Machine-Guided Directed Evolution of MAAP to Promote AAV Secretion
Gene therapy has been a rapidly emerging field of experimental therapeutics, wherein nucleic acids are delivered into cells via viral vectors in the effort to treat diseases associated with genetic defects. An increasing number of clinical trials have shown that the recombinant adeno-associated virus (rAAV) is an efficient vehicle for gene therapy. However, the low production capacity of rAAV has been a major bottleneck that decelerates AAV gene therapy development. To overcome this, the present project aims to target membrane-associated accessory protein (MAAP), which is known to serve a critical role in promoting secretion of AAV virion. Initial rounds of direct evolution of MAAP will be conducted to enrich functional variants in the construction of an AAV library. This library will further undergo next generation sequencing to recover functionally improved variants in comparison with wild type MAAP. The resulting sequence-function dataset will further train machine learning algorithms in predicting optimally functional variants. Ultimately, through both high-throughput and deep learning analyses, this project explores AAV engineering as an avenue of enhancing gene therapeutics production and delivery.
Message to Sponsor
- Major: Bioengineering
- Sponsor: Rose Hill Foundation
- Mentor: David Schaffer