Activity of Naturally Derived Antimicrobial Peptides against Filamentous Fungi Relevant for Agriculture
Bibliographic record
Abstract
The search for environmentally biocompatible and cost-effective methods to control filamentous fungi in agriculture is becoming increasingly urgent. In vitro antimicrobial activity of three synthetic peptides was investigated against some filamentous fungi with agricultural relevance. The peptides were an analog of Temporin called Temporizina, a fragment from Pleurocidin termed Plc-2, and a peptide identified from sesame seeds named Pses3. Antimicrobial activity of these peptides towards filamentous fungi has not been previously reported. Seven plant pathogenic or mycotoxigenic fungal species, isolated from plant tissues were assayed: Alternaria solani, Colletotrichum gloesporioides, Fulvia fulvum, Fusarium oxisporum, Aspergillus niger, A. ochraceus and Penicillium digitatum. Values of Minimum Inhibitory Concentration (MIC) and Minimum Fungicidal Concentration (MFC) were determined and compared with the commercially available fungicide Captan as a positive control. The peptides showing greatest inhibition were Pses3 and Plc-2 and C. gloesporioides was the most sensitive of the evaluated fungi. The MIC values for Plc-2 and Pses3 peptides ranged from 0.64 µM to 10.25 µM. These values were much lower than those observed for Captan, suggesting the potential of these peptides as fungicides. In particular, Pses3 is a novel peptide derived from sesame seeds not reported in databases.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".