Team:Harvard
A COVID-19 Antibody Therapeutic Based on Machine Learning and DNA Origami Sequence Delivery
MOTbox is a COVID-19 therapeutic that couples machine learning and DNA origami to design an optimized anti-SARS-CoV-2 antibody and deliver its mRNA sequence to immune cells in infected patients. It is intended to serve as an interim treatment in a pandemic scenario that can be manufactured cheaply and quickly with limited lab access while a vaccine is developed. Using ensemble machine learning and differential evolution algorithms, we optimized anti-SARS-CoV-2 antibody sequences to enhance binding affinity and therapeutic potential. We designed and computationally validated a novel DNA origami nanostructure to selectively deliver the optimized antibody sequences to immune cells for rapid antibody production in vivo. The high potency of the optimized antibodies and the specificity of DNA origami delivery reduce the minimum therapeutic dose, also reducing treatment cost. Our work is a proof-of-concept of a rapid, cost-effective antibody treatment for COVID-19 that can also be extended to treating other emerging diseases.
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