Peptidomics of Circular Cysteine-Rich Plant Peptides: Analysis of the Diversity of Cyclotides from <i>Viola tricolor</i> by Transcriptome and Proteome Mining
Bibliographic record
Abstract
Cyclotides are plant-derived mini proteins. They are genetically encoded as precursor proteins that become post-translationally modified to yield circular cystine-knotted molecules. Because of this structural topology cyclotides resist enzymatic degradation in biological fluids, and hence they are considered as promising lead molecules for pharmaceutical applications. Despite ongoing efforts to discover novel cyclotides and analyze their biodiversity, it is not clear how many individual peptides a single plant specimen can express. Therefore, we investigated the transcriptome and cyclotide peptidome of Viola tricolor. Transcriptome mining enabled the characterization of cyclotide precursor architecture and processing sites important for biosynthesis of mature peptides. The cyclotide peptidome was explored by mass spectrometry and bottom-up proteomics using the extracted peptide sequences as queries for database searching. In total 164 cyclotides were discovered by nucleic acid and peptide analysis in V. tricolor. Therefore, violaceous plants at a global scale may be the source to as many as 150 000 individual cyclotides. Encompassing the diversity of V. tricolor as a combinatorial library of bioactive peptides, this commercially available medicinal herb may be a suitable starting point for future bioactivity-guided screening studies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".