Intrinsically Disordered Compositional Bias in Proteins: Sequence Traits, Region Clustering, and Generation of Hypothetical Functional Associations
Bibliographic record
Abstract
Compositionally biased regions (CBRs), ie, tracts that are dominated by a subset of residue types, are common features of eukaryotic proteins. These are often found bounded within or almost coterminous with intrinsically disordered or ‘natively unfolded’ parts. Here, it is investigated how the function of such intrinsically disordered compositionally biased regions (ID-CBRs) is directly linked to their compositional traits, focusing on the well-characterized yeast ( Saccharomyces cerevisiae) proteome as a test case. The ID-CBRs that are clustered together using compositional distance are discovered to have clear functional linkages at various levels of diversity. The specific case of the Sup35p and Rnq1p proteins that underlie causally linked prion phenomena ([PSI+] and [RNQ+]) is highlighted. Their prion-forming ID-CBRs are typically clustered very close together indicating some compositional engendering for [RNQ+] seeding of [PSI+] prions. Delving further, ID-CBRs with distinct types of residue patterning such as ‘blocking’ or relative segregation of residues into homopeptides are found to have significant functional trends. Specific examples of such ID-CBR functional linkages that are discussed are: Q/N-rich ID-CBRs linked to transcriptional coactivation, S-rich to transcription-factor binding, R-rich to DNA-binding, S/E-rich to protein localization, and D-rich linked to chromatin remodelling. These data may be useful in informing experimental hypotheses for proteins containing such regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".