RT Journal Article
SR Electronic
T1 Using machine learning to design adeno-associated virus capsids with high likelihood of viral assembly
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.18.444607
DO 10.1101/2021.05.18.444607
A1 Cuong T. To
A1 Christian Wirsching
A1 Andrew D. Marques
A1 Sergei Zolotukhin
YR 2021
UL http://biorxiv.org/content/early/2021/06/10/2021.05.18.444607.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.