Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks
| Autor: | Akiko Yumoto, Takanori Uzawa, Koji Tsuda, Yoshihiro Ito, Duy Phuoc Tran, Andrejs Tucs |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | ACS Omega ACS Omega, Vol 5, Iss 36, Pp 22847-22851 (2020) |
| ISSN: | 2470-1343 |
| Popis: | 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 anddodging non-active 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 3.1μg/mL, indicating that the peptide is twice as strong as ampicillin. |
| Databáze: | OpenAIRE |
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