Artificial Intelligence-Driven Nanobody Engineering for Pathogen Recognition: From Functional Epitope Prioritization to Multimodal Foundation Models

Abstract

Nanobodies are compact and stable proteins derived from the variable domain of camelid heavy-chain-only antibodies (VHH). Advances in structural biology, molecular modeling, and artificial intelligence (AI) are shifting VHH discovery from empirical selection toward data-driven design. However, predicted binding alone does not ensure biological activity or successful translation, particularly for antigenically variable pathogens. Complementing recent reviews that broadly survey computational VHH modeling and optimization, this review focuses on how antigen biology and functional epitope prioritization can guide the design of pathogen-targeting VHHs. We organize the field along a structure-function-translation continuum, connecting the structural basis of VHH recognition and available data resources with conventional discovery, antigen-guided de novo design, and sequence-structure learning. We further examine how developability, recombinant production, multispecificity, humanization, structural dynamics, and antigenic variation shape the progression from predicted binders to functional VHHs. Finally, we discuss how multimodal foundation models could integrate these dimensions to support the design of functional, manufacturable, and escape-resilient VHHs.