Assessing Energetic Contributions to Binding from a Disordered Region in a Protein−Protein Interaction,
Bibliographic record
Abstract
Many functional proteins are at least partially disordered prior to binding. Although the structural transitions upon binding of disordered protein regions can influence the affinity and specificity of protein complexes, their precise energetic contributions to binding are unknown. Here, we use a model protein-protein interaction system in which a locally disordered region has been modified by directed evolution to quantitatively assess the thermodynamic and structural contributions to binding of disorder-to-order transitions. Through X-ray structure determination of the protein binding partners before and after complex formation and isothermal titration calorimetry of the interactions, we observe a correlation between protein ordering and binding affinity for complexes along this affinity maturation pathway. Additionally, we show that discrepancies between observed and calculated heat capacities based on buried surface area changes in the protein complexes can be explained largely by heat capacity changes that would result solely from folding the locally disordered region. Previously developed algorithms for predicting binding energies of protein-protein interactions, however, are unable to correctly model the energetic contributions of the structural transitions in our model system. While this highlights the shortcomings of current computational methods in modeling conformational flexibility, it suggests that the experimental methods used here could provide training sets of molecular interactions for improving these algorithms and further rationalizing molecular recognition in protein-protein interactions.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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 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".