Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation,Journal of Chemical Information and Modeling

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Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-08-28 , DOI: 10.1021/acs.jcim.4c01020
Vishva Saravanan R ₁ , Soham Choudhuri ₁ , Bhaswar Ghosh ₁

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The convergence of biotechnology and artificial intelligence has the potential to transform drug development, especially in the field of therapeutic peptide design. Peptides are short chains of amino acids with diverse therapeutic applications that offer several advantages over small molecular drugs, such as targeted therapy and minimal side effects. However, limited oral bioavailability and enzymatic degradation have limited their effectiveness. With advances in deep learning techniques, innovative approaches to peptide design have become possible. In this work, we demonstrate HYDRA, a hybrid deep learning approach that leverages the distribution modeling capabilities of a diffusion model and combines it with a binding affinity maximization algorithm that can be used for de novo design of peptide binders for various target receptors. As an application, we have used our approach to design therapeutic peptides targeting proteins expressed by Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) genes. The ability of HYDRA to generate peptides conditioned on the target receptor’s binding sites makes it a promising approach for developing effective therapies for malaria and other diseases.

中文翻译：

用于稳定、亲和力驱动、受体感知肽生成的混合扩散模型

生物技术和人工智能的融合有可能改变药物开发，特别是在治疗性肽设计领域。肽是具有多种治疗应用的短链氨基酸，与小分子药物相比具有多种优势，例如靶向治疗和最小的副作用。然而，有限的口服生物利用度和酶降解限制了它们的有效性。随着深度学习技术的进步，肽设计的创新方法已成为可能。在这项工作中，我们展示了 HYDRA，这是一种混合深度学习方法，它利用扩散模型的分布建模功能，并将其与结合亲和力最大化算法相结合，该算法可用于从头设计各种目标受体的肽结合物。作为一项应用，我们使用我们的方法设计了针对恶性疟原虫红细胞膜蛋白 1 (PfEMP1) 基因表达的蛋白质的治疗性肽。 HYDRA 能够产生以靶受体结合位点为条件的肽，这使其成为开发疟疾和其他疾病有效疗法的有前途的方法。

更新日期：2024-08-29

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