A prelude to building mathematical models for polypeptide folding: analysis on the conformational potential energy hypersurface cross-sections of <i>N</i>-acetyl-glycyl-glycine-<i>N</i>′-methylamide
Bibliographic record
Abstract
Finding a relationship on how a three-dimensional protein folds from its linear amino acid chain gets more complex with increasing chain length, so working on a smaller peptide conformational problem can provide initial ideas on what are the main molecular forces and how these influence the folding process. Following the study of conformations of amino acid units entering the proteins to understand the secondary structure of small peptides, this paper proposes mathematical models for the several two-rotor cross-sections of the five-dimensional N-acetyl-glycyl-glycine-N′-methylamide potential energy hypersurface (PEHS). These cross-sections are extracted along the first glycine subunit, with its coordinates fixed at the five energy minima of the glycine diamide. The resulting mathematical models yield an average RMSE of 1.36 kJ mol−1 and an average R2 of 0.9923 with respect to energy values obtained from DFT calculations. The minima geometries obtained from these models are also in good agreement with DFT-optimized energy minima conformers. An important aspect of this study also tackles the relationship between the PEHS of the glycyl-glycine diamide and its glycine subunits. It has been observed that there are deviations up to 28.35 kJ mol−1 and 29.52 kJ mol−1 between the PEHS cross-sections along γL and γD conformations, respectively, in the first glycine subunit. This may suggest that there are significant backbone–backbone intermolecular forces acting on the dipeptide. The abovementioned findings can help in developing more complex mathematical models for polypeptide folding from amino acid subunits.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".