AlphaFlex: Ensembles of the human proteome representing disordered regions
Bibliographic record
Abstract
Over a third of residues in the canonical human proteome are predicted to fall within intrinsically disordered protein regions (IDRs), which do not adopt stable folded structures. These IDRs play critical roles in biological regulation and organization, including as targets for post-translational modifications, scaffolds and mediators of biomolecular condensates. To address the pressing need for valid structural models providing biological relevance and enabling functional insight, we developed the AlphaFlex workflow, using IDPConformerGenerator or IDPForge to calculate fully atomistic conformer ensembles for proteins predicted to have disordered regions, modeled in the context of highly confident folded domains from AlphaFold2. We illustrate our approach by generating conformational ensembles of the human proteins in the AlphaFold2 database, with completed AlphaFlex models deposited in the Protein Ensemble Database that is mirrored in UniProt. This transformative resource of AlphaFlex ensembles provides more realistic and biologically relevant full-length protein models for proteins with IDRs, which we illustrate for scaffold proteins with folded domains connected by IDRs, those with IDRs that interact with folded domains, regulatory and condensate proteins requiring exposed binding elements, and a conditionally folding IDR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".