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ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria

Affiliation
Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, 45141 Essen, Germany
Ruiz-Blanco, Yasser B.;
ORCID
0000-0002-9908-2418
Affiliation
CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal
Agüero-Chapin, Guillermin;
Affiliation
Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, 45141 Essen, Germany
Romero-Molina, Sandra;
ORCID
0000-0002-1328-1732
Affiliation
CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal
Antunes, Agostinho;
ORCID
0000-0001-7793-4408
Affiliation
Institute of Molecular Virology, University Hospital Ulm, 89081 Ulm, Germany
Olari, Lia-Raluca;
ORCID
0000-0001-7552-8764
Affiliation
Institute of Medical Microbiology and Hygiene, University Hospital Ulm, 89081 Ulm, Germany
Spellerberg, Barbara;
ORCID
0000-0001-7316-7141
Affiliation
Institute of Molecular Virology, University Hospital Ulm, 89081 Ulm, Germany
Münch, Jan;
Affiliation
Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, 45141 Essen, Germany
Sanchez-Garcia, Elsa

Multi-drug resistance in bacteria is a major health problem worldwide. To overcome this issue, new approaches allowing for the identification and development of antibacterial agents are urgently needed. Peptides, due to their binding specificity and low expected side effects, are promising candidates for a new generation of antibiotics. For over two decades, a large diversity of antimicrobial peptides (AMPs) has been discovered and annotated in public databases. The AMP family encompasses nearly 20 biological functions, thus representing a potentially valuable resource for data mining analyses. Nonetheless, despite the availability of machine learning-based approaches focused on AMPs, these tools lack evidence of successful application for AMPs’ discovery, and many are not designed to predict a specific function for putative AMPs, such as antibacterial activity. Consequently, among the apparent variety of data mining methods to screen peptide sequences for antibacterial activity, only few tools can deal with such task consistently, although with limited precision and generally no information about the possible targets. Here, we addressed this gap by introducing a tool specifically designed to identify antibacterial peptides (ABPs) with an estimation of which type of bacteria is susceptible to the action of these peptides, according to their response to the Gram-staining assay. Our tool is freely available via a web server named ABP-Finder. This new method ranks within the top state-of-the-art ABP predictors, particularly in terms of precision. Importantly, we showed the successful application of ABP-Finder for the screening of a large peptide library from the human urine peptidome and the identification of an antibacterial peptide.

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