ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
pepgan_first.pdf (398.09 kB)
0/0

Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks

preprint
submitted on 12.04.2020 and posted on 13.04.2020 by Andrejs Tucs, Duy Phuoc Tran, Akiko Yumoto, Yoshihiro Ito, Takanori Uzawa, Koji Tsuda

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 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.

History

Email Address of Submitting Author

tsuda@k.u-tokyo.ac.jp

Institution

University of Tokyo

Country

Japan

ORCID For Submitting Author

0000-0002-4288-1606

Declaration of Conflict of Interest

No conflict of interest

Exports