Integrating AlphaFold2 with physics-based ensemble docking for high-efficiency nanobody discovery

Abstract

Nanobodies, the single-domain counterparts of traditional antibodies, are approximately one-tenth the size yet retain the ability to bind their target antigens tightly and specifically. A major practical bottleneck in developing functional nanobodies is the panning process required to identify them from the vast immunized cDNA libraries derived from camelids. To overcome this bottleneck, we developed a computational framework that progressed from next-generation sequencing (NGS)-derived candidate nanobody sequences to predicted structures using AlphaFold2, and prioritized nanobodies based on predicted binding energy scores. The nanobody-antigen binding poses were predicted using an ensemble docking strategy, which was selected over single-structure docking to better account for antigen conformational flexibility. We demonstrated that a physics-based docking method followed by MM/GBSA re-scoring delivered favorable performance in recovering native nanobody-antigen binding poses, outperforming sequence-only AlphaFold3 in our preliminary benchmark test. Applied to three antigen systems—Mesothelin (MSLN), PD-1, and Nectin-4—our computational workflow successfully prioritized candidate nanobodies. At least seven out of ten (70%) of the top-ranked candidates for each target exhibited strong binding by flow cytometry, with ELISA and surface plasmon resonance (SPR) further confirming nanomolar-level binding for representative candidates. Additionally, compared with conventional random-selection-based monoclonal clone picking, our workflow improved hit recovery while reducing redundancy, enabling the identification of functional nanobodies across a broader range of NGS copy-number ranks rather than only the most abundant post-panning clones. These results support the practical utility of the framework for enriching functional nanobodies from experimentally pre-enriched NGS-derived pools.