Department of Computer Science, University of Vermont, Burlington, Vermont 05405, United States.
Department of Chemistry, University of Vermont, Burlington, Vermont 05405, United States.
J Chem Inf Model. 2021 May 24;61(5):2198-2207. doi: 10.1021/acs.jcim.0c01441. Epub 2021 Mar 31.
Antibiotic resistance is a critical public health problem. Each year ∼2.8 million resistant infections lead to more than 35 000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applications of known AMPs have encountered issues in development, production, and shelf-life. To drive the development of AMP-based treatments, it is necessary to create design approaches with higher precision and selectivity toward resistant targets. Previously, we developed AMPGAN and obtained proof-of-concept evidence for the generative approach to design AMPs with experimental validation. Building on the success of AMPGAN, we present AMPGAN v2, a bidirectional conditional generative adversarial network (BiCGAN)-based approach for rational AMP design. AMPGAN v2 uses generator-discriminator dynamics to learn data-driven priors and controls generation using conditioning variables. The bidirectional component, implemented using a learned encoder to map data samples into the latent space of the generator, aids iterative manipulation of candidate peptides. These elements allow AMPGAN v2 to generate candidates that are novel, diverse, and tailored for specific applications, making it an efficient AMP design tool.
抗生素耐药性是一个严重的公共卫生问题。仅在美国，每年就有约 280 万例耐药感染导致超过 3.5 万人死亡。抗菌肽 (AMPs) 在治疗耐药感染方面显示出前景。然而，已知 AMPs 的应用在开发、生产和保质期方面遇到了问题。为了推动基于 AMP 的治疗方法的发展，有必要创建针对耐药靶标具有更高精度和选择性的设计方法。此前，我们开发了 AMPGAN ，并通过实验验证获得了针对 AMP 设计的生成方法的概念验证证据。在 AMPGAN 的成功基础上，我们提出了 AMPGAN v2 ，这是一种基于双向条件生成对抗网络 (BiCGAN) 的合理 AMP 设计方法。AMPGAN v2 使用生成器-判别器动力学来学习数据驱动的先验知识，并使用条件变量来控制生成。双向组件使用学习到的编码器将数据样本映射到生成器的潜在空间，有助于迭代地操作候选肽。这些元素使 AMPGAN v2 能够生成新颖、多样化且针对特定应用量身定制的候选肽，使其成为一种高效的 AMP 设计工具。
