tetano
Editor, Senior Moderator
Comput Struct Biotechnol J
. 2025 Jun 27:27:2915-2923.
doi: 10.1016/j.csbj.2025.06.026. eCollection 2025. AI-based antibody design targeting recent H5N1 avian influenza strains
Nicholas Santolla[SUP] 1 [/SUP], Colby T Ford[SUP] 1 2 3 4 [/SUP]
Affiliations
In 2025 alone, H5N1 avian influenza is responsible for thousands of infections across various animal species, including avian and mammalian livestock such as chickens and cows, and poses a threat to human health due to avian-to-mammalian transmission. There have been 70 human cases of H5N1 influenza in the United States since April 2024 and, as shown in recent studies, our current antibody defenses are waning. Thus, it is imperative to discover new therapeutics in the fight against more recent strains of the virus. In this study, we present the Frankies framework for automated antibody diffusion and assessment. This pipeline was used to automate the generation of 30 novel anti-HA1 Fv antibody fragment sequences, fold them into 3-dimensional structures, and then dock against a recent H5N1 HA1 antigen structure for binding evaluation. Here we show the utility of artificial intelligence in the discovery of novel antibodies against specific H5N1 strains of interest, which bind similarly to known therapeutic and elicited antibodies.
Keywords: AI; Antibodies; Avian influenza; Docking; Protein modeling.
. 2025 Jun 27:27:2915-2923.
doi: 10.1016/j.csbj.2025.06.026. eCollection 2025. AI-based antibody design targeting recent H5N1 avian influenza strains
Nicholas Santolla[SUP] 1 [/SUP], Colby T Ford[SUP] 1 2 3 4 [/SUP]
Affiliations
- PMID: 40677246
- PMCID: PMC12270607
- DOI: 10.1016/j.csbj.2025.06.026
In 2025 alone, H5N1 avian influenza is responsible for thousands of infections across various animal species, including avian and mammalian livestock such as chickens and cows, and poses a threat to human health due to avian-to-mammalian transmission. There have been 70 human cases of H5N1 influenza in the United States since April 2024 and, as shown in recent studies, our current antibody defenses are waning. Thus, it is imperative to discover new therapeutics in the fight against more recent strains of the virus. In this study, we present the Frankies framework for automated antibody diffusion and assessment. This pipeline was used to automate the generation of 30 novel anti-HA1 Fv antibody fragment sequences, fold them into 3-dimensional structures, and then dock against a recent H5N1 HA1 antigen structure for binding evaluation. Here we show the utility of artificial intelligence in the discovery of novel antibodies against specific H5N1 strains of interest, which bind similarly to known therapeutic and elicited antibodies.
Keywords: AI; Antibodies; Avian influenza; Docking; Protein modeling.