Discordant and chameleon sequences: Their distribution and implications for amyloidogenicity
Bibliographic record
Abstract
Identification of ambiguous encoding in protein secondary structure is paramount to develop an understanding of key protein segments underlying amyloid diseases. We investigate two types of structurally ambivalent peptides, which were hypothesized in the literature as indicators of amyloidogenic proteins: discordant α-helices and chameleon sequences. Chameleon sequences are peptides discovered experimentally in different secondary-structure types. Discordant α-helices are α-helical stretches with strong β-strand propensity or prediction. To assess the distribution of these features in known protein structures, and their potential role in amyloidogenesis, we analyzed the occurrence of discordant α-helices and chameleon sequences in nonredundant sets of protein domains (n = 4263) and amyloidogenic proteins extracted from the literature (n = 77). Discordant α-helices were identified if discordance was observed between known secondary structures and secondary-structure predictions from the GOR-IV and PSIPRED algorithms. Chameleon sequences were extracted by searching for identical sequence words in α-helices and β-strands. We defined frustrated chameleons and very frustrated chameleons based on varying degrees of total β propensity ≥α propensity. To our knowledge, this is the first study to discern statistical relationships between discordance, chameleons, and amyloidogenicity. We observed varying enrichment levels for some categories of discordant and chameleon sequences in amyloidogenic sequences. Chameleon sequences are also significantly enriched in proteins that have discordant helices, indicating a clear link between both phenomena. We identified the first set of discordant-chameleonic protein segments we predict may be involved in amyloidosis. We present a detailed analysis of discordant and chameleons segments in the family of one of the amyloidogenic proteins, the Prion Protein.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".