Encoding immune evasion to adeno-associated virus vectors for gene therapy
Project Description
Adeno-associated viruses (AAVs) are promising gene therapy vectors in clinics, given their safety profile and relatively high efficiency in delivery. However, AAVs found in nature are limited in use due to pre-existing neutralizing antibodies (NAbs) in patients. To overcome these limitations, a structure-based computational algorithm will be used to engineer AAV capsid sequences with enhanced immune evasion. Specifically, the algorithm will be applied to identify optimal recombination points in the capsid sequences of AAV natural serotypes to minimize structural disruption but maximize immunological epitope disruption. The project aims to create a library of chimeric, functional AAVs that can efficiently bypass NAbs as a stepping stone for efficient transduction in target cell populations.
Supervisor
ZHU, Bonnie Danqing
Quota
1
Course type
UROP1100
Applicant's Roles
1) Review the literature on (1) AAV biology, (2) structure-guided protein engineering, and (3) NAb epitope prediction algorithms;
2) Create an extensive database of reported and predicted NAb-binding epitope on the AAV capsid;
3) Redesign an existing structure-guided chimeric protein library design algorithm to incorporate mined NAb epitope information.
2) Create an extensive database of reported and predicted NAb-binding epitope on the AAV capsid;
3) Redesign an existing structure-guided chimeric protein library design algorithm to incorporate mined NAb epitope information.
Applicant's Learning Objectives
1) Understand AAV biology and its significance in gene therapy for treating incurable genetic disorders;
2) Apply and improve existing algorithms (e.g., physics-based, machine learning, neural networks, etc.) in protein engineering to design viral protein libraries.
3) Collaborate with lab members with diverse expertise to answer scientific questions grounded on applications in medicine.
4) Analyze and present research findings in a coherent manner.
2) Apply and improve existing algorithms (e.g., physics-based, machine learning, neural networks, etc.) in protein engineering to design viral protein libraries.
3) Collaborate with lab members with diverse expertise to answer scientific questions grounded on applications in medicine.
4) Analyze and present research findings in a coherent manner.
Complexity of the project
Challenging