Deep learning to design immune-evading viral vectors for gene therapy

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Project Summary The delivery of gene therapies (DNA or RNA) using viral vectors is a promising therapeutic avenue for several indications, including genetic conditions, cancer, and neurodegenerative disease. Adeno-associated viral (AAV) vectors in particular have demonstrated efficacy and safety in several clinical trials, and have been approved for the treatment of two monogenic diseases by the FDA. However, a major bottleneck in expanding the reach of AAV-based therapies is optimizing vectors to avoid immune detection while preserving their function and tropism. AAV vector applications are significantly limited by immune targeting of viral capsids within a patient's body, resulting in accelerated clearance, loss of efficacy and dangerous hypersensitivity responses. As AAV is widely prevalent in the human population, these effects are often the result of immunological memory and thus more severe than a naïve response. Adverse effects are difficult to predict through pre-clinical experiments, increasing the risk posed to clinical trial patients. While immune recognition epitopes can sometimes be mapped to specific viral capsid residues, even high-throughput library-based strategies to ablate these epitopes are plagued by the creation of non-functional variants. To address this challenge, we propose to use a generative statistical model trained on natural sequence variation to design `smart' immune-evading AAV capsid libraries enriched in functional variants. This approach will integrate statistical models of the functional constraints on protein sequence with state-of-the-art predictors of T-cell and humoral immunogenicity to produce diverse libraries of therapeutically useful vector sequences. Data gathered from initial designed libraries will inform model optimization to iterate towards further improved viral vectors. Deimmunized, diverse vector libraries will address immune evasion at the initial stages of gene therapy development, accelerating progress towards safe and effective therapies. An algorithm that builds on observed sequences to generate diversity subject to immunogenicity constraints has broad implications for the development of all protein-based biotherapeutics, such as antibodies. This approach combines the advantages of protein therapies optimized through natural selection with precise control over desirable characteristics for human health applications.