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

项目摘要 利用病毒载体进行基因治疗(dna或rna)是一条很有前途的治疗途径。 几种适应症，包括遗传疾病、癌症和神经退行性疾病。 尤其是腺相关病毒(AAV)载体在 几项临床试验，并已被批准用于治疗两种单基因疾病 食品和药物管理局。然而，扩大基于AAV的疗法覆盖范围的一个主要瓶颈是 优化载体以避免免疫检测，同时保持其功能和取向。AAV 载体的应用受到患者体内病毒衣壳免疫靶向的显著限制 身体，导致加速清除，失去效力和危险的过敏症 回应。由于AAV在人群中广泛流行，这些影响往往是 免疫记忆力下降，因此比天真的反应更严重。不良反应是 难以通过临床前试验进行预测，增加了临床试验的风险 病人。虽然免疫识别表位有时可以映射到特定的病毒衣壳 残基，即使是基于高通量文库的策略来去除这些表位也受到以下问题的困扰 非功能变种的产生。为了应对这一挑战，我们建议使用生成式 基于自然序列变异训练的统计模型设计“智能”免疫逃避AAV 衣壳蛋白文库富含功能变体。此方法将整合以下统计模型 T细胞和蛋白质序列预测因子对蛋白质序列的功能约束 体液免疫原性，以产生治疗有用的载体序列的不同文库。 从初始设计库收集的数据将为模型优化提供信息，以便迭代 进一步改进了病毒载体。去免疫的、多样化的载体库将解决免疫逃避问题 在基因治疗发展的初始阶段，加快向安全和 有效的治疗方法。一种建立在观测序列上以生成分集主题的算法 对免疫原性的限制对ALL的发展具有广泛的意义 生物治疗学，例如抗体。这种方法结合了蛋白质疗法的优点。 通过自然选择进行优化，精确控制人类所需的特征 健康应用程序。

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.