Rotamer-Specific Potentials of Mean Force for Residue Pair Interactions
Bibliographic record
Abstract
We present an approach for the development of knowledge-based mean-force potentials for representing residue−residue interactions in proteins at the amino acid level of resolution which take into account the rotamer states of the residues. The rotamer-specific potentials which consist of an additional correction term to the current database-derived statistical potentials are expressed in terms of distance-dependent mean force potentials (MFP). To describe the relative geometry of two residues we used a set of six distances between different pairs of atoms. The distant-dependent MFPs are calculated from computer simulations of each pair of amino acids at the atomic level using the weighted-histogram analysis method. The umbrella sampling for each of 52003 different rotamer pairs was performed with a force-bias Monte Carlo algorithm using molecular mechanics potentials with statistically derived restraint terms. Our simulations show that the MFPs are very sensitive to the rotamer states of the amino acid residues. Preliminary tests of the potential's performance show that it can reproduce side-chain packing with reasonable accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".