Computed Binding of Peptides to Proteins with MELD-Accelerated Molecular Dynamics
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Bibliographic record
Abstract
It has been a challenge to compute the poses and affinities for binding of peptides to proteins by molecular dynamics (MD) simulations. Such computations would be valuable for capturing the physics and the conformational freedom of the molecules, but they are currently too computationally expensive. Here we describe using MELD (Modeling Employing Limited Data)-accelerated MD for finding the binding poses and approximate relative binding free energies for flexible-peptide/protein interactions. MELD uses only weak information about the binding motif and not the detailed binding mode that is typically required by other free-energy-based methods. We apply this technique to study binding of P53-derived peptides to MDM2 and MDMX. We find that MELD finds correct poses, that the binding induces the peptide into the correct helical conformation, and that it is capable of roughly estimating relative binding affinities. This method may be useful in peptide drug discovery.
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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) | 0.000 | 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 it