Disentangling the CHAOS of intrinsic disorder in human proteins
Bibliographic record
Abstract
Abstract Most proteins consist of both folded domains and Intrinsically Disordered Regions (IDRs). However, the widespread occurrence of intrinsic disorder in human proteins, along with its characteristics, is often overlooked by the broader communities of structural and molecular biologists. Building on the MobiDB database of intrinsic disorder in proteins, here we develop a comprehensive dataset ( C omprehensive analysis of Human proteins A nd their dis O rdered Segments - CHAOS). We implement internally consistent definitions of disordered regions, and annotate general characteristics such as cellular location, essentiality, post-translational modifications, and predicted pathogenicity. Further, we cross-reference to structure predictions from AlphaFold. We find that most human proteins contain at least one disordered region, predominantly located at the protein termini. IDRs are less hydrophobic, enriched in post-translational modifications, and mutations in IDRs are predicted to be less pathogenic than in non-IDRs. Additionally, we discovered that proteins residing in different cellular locations possess distinct disorder profiles. Finally, the predicted AlphaFold models of proteins in CHAOS suggest that disordered regions and proteins are often predicted to adopt secondary structure. Hereby we enhance the visibility and understanding of intrinsic disorder in human proteins. Key messages Four out of five human proteins contain one or more intrinsically disordered regions (IDRs). Half of the IDRs are located at protein termini, but three quarters of all human proteins contain a terminal IDR. The amount and location of disordered regions differs throughout cellular compartments. One in five missense mutations in IDRs are likely pathogenic. AlphaFold predicts secondary structure elements within intrinsically disordered regions and fully disordered proteins.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".