Prediction of Adeno-Associated Virus Fitness with a Protein Language-Based Machine Learning Model

https://www.liebertpub.com/doi/10.1089/hum.2024.227?utm_campaign=MAL_HUM&utm_medium=email&_hsenc=p2ANqtz-9kGryx3OKOIJFIynFViEMH_tYRKYMLCnSRENlLTiU3gwCZYwyzfBqXIVSLZLL28t3V_LT1SRrc7rELLEp-igMDYP_whw&_hsmi=357188884&utm_content=357188884&utm_source=hs_email Machine Learning Model to Predict the Fitness of AAV Capsids for Gene Therapy New Rochelle, NY, April 17, 2025—A new study in the peer-reviewed journal Human Gene Therapy describes a machine learning (ML) model that can be used as a surrogate for laborious in vitro experiments. This in silico approach aims to increase the fitness of clinical adeno-associated virus (AAV) capsids to make gene therapies more economically viable for patients. Click here to read the article now. Developing AAV capsids with improved yield, or fitness, is a key strategy for reducing manufacturing costs in order to make gene therapies more affordable. Christian Mueller and coauthors from Sanofi describe a state-of-the-art ML model that predicts the fitness of AAV2 capsid mutants based on the amino acid sequence of the capsid monomer. “By combining a protein language model (PLM) and classical ML techniques, our model achieved a significantly high prediction accuracy (Pearson correlation = 0.818) for capsid fitness,” stated the investigators. “Importantly, tests on completely independent datasets showed robustness and generalizability of our model, even for multi-mutant AAV capsids.” “The emergence of artificial intelligence (AI)-based approaches is an exciting development in capsid engineering that has the potential to be more systematic, comprehensive, and cost-effective than traditional directed evolution- and rational design-based strategies. The study by Wu et al. is a great step forward in developing AI tools for the gene therapy field,” says Managing Editor of Human Gene Therapy Thomas Gallagher, PhD, from the University of Massachusetts Chan Medical School.