NanoDesigner: Resolving the complex–CDR interdependency with iterative refinement

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract 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 complementary-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.

Authors 5

1. Melissa Maria Rios Zertuche

   King Abdullah University of Science and Technology

   Affiliation as printed

   Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia

2. Şenay Kafkas

   King Abdullah University of Science and Technology

   Affiliation as printed

   KAUST Academy, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia

3. Dominik Renn

   King Abdullah University of Science and Technology

   Affiliation as printed

   KAUST Catalysis Center (KCC), Division of Physical Sciences and Engineering, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia

4. Magnus Rueping Aachen Institute for Experimental Molecular Imaging

   RWTH Aachen University · Universitätsklinikum Aachen · King Abdullah University of Science and Technology

   Affiliation as printed

   Institute for Experimental Molecular Imaging (ExMI), University Clinic, RWTH Aachen, Forckenbeckstraße 55, D-52074, Aachen, Germany

   KAUST Catalysis Center (KCC), Division of Physical Sciences and Engineering, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia

   KAUST Center of Excellence for Generative AI, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia

   KAUST Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia

5. Robert Hoehndorf corresponding

   King Abdullah University of Science and Technology

   Affiliation as printed

   Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia

   Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia

   KAUST Center of Excellence for Generative AI, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia

   KAUST Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia

   SDAIA–KAUST Center of Excellence in Data Science and Artificial Intelligence, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia