Short-Range Contact Preferences and Long-Range Indifference: Is Protein Folding Stoichiometry Driven?
Bibliographic record
Abstract
Mittal et al. (1) recently advanced an unconventional view on protein folding. By analyzing the spatial neighborhoods of amino acid residues in an extensive set of structures in the Protein Data Bank (PDB), the authors concluded that preferential interactions between amino acid residues do not drive protein folding. In this connection, it should be noted that preferential interactions between amino acids are the basis for introducing knowledge-based potentials, which in turn provide the underpinning for present day three-dimensional protein structure prediction by modeling and simulation (2-5 and references therein). Instead of these preferential interactions, Mittal et al. indicate that “protein folding is a direct consequence of a narrow band of stoichiometric occurrences of amino-acids in the primary sequences” (1). According to the authors, this observation is akin to Chargaff’s discovery that the molar ratios of adenine and thymine and that of guanine and cytosine in DNA were not far from unity (6).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".