Artificial Intelligence-Driven Nanobody Engineering for Pathogen Recognition: From Functional Epitope Prioritization to Multimodal Foundation Models
- 1Innovation Team for Information Science and Biomedicine, School of Medicine, Sichuan University of Arts and Science, Dazhou 635000, China
- 2Department of Parasitology, Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
- 3School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
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.
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BibTeX
@article{hu2026artificial,
title = {{Artificial Intelligence-Driven Nanobody Engineering for Pathogen Recognition: From Functional Epitope Prioritization to Multimodal Foundation Models}},
author = {Rui-Si Hu and Muhammad Ehsan and Wei Luo and Jian Zhou and Yong-Le Xiao and He-Xin Wang and Cheng-Yang Li and Kui Gu},
journal = {Biomolecules},
year = {2026},
month = oct,
doi = {10.3390/biom16101479},
url = {https://www.mdpi.com/2218-273X/16/10/1479},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence-Driven Nanobody Engineering for Pathogen Recognition: From Functional Epitope Prioritization to Multimodal Foundation Models
AU - Rui-Si Hu
AU - Muhammad Ehsan
AU - Wei Luo
AU - Jian Zhou
AU - Yong-Le Xiao
AU - He-Xin Wang
AU - Cheng-Yang Li
AU - Kui Gu
PY - 2026
DA - 2026/10/01
JO - Biomolecules
DO - 10.3390/biom16101479
UR - https://www.mdpi.com/2218-273X/16/10/1479
AB - 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.
SN - 2218-273X
ER -