Rios Zertuche Melissa Maria, Kafkas Şenay, Renn Dominik, Rueping Magnus, Hoehndorf Robert
Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia.
KAUST Beacon Development, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia.
J Cheminform. 2025 Aug 7;17(1):120. doi: 10.1186/s13321-025-01069-2.
Camelid heavy-chain only antibodies consist of two heavy chains and single variable domains (VHHs), which retain antigen-binding functionality even when isolated. The term "nanobody" is now more generally used for describing small, single-domain antibodies. Several antibody generative models have been developed for the sequence and structure co-design of the complementarity-determining regions (CDRs) based on the binding interface with a target antigen. However, these models are not tailored for nanobodies and are often constrained by their reliance on experimentally determined antigen-antibody structures, which are labor-intensive to obtain. Here, we introduce NanoDesigner, a tool for nanobody design and optimization based on generative AI methods. NanoDesigner integrates key stages-structure prediction, docking, CDR generation, and side-chain packing-into an iterative framework based on an expectation maximization (EM) algorithm. The algorithm effectively tackles an interdependency challenge where accurate docking presupposes a priori knowledge of the CDR conformation, while effective CDR generation relies on accurate docking outputs to guide its design. NanoDesigner approximately doubles the success rate of de novo nanobody designs through continuous refinement of docking and CDR generation.
骆驼科仅重链抗体由两条重链和单个可变结构域（VHH）组成，即使分离后仍保留抗原结合功能。现在，“纳米抗体”一词更广泛地用于描述小型单结构域抗体。已经开发了几种抗体生成模型，用于基于与靶抗原的结合界面进行互补决定区（CDR）的序列和结构协同设计。然而，这些模型并非针对纳米抗体量身定制，并且常常受到其对实验确定的抗原-抗体结构的依赖的限制，而获得这些结构需要耗费大量人力。在此，我们介绍了NanoDesigner，这是一种基于生成式人工智能方法的纳米抗体设计和优化工具。NanoDesigner将关键阶段——结构预测、对接、CDR生成和侧链堆积——整合到一个基于期望最大化（EM）算法的迭代框架中。该算法有效应对了一个相互依赖的挑战，即准确的对接以CDR构象的先验知识为前提，而有效的CDR生成则依赖于准确的对接输出以指导其设计。通过对接和CDR生成的持续优化，NanoDesigner使从头设计纳米抗体的成功率提高了约一倍。
