Researchers have used an artificial intelligence (AI) model to redesign a crucial protein involved in the delivery of gene therapy, a technique that employs genes to treat, prevent or cure diseases.
The research, published in the journal Nature Machine Intelligence, optimised proteins to mitigate immune responses, thereby improving the efficacy of gene therapy and reducing side effects.
"Gene therapy holds immense promise, but the body's pre-existing immune response to viral vectors greatly hampers its success," said Michael Garton, an assistant professor at the University of Toronto, Canada.
"Our research zeroes in on hexons, a fundamental protein in adenovirus vectors, whichbut for the immune problemhold huge potential for gene therapy," Garton said.
Immune responses triggered by certain antibodies pose a significant obstacle in getting these vehicles to the right target which can result in reduced efficacy and severe adverse effects, the researchers said.
To overcome this shortcoming, Garton's lab used AI to custom-design variants of hexons that are distinct from natural sequences.
"We want to design something that is distant from all human variants and is, by extension, unrecognisable by the immune system," said Ph.D. candidate Suyue Lyu, who is lead author of the study.
Traditional methods of designing new proteins often involve extensive trial and error as well as mounting costs.
By using an AI-based approach for protein design, researchers can achieve a higher degree of variation, reduce costs and quickly generate simulation scenarios before homing in on a specific subset of targets for experimental testing.
While numerous protein-designing frameworks exist, it can be challenging for researchers to properly design new variants because of the lack of natural sequences available and hexons' relatively large sizeconsisting, on average, of 983 amino acids.
The team developed a different AI framework, dubbed ProteinVAE, that can be trained to learn the characteristics of a long protein using limited data.
Despite its compact design, ProteinVAE exhibits a generative capability comparable to larger available models, the researchers said.
"Our model takes advantage of pre-trained protein language models for efficient learning on small datasets. We also incorporated many tailored engineering approaches to make the model suitable for generating long proteins," said Lyu.
"Unlike other, considerably larger models that demand high computational resources to design a long protein, ProteinVAE supports fast training and inference on any standard GPUs. This feature could make the model more friendly for other academic labs," the researcher said.
The researchers noted that the AI model, validated through molecular simulation, demonstrates the ability to change a significant percentage of the protein's surface, potentially evading immune responses.
Garton believes the AI-model can be utilized beyond gene therapy protein design and could likely be expanded to support protein design in other disease cases as well.
"This work indicates that we are potentially able to design new subspecies and even species of biological entities using generative AI and these entities have therapeutic value that can be used in novel medical treatments," Garton added.