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bioRxiv · 10.64898/2025.12.02.691880

De Novo Computational Design of VHH Nanobodies Against LGR5

Xu, C.·Li, Y.·Nguyen, T.·Zhou, Y.·Cong, L.·Lee, T.-H.·Lang, Y.·Shek, R.·Yi, L.·Greisen, P.
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Abstract

VHH discovery traditionally relies on animal immunization or large-scale library screening, methods that are slow, costly, and often ineffective for challenging targets such as GPCRs. We present a fully de novo computational pipeline for epitope-directed VHH design, integrating generative backbone modeling, deep learning-based sequence optimization, and iterative experimental feedback. Using LGR5 as a model, we progressed from in silico design to functional binders without structural templates. Across three design-test-learn cycles, millions of candidates were reduced to epitope-specific binders with nanomolar affinity and high thermal stability (melting temperature [Tm] > 65 {degrees}C). Cryogenic electron microscopy (cryo-EM) confirmed atomic-level agreement (RMSD {approx} 2.2 [A]). This structure-validated approach accelerates timelines, reduces cost, and is broadly applicable to GPCRs and other membrane proteins, enabling "on-demand" therapeutic antibody generation.

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Xu, C., Li, Y., Nguyen, T., Zhou, Y., Cong, L., Lee, T.-H., Lang, Y., Shek, R., Yi, L., Greisen, P.. 2025-12-05. De Novo Computational Design of VHH Nanobodies Against LGR5. https://doi.org/10.64898/2025.12.02.691880

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