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CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides
To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel...
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| Published in: | Antibiotics (Basel) 2023-04, Vol.12 (4), p.725 |
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| Main Authors: | , , , , , , , , |
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| cited_by | cdi_FETCH-LOGICAL-c567t-3053cf9014439da924c4ad5a4bbcfaff0a530c9776d3dc12cbd8478e9948e1ca3 |
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| cites | cdi_FETCH-LOGICAL-c567t-3053cf9014439da924c4ad5a4bbcfaff0a530c9776d3dc12cbd8478e9948e1ca3 |
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| container_issue | 4 |
| container_start_page | 725 |
| container_title | Antibiotics (Basel) |
| container_volume | 12 |
| creator | Bournez, Colin Riool, Martijn de Boer, Leonie Cordfunke, Robert A de Best, Leonie van Leeuwen, Remko Drijfhout, Jan Wouter Zaat, Sebastian A J van Westen, Gerard J P |
| description | To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones ( |
| doi_str_mv | 10.3390/antibiotics12040725 |
| format | article |
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Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones (<35 amino acids), can become an effective solution to face the multi-drug resistance issue arising worldwide. Whereas finding potent AMPs through classical wet-lab techniques is still a long and expensive process, a machine learning model can be useful to help researchers to rapidly identify whether peptides present potential or not. Our prediction model is based on a new data set constructed from the available public data on AMPs and experimental antimicrobial activities. CalcAMP can predict activity against both Gram-positive and Gram-negative bacteria. Different features either concerning general physicochemical properties or sequence composition have been assessed to retrieve higher prediction accuracy. CalcAMP can be used as an promising prediction asset to identify short AMPs among given peptide sequences.</description><identifier>ISSN: 2079-6382</identifier><identifier>EISSN: 2079-6382</identifier><identifier>DOI: 10.3390/antibiotics12040725</identifier><identifier>PMID: 37107088</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Algorithms ; Amino acids ; Antibiotics ; Antiinfectives and antibacterials ; Antimicrobial activity ; Antimicrobial agents ; Antimicrobial peptides ; antimicrobial resistance ; artificial intelligence ; Bacteria ; Cancer ; Datasets ; Deep learning ; drug discovery ; Drug resistance ; Fungi ; Gram-negative bacteria ; Gram-positive bacteria ; Health aspects ; Immune system ; Innate immunity ; Learning algorithms ; Machine learning ; Microorganisms ; Multidrug resistance ; Parasites ; Pathogens ; Peptides ; Physicochemical properties ; Prediction models ; Streptococcus infections ; Viruses</subject><ispartof>Antibiotics (Basel), 2023-04, Vol.12 (4), p.725</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Different features either concerning general physicochemical properties or sequence composition have been assessed to retrieve higher prediction accuracy. CalcAMP can be used as an promising prediction asset to identify short AMPs among given peptide sequences.</description><subject>Algorithms</subject><subject>Amino acids</subject><subject>Antibiotics</subject><subject>Antiinfectives and antibacterials</subject><subject>Antimicrobial activity</subject><subject>Antimicrobial agents</subject><subject>Antimicrobial peptides</subject><subject>antimicrobial resistance</subject><subject>artificial intelligence</subject><subject>Bacteria</subject><subject>Cancer</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>drug discovery</subject><subject>Drug resistance</subject><subject>Fungi</subject><subject>Gram-negative bacteria</subject><subject>Gram-positive bacteria</subject><subject>Health aspects</subject><subject>Immune system</subject><subject>Innate immunity</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Microorganisms</subject><subject>Multidrug resistance</subject><subject>Parasites</subject><subject>Pathogens</subject><subject>Peptides</subject><subject>Physicochemical properties</subject><subject>Prediction models</subject><subject>Streptococcus infections</subject><subject>Viruses</subject><issn>2079-6382</issn><issn>2079-6382</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptUk1vEzEQXSEQrUp_ARKyxIVLir32xjYXtIr4qJRADnC2vONx4mizDl6nVf89DimlQbUPtmbee_bMm6p6zegV55q-t0MOXYg5wMhqKqism2fVeU2lnky5qp8_up9Vl-O4oWVpxhVVL6szLhmVVKnzajWzPbSL5QfSkm94SxYW1mFAMkebhjCsyCI67ImPieQ1khZgn2xGskzoAuQQBxI9actvtgFS7ILtCyiHm5DvDpkl7nJwOL6qXnjbj3h5f15UPz9_-jH7Opl__3I9a-cTaKYyTzhtOHhNmRBcO6trAcK6xoquA2-9p7bhFLSUU8cdsBo6p4RUqLVQyMDyi-r6qOui3ZhdClub7ky0wfwJxLQyNpWu9Wi6zvIGHZUAQjAuFHCtpLNFnwkJvmh9PGrt9t0WHeCQk-1PRE8zQ1ibVbwxjDLeMKGKwrt7hRR_7XHMZhtGwL63A8b9aGpVTKprqnmBvv0Puon7NJReHVBT0WhZq3-olS0VhMHH8jAcRE0rheRUFscL6uoJVNkOi0txQB9K_ITAj4Ri4Tgm9A9FMmoO82aemLfCevO4Pw-cv9PFfwOiT9Ja</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>Bournez, Colin</creator><creator>Riool, Martijn</creator><creator>de Boer, Leonie</creator><creator>Cordfunke, Robert A</creator><creator>de Best, Leonie</creator><creator>van Leeuwen, Remko</creator><creator>Drijfhout, Jan Wouter</creator><creator>Zaat, Sebastian A J</creator><creator>van Westen, Gerard J P</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7T7</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4808-5296</orcidid><orcidid>https://orcid.org/0000-0001-9589-186X</orcidid><orcidid>https://orcid.org/0000-0002-5444-3179</orcidid><orcidid>https://orcid.org/0000-0002-6935-1509</orcidid><orcidid>https://orcid.org/0000-0001-5568-4110</orcidid><orcidid>https://orcid.org/0000-0003-0717-1817</orcidid></search><sort><creationdate>20230401</creationdate><title>CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides</title><author>Bournez, Colin ; 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| ispartof | Antibiotics (Basel), 2023-04, Vol.12 (4), p.725 |
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| subjects | Algorithms Amino acids Antibiotics Antiinfectives and antibacterials Antimicrobial activity Antimicrobial agents Antimicrobial peptides antimicrobial resistance artificial intelligence Bacteria Cancer Datasets Deep learning drug discovery Drug resistance Fungi Gram-negative bacteria Gram-positive bacteria Health aspects Immune system Innate immunity Learning algorithms Machine learning Microorganisms Multidrug resistance Parasites Pathogens Peptides Physicochemical properties Prediction models Streptococcus infections Viruses |
| title | CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides |
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