Molecular surface generation using a variable‐radius solvent probe
Bibliographic record
Abstract
Protein-ligand binding occurs through interactions at the molecular surface. Hence, a proper description of this surface is essential to our understanding of the process of molecular recognition. Recent studies have noted the inadequacy of using a fixed 1.4 A solvent probe radius to generate the molecular surface. This assumes that water molecules approach all surface atoms at an equal distance irrespective of polarity, which is not the case. To adequately model the protein-water boundary requires that the solvent probe radius change according to the polarity of its contacting atoms, smaller near polar atoms and larger near apolar atoms. To our knowledge, no method currently exists to generate the molecular surface of a protein in this manner. Using a modification of the marching tetrahedra algorithm, we present a method to generate molecular surfaces using a variable radius solvent probe. The resulting surface lacks many of the unrealistic small crevices in nonpolar regions that are found when utilizing an invariant 1.4 A solvent probe, while maintaining the fine detail of the surface at polar regions. On application of the method on a test set of 20 protein structures taken from the Protein Data Bank (PDB), we also find far fewer empty unsolvated cavities that are present when using only a 1.4 A solvent probe, while the majority of solvated and polar cavities is retained. This suggests that the majority of empty cavities previously observed in protein structures might simply be artifacts of the surfacing method. We also find that the variable probe surface can have significant effects on electrostatic calculations by generating a better tuned description of the protein-water boundary. We also examined the binding interfaces of a diverse set of 55 protein-protein complexes. We find that using a variable probe results in an increase in perceived shape complementarity at these sites compared to using a 1.4 A solvent probe. The molecular volume and surface area are geometric values that determine various important properties for macromolecules, and the altered description afforded by a variable solvent probe molecular surface can have significant implications in protein recognition, energetics, folding, and stability calculations.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".