ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria
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 candidate...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Antibiotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6382/11/12/1708 |
| _version_ | 1827642116914806784 |
|---|---|
| author | Yasser B. Ruiz-Blanco Guillermin Agüero-Chapin Sandra Romero-Molina Agostinho Antunes Lia-Raluca Olari Barbara Spellerberg Jan Münch Elsa Sanchez-Garcia |
| author_facet | Yasser B. Ruiz-Blanco Guillermin Agüero-Chapin Sandra Romero-Molina Agostinho Antunes Lia-Raluca Olari Barbara Spellerberg Jan Münch Elsa Sanchez-Garcia |
| author_sort | Yasser B. Ruiz-Blanco |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T17:24:05Z |
| format | Article |
| id | doaj.art-7bb33664123447ad951c4c8ddd542158 |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-03-09T17:24:05Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-7bb33664123447ad951c4c8ddd5421582023-11-24T12:53:01ZengMDPI AGAntibiotics2079-63822022-11-011112170810.3390/antibiotics11121708ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted BacteriaYasser B. Ruiz-Blanco0Guillermin Agüero-Chapin1Sandra Romero-Molina2Agostinho Antunes3Lia-Raluca Olari4Barbara Spellerberg5Jan Münch6Elsa Sanchez-Garcia7Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, 45141 Essen, GermanyCIIMAR—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, PortugalComputational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, 45141 Essen, GermanyCIIMAR—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, PortugalInstitute of Molecular Virology, University Hospital Ulm, 89081 Ulm, GermanyInstitute of Medical Microbiology and Hygiene, University Hospital Ulm, 89081 Ulm, GermanyInstitute of Molecular Virology, University Hospital Ulm, 89081 Ulm, GermanyComputational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen, 45141 Essen, GermanyMulti-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.https://www.mdpi.com/2079-6382/11/12/1708antibacterial peptidemachine learningAMPs databaseStarPepGram staining-based targetpeptide library screening |
| spellingShingle | Yasser B. Ruiz-Blanco Guillermin Agüero-Chapin Sandra Romero-Molina Agostinho Antunes Lia-Raluca Olari Barbara Spellerberg Jan Münch Elsa Sanchez-Garcia ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria Antibiotics antibacterial peptide machine learning AMPs database StarPep Gram staining-based target peptide library screening |
| title | ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria |
| title_full | ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria |
| title_fullStr | ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria |
| title_full_unstemmed | ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria |
| title_short | ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria |
| title_sort | abp finder a tool to identify antibacterial peptides and the gram staining type of targeted bacteria |
| topic | antibacterial peptide machine learning AMPs database StarPep Gram staining-based target peptide library screening |
| url | https://www.mdpi.com/2079-6382/11/12/1708 |
| work_keys_str_mv | AT yasserbruizblanco abpfinderatooltoidentifyantibacterialpeptidesandthegramstainingtypeoftargetedbacteria AT guillerminaguerochapin abpfinderatooltoidentifyantibacterialpeptidesandthegramstainingtypeoftargetedbacteria AT sandraromeromolina abpfinderatooltoidentifyantibacterialpeptidesandthegramstainingtypeoftargetedbacteria AT agostinhoantunes abpfinderatooltoidentifyantibacterialpeptidesandthegramstainingtypeoftargetedbacteria AT liaralucaolari abpfinderatooltoidentifyantibacterialpeptidesandthegramstainingtypeoftargetedbacteria AT barbaraspellerberg abpfinderatooltoidentifyantibacterialpeptidesandthegramstainingtypeoftargetedbacteria AT janmunch abpfinderatooltoidentifyantibacterialpeptidesandthegramstainingtypeoftargetedbacteria AT elsasanchezgarcia abpfinderatooltoidentifyantibacterialpeptidesandthegramstainingtypeoftargetedbacteria |