دورية أكاديمية
Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence.
العنوان: | Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence. |
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المؤلفون: | Zhang Y; National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.; Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China., Li R; National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.; Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China., Zou G; National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.; Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China., Guo Y; National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.; College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China., Wu R; National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.; College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China., Zhou Y; National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China., Chen H; National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.; College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China., Zhou R; National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.; College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China., Lavigne R; Department of Biosystems, Laboratory of Gene Technology, KU Leuven, Leuven, 3001, Belgium., Bergen PJ; Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, 3800, Australia., Li J; Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, 3800, Australia., Li J; National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.; Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.; College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518000, China. |
المصدر: | Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Adv Sci (Weinh)] 2024 Jun 20, pp. e2404049. Date of Electronic Publication: 2024 Jun 20. |
Publication Model: | Ahead of Print |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: WILEY-VCH Country of Publication: Germany NLM ID: 101664569 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2198-3844 (Electronic) Linking ISSN: 21983844 NLM ISO Abbreviation: Adv Sci (Weinh) Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: Weinheim : WILEY-VCH, [2014]- |
مستخلص: | The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs ("dark matter") for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best-in-class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance. (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.) |
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معلومات مُعتمدة: | 32322082 National Natural Science Foundation of China; 32072323 National Natural Science Foundation of China; 32073022 National Natural Science Foundation of China; 2023YFD1801000 National Key Research and Development Program of China; 2022YFD1800903 National Key Research and Development Program of China; SZYJY2022018 HZAU-AGIS Cooperation Fund; 13210333 Training Program of Distinguished Agricultural Researcher; 2023AFA111 Natural Science Foundation of Hubei Province; 2022CFB659 Natural Science Foundation of Hubei Province; Young Top-notch Talent Cultivation Program of Hubei Province; 2662024JC008 the Fundamental Research Funds for the Central Universities |
فهرسة مساهمة: | Keywords: antibacterial protein; antibiotic resistance; high‐throughput screening; infectious diseases; phage lysin; prophage; stacking model |
تواريخ الأحداث: | Date Created: 20240620 Latest Revision: 20240720 |
رمز التحديث: | 20240720 |
DOI: | 10.1002/advs.202404049 |
PMID: | 38899839 |
قاعدة البيانات: | MEDLINE |
تدمد: | 2198-3844 |
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DOI: | 10.1002/advs.202404049 |