Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides
Antimicrobial peptides (AMPs) offer a highly potent alternative solution due to their broad‐spectrum activity and minimum resistance development against the rapidly evolving antibiotic‐resistant pathogens. Herein, to accelerate the discovery process of new AMPs, a predictive and generative algorithm...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-06-01
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| Series: | Small Science |
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| Online Access: | https://doi.org/10.1002/smsc.202400579 |
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| author | Sukhvir Kaur Bhangu Nicholas Welch Morgan Lewis Fanyi Li Brint Gardner Helmut Thissen Wioleta Kowalczyk |
| author_facet | Sukhvir Kaur Bhangu Nicholas Welch Morgan Lewis Fanyi Li Brint Gardner Helmut Thissen Wioleta Kowalczyk |
| author_sort | Sukhvir Kaur Bhangu |
| collection | DOAJ |
| description | Antimicrobial peptides (AMPs) offer a highly potent alternative solution due to their broad‐spectrum activity and minimum resistance development against the rapidly evolving antibiotic‐resistant pathogens. Herein, to accelerate the discovery process of new AMPs, a predictive and generative algorithm is build, which constructs new peptide sequences, scores their antimicrobial activity using a machine learning (ML) model, identifies amino acid motifs, and assembles high‐ranking motifs into new peptide sequences. The eXtreme Gradient Boosting model achieves an accuracy of ≈87% in distinguishing between AMPs and non‐AMPs. The generated peptide sequences are experimentally validated against the bacterial pathogens, and an accuracy of ≈60% is achieved. To refine the algorithm, the physicochemical features are analyzed, particularly charge and hydrophobicity of experimentally validated peptides. The peptides with specific range of charge and hydrophobicity are then removed, which lead to a substantial increase in an experimental accuracy, from ≈60% to ≈80%. Furthermore, generated peptides are active against different fungal strains with minimal off‐target toxicity. In summary, in silico predictive and generative models for functional motif and AMP discovery are powerful tools for engineering highly effective AMPs to combat multidrug resistant pathogens. |
| format | Article |
| id | doaj-art-ae6dc83f81e84a228d8f62fbe46040b8 |
| institution | Kabale University |
| issn | 2688-4046 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Small Science |
| spelling | doaj-art-ae6dc83f81e84a228d8f62fbe46040b82025-08-20T03:45:10ZengWiley-VCHSmall Science2688-40462025-06-0156n/an/a10.1002/smsc.202400579Machine Learning‐Assisted Prediction and Generation of Antimicrobial PeptidesSukhvir Kaur Bhangu0Nicholas Welch1Morgan Lewis2Fanyi Li3Brint Gardner4Helmut Thissen5Wioleta Kowalczyk6CSIRO Manufacturing Research Way Clayton Victoria 3168 AustraliaCSIRO Manufacturing Research Way Clayton Victoria 3168 AustraliaCSIRO Information Management & Technology Kensington Western Australia 6151 AustraliaCSIRO Manufacturing Research Way Clayton Victoria 3168 AustraliaCSIRO Information Management & Technology Research Way Clayton Victoria 3168 AustraliaCSIRO Manufacturing Research Way Clayton Victoria 3168 AustraliaCSIRO Manufacturing Research Way Clayton Victoria 3168 AustraliaAntimicrobial peptides (AMPs) offer a highly potent alternative solution due to their broad‐spectrum activity and minimum resistance development against the rapidly evolving antibiotic‐resistant pathogens. Herein, to accelerate the discovery process of new AMPs, a predictive and generative algorithm is build, which constructs new peptide sequences, scores their antimicrobial activity using a machine learning (ML) model, identifies amino acid motifs, and assembles high‐ranking motifs into new peptide sequences. The eXtreme Gradient Boosting model achieves an accuracy of ≈87% in distinguishing between AMPs and non‐AMPs. The generated peptide sequences are experimentally validated against the bacterial pathogens, and an accuracy of ≈60% is achieved. To refine the algorithm, the physicochemical features are analyzed, particularly charge and hydrophobicity of experimentally validated peptides. The peptides with specific range of charge and hydrophobicity are then removed, which lead to a substantial increase in an experimental accuracy, from ≈60% to ≈80%. Furthermore, generated peptides are active against different fungal strains with minimal off‐target toxicity. In summary, in silico predictive and generative models for functional motif and AMP discovery are powerful tools for engineering highly effective AMPs to combat multidrug resistant pathogens.https://doi.org/10.1002/smsc.202400579antimicrobial peptidesantimicrobial resistancesbacteriamachine learningmultidrug resistances |
| spellingShingle | Sukhvir Kaur Bhangu Nicholas Welch Morgan Lewis Fanyi Li Brint Gardner Helmut Thissen Wioleta Kowalczyk Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides Small Science antimicrobial peptides antimicrobial resistances bacteria machine learning multidrug resistances |
| title | Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides |
| title_full | Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides |
| title_fullStr | Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides |
| title_full_unstemmed | Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides |
| title_short | Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides |
| title_sort | machine learning assisted prediction and generation of antimicrobial peptides |
| topic | antimicrobial peptides antimicrobial resistances bacteria machine learning multidrug resistances |
| url | https://doi.org/10.1002/smsc.202400579 |
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