Using Machine Learning to design AAV2 with high likelihood of viral assembly

To, C. T.; Wirsching, C.

doi:10.1101/2021.05.18.444607

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bioRxiv · 10.1101/2021.05.18.444607

Using Machine Learning to design AAV2 with high likelihood of viral assembly

To, C. T.·Wirsching, C.

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Abstract

We study the application of machine learning in designing adeno-associated virus (AAV) capsid sequences with high likelihood of viral assembly, i.e. capsid viability. Specifically, we design and implement Origami, a model-based optimization algorithm, to identify highly viable capsid sequences within the vast space of 2033 possibilities. Our evaluation shows that Origami performs well in terms of optimality and diversity of model-designed sequences. Moreover, these sequences are ranked according to their viability score. This helps designing experiments given budget constraint.

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To, C. T., Wirsching, C.. 2021-05-18. Using Machine Learning to design AAV2 with high likelihood of viral assembly. https://doi.org/10.1101/2021.05.18.444607

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