Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence.

Yue Zhang, Runze Li, Geng Zou, Yating Guo, Renwei Wu, Yang Zhou, Huanchun Chen, Rui Zhou, Rob Lavigne, Phillip J Bergen, Jian Li, Jinquan Li
Author Information
  1. Yue Zhang: 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.
  2. Runze Li: 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.
  3. Geng Zou: 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.
  4. Yating Guo: 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.
  5. Renwei Wu: 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.
  6. Yang Zhou: 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.
  7. Huanchun Chen: 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.
  8. Rui Zhou: 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.
  9. Rob Lavigne: Department of Biosystems, Laboratory of Gene Technology, KU Leuven, Leuven, 3001, Belgium.
  10. Phillip J Bergen: Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, 3800, Australia.
  11. Jian Li: Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, 3800, Australia.
  12. Jinquan Li: 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. ORCID

Abstract

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.

Keywords

References

  1. Bioinformatics. 2006 Jul 1;22(13):1658-9 [PMID: 16731699]
  2. Adv Sci (Weinh). 2024 Aug;11(32):e2404049 [PMID: 38899839]
  3. Lancet. 2022 Feb 12;399(10325):629-655 [PMID: 35065702]
  4. Sci Adv. 2020 Jun 03;6(23):eaaz1136 [PMID: 32537492]
  5. Infect Dis (Lond). 2023 Jan;55(1):1-8 [PMID: 36151989]
  6. Cell. 2020 Sep 3;182(5):1311-1327.e14 [PMID: 32888495]
  7. Biofouling. 2021 Feb;37(2):184-193 [PMID: 33615928]
  8. Bioinformatics. 2014 Jul 15;30(14):2068-9 [PMID: 24642063]
  9. Int J Antimicrob Agents. 2020 Feb;55(2):105844 [PMID: 31715257]
  10. Bioorg Chem. 2023 Dec;141:106894 [PMID: 37776682]
  11. Nat Commun. 2023 Jul 28;14(1):4552 [PMID: 37507402]
  12. PLoS One. 2015 Mar 17;10(3):e0120066 [PMID: 25781990]
  13. Int J Mol Sci. 2020 Mar 25;21(7): [PMID: 32218345]
  14. Nucleic Acids Res. 2023 Jan 6;51(D1):D523-D531 [PMID: 36408920]
  15. J Crit Care Med (Targu Mures). 2018 Apr 01;4(2):47-49 [PMID: 30581994]
  16. J Med Microbiol. 2018 Mar;67(3):296-307 [PMID: 29458674]
  17. Nat Methods. 2022 Jun;19(6):679-682 [PMID: 35637307]
  18. J Clin Microbiol. 2022 Apr 20;60(4):e0242921 [PMID: 35254101]
  19. Nat Biotechnol. 2021 May;39(5):578-585 [PMID: 33349699]
  20. Bioinformatics. 2018 Jul 15;34(14):2499-2502 [PMID: 29528364]
  21. Nucleic Acids Res. 2012 Sep;40(16):e126 [PMID: 22584627]
  22. Int J Food Microbiol. 2021 Mar 02;341:109068 [PMID: 33498009]
  23. J Infect. 2021 Dec;83(6):656-663 [PMID: 34626700]
  24. Sci Rep. 2017 Jan 09;7:40182 [PMID: 28067286]
  25. Sci Rep. 2015 Nov 26;5:17257 [PMID: 26607832]
  26. J Clin Invest. 2020 Jul 1;130(7):3750-3760 [PMID: 32271718]
  27. Nat Biomed Eng. 2023 Jun;7(6):797-810 [PMID: 36635418]
  28. Viruses. 2021 Jun 26;13(7): [PMID: 34206969]
  29. Brief Bioinform. 2022 Jan 17;23(1): [PMID: 34472593]
  30. Curr Opin Biotechnol. 2021 Apr;68:51-59 [PMID: 33126104]
  31. J Bacteriol. 1999 Aug;181(15):4452-60 [PMID: 10419939]
  32. J Exp Med. 1971 May 1;133(5):1105-17 [PMID: 4928818]
  33. Antimicrob Agents Chemother. 2017 May 24;61(6): [PMID: 28348152]
  34. Nat Biotechnol. 2022 Jun;40(6):921-931 [PMID: 35241840]
  35. Nucleic Acids Res. 2018 Jul 2;46(W1):W200-W204 [PMID: 29905871]
  36. Antimicrob Agents Chemother. 2019 Mar 27;63(4): [PMID: 30670427]
  37. Cell. 2020 Apr 16;181(2):475-483 [PMID: 32302574]
  38. Microbiol Mol Biol Rev. 2010 Sep;74(3):417-33 [PMID: 20805405]
  39. J Infect Dis. 2014 May 1;209(9):1469-78 [PMID: 24286983]
  40. J Biomed Sci. 2023 Apr 26;30(1):29 [PMID: 37101261]
  41. J Virol. 2013 Apr;87(8):4558-70 [PMID: 23408602]
  42. Science. 2009 Aug 28;325(5944):1089-93 [PMID: 19713519]
  43. Trials. 2017 Aug 31;18(1):404 [PMID: 28859690]
  44. Genome Res. 2015 Jul;25(7):1043-55 [PMID: 25977477]
  45. Food Microbiol. 2024 Feb;117:104401 [PMID: 37919009]
  46. IEEE/ACM Trans Comput Biol Bioinform. 2021 May-Jun;18(3):985-994 [PMID: 31751283]
  47. Antimicrob Agents Chemother. 2011 Feb;55(2):738-44 [PMID: 21098252]
  48. Int J Food Microbiol. 2017 Mar 6;244:19-26 [PMID: 28063330]
  49. J Invest Dermatol. 2020 Aug;140(8):1488-1497.e1 [PMID: 32407714]
  50. Nucleic Acids Res. 2014 Jan;42(Database issue):D1154-8 [PMID: 24265220]
  51. BMC Med Inform Decis Mak. 2021 May 3;21(Suppl 1):143 [PMID: 33941163]
  52. Sci Adv. 2020 Sep 2;6(36): [PMID: 32917596]
  53. Antimicrob Agents Chemother. 2023 May 17;67(5):e0151922 [PMID: 37098944]

Grants

  1. 32322082/National Natural Science Foundation of China
  2. 32072323/National Natural Science Foundation of China
  3. 32073022/National Natural Science Foundation of China
  4. 2023YFD1801000/National Key Research and Development Program of China
  5. 2022YFD1800903/National Key Research and Development Program of China
  6. SZYJY2022018/HZAU-AGIS Cooperation Fund
  7. 13210333/Training Program of Distinguished Agricultural Researcher
  8. 2023AFA111/Natural Science Foundation of Hubei Province
  9. 2022CFB659/Natural Science Foundation of Hubei Province
  10. /Young Top-notch Talent Cultivation Program of Hubei Province
  11. 2662024JC008/the Fundamental Research Funds for the Central Universities

MeSH Term

Artificial Intelligence
Animals
Mice
Staphylococcus aureus
Anti-Bacterial Agents
Disease Models, Animal
Bacteriophages
Software

Chemicals

Anti-Bacterial Agents

Word Cloud

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