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bioRxiv · 10.1101/2025.06.08.658414

Computational nanobody design using graph neural networks and Metropolis Monte Carlo sampling

Wang, L.·He, X.·Guo, G.·Qian, X.·Huang, Q.
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Abstract

Nanobodies have emerged as promising protein therapeutics due to their high-stability, low immunogenicity, and ease of production. However, experimental screening of high-affinity nanobodies for specific antigens and their post optimization remain costly and time-consuming, mainly due to the large number of possible variants. Here, we developed a computational approach that integrates graph neural networks (GNNs) with Monte Carlo Metropolis algorithm for nanobody design. We constructed a GNN model, AiPPA, to predict the protein-protein binding free energy (BFE) without requiring the complex structure, achieving a Pearson correlation of 0.62 on benchmark. We then combined AiPPA with Metropolis importance sampling to design low-BFE nanobodies from a non-affinity template. We applied this method to the antigen TL1A, and generated two affinity nanobodies. This work establishes a physics-informed deep learning method for computational nanobody design, providing a novel development strategy for protein therapeutics.

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Wang, L., He, X., Guo, G., Qian, X., Huang, Q.. 2025-06-08. Computational nanobody design using graph neural networks and Metropolis Monte Carlo sampling. https://doi.org/10.1101/2025.06.08.658414

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