Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910852589813760 |
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| author | Wang, Jike Feng, Jianwen Kang, Yu Pan, Peichen Ge, Jingxuan Wang, Yan Wang, Mingyang Wu, Zhenxing Zhang, Xingcai Yu, Jiameng Zhang, Xujun Wang, Tianyue Wen, Lirong Yan, Guangning Deng, Yafeng Shi, Hui Hsieh, Chang-Yu Jiang, Zhihui Hou, Tingjun |
| author_facet | Wang, Jike Feng, Jianwen Kang, Yu Pan, Peichen Ge, Jingxuan Wang, Yan Wang, Mingyang Wu, Zhenxing Zhang, Xingcai Yu, Jiameng Zhang, Xujun Wang, Tianyue Wen, Lirong Yan, Guangning Deng, Yafeng Shi, Hui Hsieh, Chang-Yu Jiang, Zhihui Hou, Tingjun |
| contents | Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks. We introduce AMP-Designer, an LLM-based approach for swiftly designing novel antimicrobial peptides (AMPs) with desired properties. Within 11 days, AMP-Designer achieved the de novo design of 18 AMPs with broad-spectrum activity against Gram-negative bacteria. In vitro validation revealed a 94.4% success rate, with two candidates demonstrating exceptional antibacterial efficacy, minimal hemotoxicity, stability in human plasma, and low potential to induce resistance, as evidenced by significant bacterial load reduction in murine lung infection experiments. The entire process, from design to validation, concluded in 48 days. AMP-Designer excels in creating AMPs targeting specific strains despite limited data availability, with a top candidate displaying a minimum inhibitory concentration of 2.0 μg/ml against Propionibacterium acnes. Integrating advanced machine learning techniques, AMP-Designer demonstrates remarkable efficiency, paving the way for innovative solutions to antibiotic resistance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12296 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model Wang, Jike Feng, Jianwen Kang, Yu Pan, Peichen Ge, Jingxuan Wang, Yan Wang, Mingyang Wu, Zhenxing Zhang, Xingcai Yu, Jiameng Zhang, Xujun Wang, Tianyue Wen, Lirong Yan, Guangning Deng, Yafeng Shi, Hui Hsieh, Chang-Yu Jiang, Zhihui Hou, Tingjun Biomolecules Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks. We introduce AMP-Designer, an LLM-based approach for swiftly designing novel antimicrobial peptides (AMPs) with desired properties. Within 11 days, AMP-Designer achieved the de novo design of 18 AMPs with broad-spectrum activity against Gram-negative bacteria. In vitro validation revealed a 94.4% success rate, with two candidates demonstrating exceptional antibacterial efficacy, minimal hemotoxicity, stability in human plasma, and low potential to induce resistance, as evidenced by significant bacterial load reduction in murine lung infection experiments. The entire process, from design to validation, concluded in 48 days. AMP-Designer excels in creating AMPs targeting specific strains despite limited data availability, with a top candidate displaying a minimum inhibitory concentration of 2.0 μg/ml against Propionibacterium acnes. Integrating advanced machine learning techniques, AMP-Designer demonstrates remarkable efficiency, paving the way for innovative solutions to antibiotic resistance. |
| title | Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2407.12296 |