In various protein complexes, disordered protomers have large per‐residue surface areas and area of protein‐, DNA‐ and RNA‐binding interfaces
Bibliographic record
Abstract
We provide first large scale analysis of the peculiarities of surface areas of 5658 dissimilar (below 50% sequence similarity) proteins with known 3D-structures that bind to proteins, DNA or RNAs. We show here that area of the protein surface is highly correlated with the protein length. The size of the interface surface is only modestly correlated with the protein size, except for RNA-binding proteins where larger proteins are characterized by larger interfaces. Disordered proteins with disordered interfaces are characterized by significantly larger per-residue areas of their surfaces and interfaces when compared to the structured proteins. These result are applicable for proteins involved in interaction with DNA, RNA, and proteins and suggest that disordered proteins and binding regions are less compact and more likely to assume extended shape. We demonstrate that disordered protein binding residues in the interfaces of disordered proteins drive the increase in the per residue area of these interfaces. Our results can be used to predict in silico whether a given protomer from the DNA, RNA or protein complex is likely to be disordered in its unbound form.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".