From dipeptide systems to polypeptides: evolution of mutual information.
Bibliographic record
Abstract
Understanding the electronic structure of amino acids is crucial to understanding protein stability, folding mechanisms, and molecular interactions. In this study, we introduce fragment-wise mutual information (FMI) as a quantum information-based tool to quantify interatomic correlations in peptides. By extending mutual information (MI) analysis to amino acid fragments, FMI provides a detailed map of electronic interactions beyond classical descriptors, such as van der Waals forces. We first validated FMI on 400 dipeptides, demonstrating a correlation with the atomization and bonding energies. Expanding this approach to the 10-mer Neh2 peptide, we analyze molecular dynamics (MD) simulations and reveal how interatomic correlations evolve during folding. Our results show that FMI distinguishes stabilizing interactions such as salt bridges and variable hydrogen-bond strengths, providing deeper insight into peptide stability. These findings suggest that FMI could enhance molecular modeling and force-field development by incorporating quantum electronic effects into biomolecular analysis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".