Side-chain rotamer changes upon ligand binding: common, crucial, correlate with entropy and rearrange hydrogen bonding
Bibliographic record
Abstract
MOTIVATION: Protein movements form a continuum from large domain rearrangements (including folding and restructuring) to side-chain rotamer changes and small rearrangements. Understanding side-chain flexibility upon binding is important to understand molecular recognition events and predict ligand binding. METHODS: In the present work, we developed a well-curated non-redundant dataset of 188 proteins in pairs of structures in the Apo (unbound) and Holo (bound) forms to study the extent and the factors that guide side-chain rotamer changes upon binding. RESULTS: Our analysis shows that side-chain rotamer changes are widespread with only 10% of binding sites displaying no conformational changes. Overall, at most five rotamer changes account for the observed movements in 90% of the cases. Furthermore, rotamer changes are essential in 32% of flexible binding sites. The different amino acids have a 11-fold difference in their probability to undergo changes. Side-chain flexibility represents an intrinsic property of amino acids as it correlates well with configurational entropy differences. Furthermore, on average b-factors and solvent accessible surface areas can discriminate flexible side-chains in the Apo form. Finally, there is a rearrangement of the hydrogen-bonding network upon binding primarily with a loss of H-bonds with water molecules and a gain of H-bonds with protein residues for flexible residues. Interestingly, only 25% of side chains capable of forming H-bonds do so with the ligand upon binding. In terms of drug design, this last result shows that there is a large number of potential interactions that may be exploited to modulate the specificity and sensitivity of inhibitors. CONTACT: rafael.najmanovich@usherbrooke.ca.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".