Generative Deep Learning Design of Single-Domain Antibodies Against Venezuelan Equine Encephalitis Virus

Background/Objectives: Venezuelan equine encephalitis virus (VEEV) represents a significant biothreat with no FDA-approved vaccine currently available, highlighting the need for alternative therapeutic strategies. Single-domain antibodies (sdAbs) present a potential alternative to conventional antibodies, due to their small size and ability to recognize cryptic epitopes. Methods: This research describes the development and preliminary evaluation of VEEV-binding sdAbs generated using a generative artificial intelligence (AI) platform. Using a dataset of known alphavirus-binding sdAbs, the AI model produced sequences with predicted affinity for the E2 glycoprotein of VEEV. These candidate sdAbs were expressed in a bacterial periplasmic system and purified for initial assessment. Results: Enzyme-linked immunosorbent assays (ELISAs) indicated binding activity of the sdAbs to VEEV antigens. In vitro neutralization tests suggested inhibition of VEEV infection in cultured cells for some of the candidates. Conclusions: This study demonstrates how generative AI can expedite antiviral therapeutic development and establishes a framework for quick responses to emerging viral threats when extensive example databases are unavailable. Additional refinement and validation of AI-generated sdAbs could establish effective VEEV therapeutics.