Stability issues of covalently and noncovalently bonded peptide subunits
Bibliographic record
Abstract
The present study focuses on important questions associated with modeling of peptide and protein stability. Computing at different levels of theory (RHF, B3LYP) for a representative ensemble of conformers of di- and tripeptides of alanine, we found that the Gibbs Free Energy values correlate significantly with the total electronic energy of the molecules (0.922 < or = R2). For noncovalently attached but interacting peptide subunits, such as [For-NH2]2 or [For-L-Ala-NH2]2, we have found, as expected, that the basis set superimposition error (BSSE) is large in magnitude for small basis set but significantly smaller when larger basis sets [e.g., B3LYP/6-311++G(d,p)] are used. Stability of the two hydrogen bonds of antiparallel beta-pleated sheets were quantitatively determined as a function of the molecular structure, S10 and S14, computed as 4.0 +/- 0.5 and 8.1 +/- 1.1 kcal/mol, respectively. Finally, a suitable thermoneutral isodesmic reaction was introduced to scale both covalently and noncovalently attached peptide units onto a common stability scale. We found that a suitable isodesmic reaction can result in the total electronic energy as well as the Gibbs free energy of a molecule, from its "noninteracting" fragments, as accurate as a few tenths of a kcal per mol. The latter observation seems to hold for peptides regardless of their length (1 < or = n < or = 8) or the level of theory applied.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".