Proteome-Wide Discovery of Evolutionary Conserved Sequences in Disordered Regions
Bibliographic record
Abstract
At least 30% of human proteins are thought to contain intrinsically disordered regions, which lack stable structural conformation. Despite lacking enzymatic functions and having few protein domains, disordered regions are functionally important for protein regulation and contain short linear motifs (short peptide sequences involved in protein-protein interactions), but in most disordered regions, the functional amino acid residues remain unknown. We searched for evolutionarily conserved sequences within disordered regions according to the hypothesis that conservation would indicate functional residues. Using a phylogenetic hidden Markov model (phylo-HMM), we made accurate, specific predictions of functional elements in disordered regions even when these elements are only two or three amino acids long. Among the conserved sequences that we identified were previously known and newly identified short linear motifs, and we experimentally verified key examples, including a motif that may mediate interaction between protein kinase Cbk1 and its substrates. We also observed that hub proteins, which interact with many partners in a protein interaction network, are highly enriched in these conserved sequences. Our analysis enabled the systematic identification of the functional residues in disordered regions and suggested that at least 5% of amino acids in disordered regions are important for function.
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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) | 0.000 | 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".