (MD) simulation is first developed. This was then used to correlate the hydration shells of ligands with the locations of the bridging water molecules. Further, the hydration shell of ligands is used to improve WaterDock - a protocol to predict the locations of water molecules within binding sites. Finally, a comparison of MD is made with Empirical Potential Structure Refinement - a combined experimental and computational method that is often used to resolve the solvation structure of small molecules.</p>
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".