PhytAMP: a database dedicated to antimicrobial plant peptides
Bibliographic record
Abstract
Plants produce small cysteine-rich antimicrobial peptides as an innate defense against pathogens. Based on amino acid sequence homology, these peptides were classified mostly as alpha-defensins, thionins, lipid transfer proteins, cyclotides, snakins and hevein-like. Although many antimicrobial plant peptides are now well characterized, much information is still missing or is unavailable to potential users. The compilation of such information in one centralized resource, such as a database would therefore facilitate the study of the potential these peptide structures represent, for example, as alternatives in response to increasing antibiotic resistance or for increasing plant resistance to pathogens by genetic engineering. To achieve this goal, we developed a new database, PhytAMP, which contains valuable information on antimicrobial plant peptides, including taxonomic, microbiological and physicochemical data. Information is very easy to extract from this database and allows rapid prediction of structure/function relationships and target organisms and hence better exploitation of plant peptide biological activities in both the pharmaceutical and agricultural sectors. PhytAMP may be accessed free of charge at http://phytamp.pfba-lab.org.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
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".