
본 연구는 딥러닝 기반 생성 모델과 활성 예측 모델을 결합하여 세균과 바이러스에 동시에 효과적인 이중 기능성 항균 펩타이드를 개발하였다.


coremsg

Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target bio-membranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distributions in a vast peptide universe, especially for peptides that demonstrate potencies for both bacterial membranes and viral envelopes. Here we establish a de novo AMP design framework by bridging a deep generative module and a graph-encoding activity regressor. The generative module learns hidden ‘grammars’ of AMP features and produces candidates sequentially pass antimicrobial predictor and antiviral classifiers. We discover three bifunctional AMPs and experimentally validated their abilities to inhibit a spectrum of pathogens in vitro and in animal models. Notably, P076 is a highly potent bactericide with the minimal inhibitory concentration of 0.21 μM against multidrug-resistant A. baumannii , while P002 broadly inhibits five enveloped viruses. Our study provides feasible means to uncover sequences that simultaneously encode antimicrobial and antiviral activities, thus bolstering the function spectra of AMPs to combat a wide range of drug-resistant infections.

### Competing Interest Statement

The authors have declared no competing interest.

Exploring the repository of de novo designed bifunctional antimicrobial peptides through deep learning

세계-최초-인공지능-기반-이중-기능성-항균-펩타이드-개발-및-실험적-검증


세계 최초 인공지능 기반 이중 기능성 항균 펩타이드 개발 및 실험적 검증
