deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model.

Journal: PeerJ
Published Date: Jul 19, 2024

Abstract

BACKGROUND: Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development.

Authors

  • Fei Zhao
  • Junhui Qiu
    Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China.
  • Dongyou Xiang
    Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China.
  • Pengrui Jiao
    Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China.
  • Yu Cao
    Department of Neurosurgery, FuShun County Zigong City People's Hospital, Fushun, China.
  • Qingrui Xu
    Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China.
  • Dairong Qiao
    Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China.
  • Hui Xu
    No 202 Hospital of People's Liberation Army, Liaoning 110003, China.
  • Yi Cao
    Department of Dermatology, First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

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