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- Artificial Intelligence/Machine Learning
Systematic Multi-trait AAV Capsid Engineering for Efficient Gene Delivery
Eid et al., Nature Communications. 2024.
https://doi.org/10.1038/s41467-024-50555-y
Engineering novel functions into proteins while retaining desired traits is a key challenge for developers of viral vectors, antibodies, and inhibitors of medical and industrial value. In this study, investigators developed Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait adeno-associated virus (AAV) capsids. Fit4Function was used to generate reproducible screening data from a capsid library that samples the entire manufacturable sequence space. The Fit4Function data were used to train accurate sequence-to-function models, which were combined to develop a library of capsid candidates. Compared to AAV9, top candidates from the Fit4Function capsid library exhibited comparable production yields; more efficient murine liver transduction; up to 1,000-fold greater human hepatocyte transduction; and increased enrichment in a screen for liver transduction in macaques. The Fit4Function strategy enables prediction of peptide-modified AAV capsid traits across species and is a critical step toward assembling an ML atlas that predicts AAV capsid performance across dozens of traits. Supported by ORIP (P51OD011107, U42OD027094), NIDDK, NIMH, and NINDS.
The Monarch Initiative in 2024: An Analytic Platform Integrating Phenotypes, Genes and Diseases Across Species
Putman et al., Nucleic Acids Research. 2024.
https://pubmed.ncbi.nlm.nih.gov/38000386/
The Monarch Initiative aims to bridge the gap between the genetic variations, environmental determinants, and phenotypic outcomes critical for translational research. The Monarch app provides researchers access to curated data sets with information on genes, phenotypes, and diseases across species and advanced analysis tools for such diverse applications as variant prioritization, deep phenotyping, and patient profile matching. Researchers describe upgrades to the app, including scalable cloud-based infrastructure, simplified data ingestion and knowledge graph integration systems, enhanced data mapping and integration standards, and a new user interface with novel search and graph navigation features. A customized plugin for OpenAI’s ChatGPT allows the use of large language models to interrogate knowledge in the Monarch graph and increase the reliability of the responses of Monarch’s analytic tools. These upgrades will enhance clinical diagnosis and the understanding of disease mechanisms. Supported by ORIP (R24OD011883), NLM, and NHGRI.