期刊
ACS OMEGA
卷 5, 期 36, 页码 22847-22851出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsomega.0c02088
关键词
-
资金
- RIKEN engineering network fund
- NEDO [P15009]
- SIP (Technologies for Smart Bio-industry and Agriculture), JST CREST [JPMJCR1502]
- JST ERATO [JPMJER1903]
- Molecular Structure Characterization Unit, RIKEN Center for Sustainable Resource Science (CSRS)
Antimicrobial peptides are a potential solution to the threat of multidrug-resistant bacterial pathogens. Recently, deep generative models including generative adversarial networks (GANs) have been shown to be capable of designing new antimicrobial peptides. Intuitively, a GAN controls the probability distribution of generated sequences to cover active peptides as much as possible. This paper presents a peptide-specialized model called PepGAN that takes the balance between covering active peptides and dodging nonactive peptides. As a result, PepGAN has superior statistical fidelity with respect to physicochemical descriptors including charge, hydrophobicity, and weight. Top six peptides were synthesized, and one of them was confirmed to be highly antimicrobial. The minimum inhibitory concentration was 3.1 mu g/mL, indicating that the peptide is twice as strong as ampicillin.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据