Evidence map›Paper›PMID 33037295›Full record

ArticleScientific reports2020

A bioinformatic study of antimicrobial peptides identified in the Black Soldier Fly (BSF) Hermetia illucens (Diptera: Stratiomyidae).

Antonio Moretta, Rosanna Salvia, Carmen Scieuzo, Angela Di Somma, Heiko Vogel, Pietro Pucci, Alessandro Sgambato, Michael Wolff, Patrizia Falabella

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.

0numbers the graph read from it
0cells of the map it votes in
83citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

83 citing papers in PubMed.

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  18. Antimicrobial peptides in post-harvest management of fruits.Journal of food science and technology · 2025
    Review
  19. Investigating the Microbial Dynamics ofFoods (Basel, Switzerland) · 2025
    Article
  20. Article

23 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Antonio MorettaDepartment of Sciences, University of Basilicata, Via dell'Ateneo Lucano 10, 85100, Potenza, Italy.
Rosanna SalviaDepartment of Sciences, University of Basilicata, Via dell'Ateneo Lucano 10, 85100, Potenza, Italy.
Carmen ScieuzoDepartment of Sciences, University of Basilicata, Via dell'Ateneo Lucano 10, 85100, Potenza, Italy.
Angela Di SommaDepartment of Chemical Sciences, University Federico II of Napoli, Via Cinthia 6, 80126, Napoli, Italy.
Heiko VogelDepartment of Entomology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, 07745, Jena, Germany.
Pietro PucciCEINGE Advanced Biotechnology, Via Gaetano Salvatore 486, Naples, Italy.
Alessandro SgambatoCentro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Rionero in Vulture, PZ, Italy.
Michael WolffInstitute of Bioprocess Engineering and Pharmaceutical Technology, Technische Hochschule Mittelhessen, Wiesenstrasse 14, 35390, Giessen, Germany.
Patrizia FalabellaDepartment of Sciences, University of Basilicata, Via dell'Ateneo Lucano 10, 85100, Potenza, Italy. patrizia.falabell@unibas.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antimicrobial peptides (AMPs) play a key role in the innate immunity, the first line of defense against bacteria, fungi, and viruses. AMPs are small molecules, ranging from 10 to 100 amino acid residues produced by all living organisms. Because of their wide biodiversity, insects are among the richest and most innovative sources for AMPs. In particular, the insect Hermetia illucens (Diptera: Stratiomyidae) shows an extraordinary ability to live in hostile environments, as it feeds on decaying substrates, which are rich in microbial colonies, and is one of the most promising sources for AMPs. The larvae and the combined adult male and female H. illucens transcriptomes were examined, and all the sequences, putatively encoding AMPs, were analysed with different machine learning-algorithms, such as the Support Vector Machine, the Discriminant Analysis, the Artificial Neural Network, and the Random Forest available on the CAMP database, in order to predict their antimicrobial activity. Moreover, the iACP tool, the AVPpred, and the Antifp servers were used to predict the anticancer, the antiviral, and the antifungal activities, respectively. The related physicochemical properties were evaluated with the Antimicrobial Peptide Database Calculator and Predictor. These analyses allowed to identify 57 putatively active peptides suitable for subsequent experimental validation studies.

Indexed as

AlgorithmsAnimalsAntifungal AgentsAntineoplastic AgentsAntiviral AgentsChemical PhenomenaDipteraFemaleImmunity, InnateLarvaMachine LearningMalePore Forming Cytotoxic ProteinsTranscriptomeAntifungal AgentsAntineoplastic AgentsAntiviral AgentsPore Forming Cytotoxic Proteins

Identifiers

PMID33037295
PMCPMC7547115

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.