Bioinformatical parsing of folding-on-binding proteins reveals their compositional and evolutionary sequence design
Bibliographic record
Abstract
Intrinsic disorder occurs when (part of) a protein remains unfolded during normal functioning. Intrinsically-disordered regions can contain segments that 'fold on binding' to another molecule. Here, we perform bioinformatical parsing of human 'folding-on-binding' (FB) proteins, into four subsets: Ordered regions, FB regions, Disordered regions that surround FB regions ('Disordered-around-FB'), and Other-Disordered regions. We examined the composition and evolutionary behaviour (across vertebrate orthologs) of these subsets. From a convergence of three separate analyses, we find that for hydrophobicity, Ordered regions segregate from the other subsets, but the Ordered and FB regions group together as highly conserved, and the Disordered-around-FB and Other-Disordered regions as less conserved (with a lesser significant difference between Ordered and FB regions). FB regions are highly-conserved with net positive charge, whereas Disordered-around-FB have net negative charge and are relatively less hydrophobic than FB regions. Indeed, these Disordered-around-FB regions are excessively hydrophilic compared to other disordered regions generally. We describe how our results point towards a possible compositionally-based steering mechanism of folding-on-binding.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".