Deep generative design of neutralizing nanobodies againstSARS-CoV-2 variants

Abstract

In recent years, single-domain heavy chain antibodies (nanobodies), with only one-tenth the molecular weight of conventional antibodies, have emerged as important therapeutic proteins in the fight against SARS-CoV-2. However, the rapid mutation of the virus often renders existing nanobodies ineffective, underscoring the need to develop nanobodies that specifically target new variants. Traditional methods for discovering nanobodies are time-consuming and complex, making it difficult to efficiently identify nanobodies that bind to specific epitopes. To address this, we propose a de novo nanobody design method based on a Generative Adversarial Network (GAN). We developed a deep generative model, AiCDR, which consists of three discriminators and one generator. By determining the contribution of these three discriminators to the entire network, we can enhance the discrimination of generated sequences and reduce the similarity of the generated CDR3 sequences with natural peptides and random sequences, thereby ensuring their nature-like properties. These generated CDR3 sequences were then grafted onto humanized nanobody scaffolds, resulting in a structural library of approximately 104 nanobodies with natural-like properties. Using computational methods, we screened this library against the Spike (S) protein of the SARS-CoV-2 Omicron variant and identified 10 candidate nanobodies. Functional assays confirmed that two of these nanobodies exhibited neutralizing activity against the S protein. Our study demonstrates the potential of deep learning in nanobody design and offers a novel approach for developing nanobodies that target rapidly evolving viral variants.