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Intelligent De Novo Design of Novel Antimicrobial Peptides against Antibiotic-Resistant Bacteria Strains

Biology

Intelligent De Novo Design of Novel Antimicrobial Peptides against Antibiotic-Resistant Bacteria Strains

T. Lin, L. Yang, et al.

In a groundbreaking study, researchers including T.-T Lin and L.-Y Yang have harnessed a Wasserstein generative adversarial network to create novel antimicrobial peptides. With seven out of eight synthesized peptides exhibiting antibacterial properties, GAN-pep 3 stands out, showing impressive effectiveness against antibiotic-resistant strains. This research paves the way for innovative solutions to combat the pressing issue of antibiotic resistance.

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Playback language: English
Abstract
The rise of antibiotic-resistant bacteria necessitates the discovery of novel antimicrobial agents. This study leverages a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to design novel antimicrobial peptides (AMPs). The generated AMPs were evaluated in silico and in vitro. Seven out of eight synthesized peptides showed antibacterial activity, with GAN-pep 3 and GAN-pep 8 demonstrating broad-spectrum activity against antibiotic-resistant strains like methicillin-resistant Staphylococcus aureus and carbapenem-resistant Pseudomonas aeruginosa. GAN-pep 3 showed particularly low minimum inhibitory concentrations (MICs) against all tested bacteria, highlighting its potential as a promising candidate for further development.
Publisher
International Journal of Molecular Sciences
Published On
Apr 05, 2023
Authors
T.-T Lin, L.-Y Yang, C.-Y Lin, C.-T Wang, C.-W Lai, Chi-Fong Ko, Francisco Torrens, Antonio Rescifina, Tzu-Tang Lin, Yang-Hsin Shih, Shu-Hwa Chen, C.-F. Shih, Y.-H Chen
Tags
antimicrobial peptides
antibiotic resistance
Wasserstein GAN
bacterial activity
therapeutic candidates
in silico evaluation
broad-spectrum
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