MACHINE LEARNING GUIDED DESIGN OF VIRAL VECTOR LIBRARIES
Researchers at UC Berkeley have developed a machine learning model that can aide in the design of more efficient viral vector libraries.
Directed evolution of biomolecules to generate large numbers of randomized variants is an important innovation in biochemistry. This methodology can be applied to myriad biomolecules of interest, including viruses. In the case of viral variants, this method may be used to select viral variants or viral vectors with specific properties such as tissue type specificity, increased replication capacity, or enhanced evasion of the immune system. However, testing large numbers of viral variants for specific properties is inherently time consuming and limits potential innovation.
Stage of Research
The inventors have devised a new method to optimize the functionality of viral libraries with many random variants. Specifically, this methodology comprises a machine learning model that systematically designs more effectively starting libraries by optimizing for a chosen factor. This method works by using a training set of viruses that can be evaluated experimentally for the chosen optimization factor (e.g., packaging efficiency, infectivity of a cell line, etc.). These experiments will then provide a fitness value for each viral variant, and the fitness value matched with viral variant sequences will in turn be used in a supervised machine learning model to select sequences for a larger library that is optimized for the chosen factor.
Applications
- Construction of optimized viral vector or viral variant libraries to enable downstream screening in laboratory settings.
- Selection of advantageous adeno-associated viral variants for human gene therapy
Advantages
- Broad number of optimizable factors to choose from
- Higher starting average fitness value of viral variants in a given library
- Reduction in cost for experiments due to less failure of individual viral variant experiments
Stage of Development
Research- in vitro
Publications
PCT/US2022/048736
Related Web Links
N/A
Keywords
Vector, machine learning, recombinant Adenoviral-Associated Virus (rAAV)
Technology Reference
CZ Biohub SF ref. no. CZB-226B
UC Berkeley ref. no. BK2022-010
