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Precise, Specific, and Sensitive De Novo Antibody Design Across Multiple Cases

Bang, Y.; Cho, K.; Gu, J.; Kang, S.; Kwon, S.; Lee, D.; Lee, M. S.; Lee, S.; Lee, S. J.; Lim, J.; Maeng, J. Y.; Noh, J.; Oh, H.; Oh, S.; Park, S.; Park, T.; Sa, D. H.; Seok, C.; Song, M. Y.; Won, J.; Woo, H.; Yang, J.

2025-03-13 bioengineering
10.1101/2025.03.09.642274 bioRxiv
Show abstract

The precision design of antibodies, which naturally recognize diverse molecules through six variable loops, remains a critical challenge in therapeutic molecule discovery. In this study, we demonstrate that precise, sensitive, and specific antibody design can be achieved without prior antibody information across eight distinct target proteins. For each target, binders were identified from a yeast display scFv library of approximately 106 sequences, constructed by combining 102 designed light chain sequences with 104 designed heavy chain sequences. Binders with varying binding strengths were identified for all eight targets, including a case where no experimentally resolved target protein structure was available, demonstrating the highest level of precision compared to previous de novo antibody design reports. To further validate the designed antibodies, they were characterized in the IgG format for five distinct targets. The antibodies exhibited favorable developability properties, positioning them as promising hit or lead candidates. Notably, for one target in particular, the IgG-formatted antibodies exhibited affinity, activity, and developability, comparable to a commercial antibody, highlighting the sensitivity of the design method. Furthermore, binders capable of distinguishing closely related protein subtypes or mutants were identified, demonstrating that the method can achieve high molecular specificity. Cryo-EM analysis experimentally validated the designs accuracy by confirming that the key binding interactions were precisely as designed. These findings underscore the effectiveness of precision molecular design based on atomic-accuracy structure prediction. This study establishes computational antibody design as a viable approach for generating therapeutic molecules with tailored properties, with promising potential for achieving the efficacy and safety required for successful therapeutics.

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