Deep‐Learning‐Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability
ABSTRACT Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models for stapled AMPs using deep and machine lea...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Microbial Biotechnology |
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| Online Access: | https://doi.org/10.1111/1751-7915.70121 |
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| author | Ruole Chen Yuhao You Yanchao Liu Xin Sun Tianyue Ma Xingzhen Lao Heng Zheng |
| author_facet | Ruole Chen Yuhao You Yanchao Liu Xin Sun Tianyue Ma Xingzhen Lao Heng Zheng |
| author_sort | Ruole Chen |
| collection | DOAJ |
| description | ABSTRACT Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models for stapled AMPs using deep and machine learning, tested their accuracy with an independent data set and wet lab experiments, and characterised stapled loop structures using structural, sequence and amino acid descriptors. AlphaFold improved stapled peptide structure prediction. The support vector machine model performed best, while two deep learning models achieved the highest accuracy of 1.0 on an external test set. Designed cysteine‐ and lysine‐stapled peptides inhibited various bacteria with low concentrations and showed good serum stability and low haemolytic activity. This study highlights the potential of the deep learning method in peptide modification and design. |
| format | Article |
| id | doaj-art-77f9a51eec424fb896db26b0d669e112 |
| institution | OA Journals |
| issn | 1751-7915 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Microbial Biotechnology |
| spelling | doaj-art-77f9a51eec424fb896db26b0d669e1122025-08-20T01:48:41ZengWileyMicrobial Biotechnology1751-79152025-03-01183n/an/a10.1111/1751-7915.70121Deep‐Learning‐Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and StabilityRuole Chen0Yuhao You1Yanchao Liu2Xin Sun3Tianyue Ma4Xingzhen Lao5Heng Zheng6School of Life Science and Technology China Pharmaceutical University Nanjing Jiangsu ChinaSchool of Life Science and Technology China Pharmaceutical University Nanjing Jiangsu ChinaSchool of Life Science and Technology China Pharmaceutical University Nanjing Jiangsu ChinaSchool of Life Science and Technology China Pharmaceutical University Nanjing Jiangsu ChinaSchool of Life Science and Technology China Pharmaceutical University Nanjing Jiangsu ChinaSchool of Life Science and Technology China Pharmaceutical University Nanjing Jiangsu ChinaSchool of Life Science and Technology China Pharmaceutical University Nanjing Jiangsu ChinaABSTRACT Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models for stapled AMPs using deep and machine learning, tested their accuracy with an independent data set and wet lab experiments, and characterised stapled loop structures using structural, sequence and amino acid descriptors. AlphaFold improved stapled peptide structure prediction. The support vector machine model performed best, while two deep learning models achieved the highest accuracy of 1.0 on an external test set. Designed cysteine‐ and lysine‐stapled peptides inhibited various bacteria with low concentrations and showed good serum stability and low haemolytic activity. This study highlights the potential of the deep learning method in peptide modification and design.https://doi.org/10.1111/1751-7915.70121antimicrobial activityantimicrobial peptidesdeep learninghaemolytic activityserum stabilitystapled peptides |
| spellingShingle | Ruole Chen Yuhao You Yanchao Liu Xin Sun Tianyue Ma Xingzhen Lao Heng Zheng Deep‐Learning‐Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability Microbial Biotechnology antimicrobial activity antimicrobial peptides deep learning haemolytic activity serum stability stapled peptides |
| title | Deep‐Learning‐Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability |
| title_full | Deep‐Learning‐Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability |
| title_fullStr | Deep‐Learning‐Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability |
| title_full_unstemmed | Deep‐Learning‐Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability |
| title_short | Deep‐Learning‐Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability |
| title_sort | deep learning based approaches for rational design of stapled peptides with high antimicrobial activity and stability |
| topic | antimicrobial activity antimicrobial peptides deep learning haemolytic activity serum stability stapled peptides |
| url | https://doi.org/10.1111/1751-7915.70121 |
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