Machine learning assisted AAV capsid bioengineering for enhanced EV loading and tissue specificity

在合成生物学领域，科学家们遵循工程范式重新设计适用于医疗保健、环境和工业的生物系统。受自然选择的启发，定向进化作为一种成功的蛋白质工程方法脱颖而出，特别是在缺乏对潜在生物物理过程的全面理解的情况下。指导进化的一个关键组成部分在于确定合适的筛选来分离具有所需属性的蛋白质变体。然而，由于需要探索的序列空间很大，这些筛选既耗时又昂贵。机器学习（ML）有望通过从先前研究的蛋白质变体中学习模式来减少序列空间，从而预测具有增强功能的新蛋白质变体。在这里，我们建议开发和应用一种ml引导的定向进化策略来设计腺相关病毒（AAV）衣壳蛋白，增强其基因治疗的适应性。aav目前是提供基因治疗的黄金标准，有望治疗罕见疾病。然而，aav递送的基因疗法受到免疫原性和生物利用度问题的限制，限制了对一小部分患者的治疗。除了对基于aav的基因治疗的影响之外，这项工作还将通过展示机器学习如何有效地应对挑战和加速发现，为更广泛的生物科学领域做出贡献。该项目属于以下UKRI-BBSRC优先事项：数据驱动生物学，生物科学技术开发，工业生物技术和合成生物学的新战略方法。我们将运用数据科学中的方法，如线性回归模型和神经网络，来分析蛋白质的序列-函数关系。利用迁移学习技术，我们将开发一种新的机器学习指导的定向进化方法，这将使我们能够使蛋白质变体多样化。这种ml引导的定向进化方法将进一步允许我们设计具有增强细胞外囊泡（EV）装载能力的AAV衣壳蛋白。通过优化与电动汽车相关的自动驾驶汽车（ev - aav）的生产，我们将克服阻碍EVAAV技术实现工业规模生产、制造成本和达到所需产量的主要瓶颈。最后，在合成生物学领域，科学家们遵循工程范式重新设计自然界中发现的物质，如病毒。遵循生物工程范式，我们将通过测量，挖掘，建模和操纵在定向进化过程中重新设计aav的步骤循环。

In the field of synthetic biology, scientists follow engineering paradigms to redesign biological systems with applicability in healthcare, the environment, and industry. Inspired by natural selection, directed evolution stands out as a successful approach to protein engineering, particularly when comprehensive understanding of the underlying biophysical processes is lacking. A key component of directing evolution lies in identifying suitable screens to isolate protein variants with desired attributes. However, these screens can be time-consuming and costly due to the vast sequence space to be explored. Machine learning (ML) holds the promise of reducing the sequence space by learning patterns from previously studied protein variants to predict new protein variants with enhanced capabilities. Here, we propose to develop and apply an ML-guided directed evolution strategy to engineer adeno-associated virus (AAV) capsid proteins with enhanced suitability for gene therapy. AAVs are currently the gold standard for delivering gene therapies, which hold promise for treating rare diseases. However, AAV-delivered gene therapies are limited by immunogenicity and bioavailability issues, restricting treatment to a subset of patients. Beyond its implications for AAV-based gene therapy, this work will contribute to the broader bioscience landscape by showcasing how ML can efficiently address challenges and accelerate discovery.BBSRC priority areas This project falls under the following UKRI-BBSRC Priorities: data-driven biology, technology development for the biosciences, new strategic approaches to industrial biotechnology and synthetic biology. We will apply methods used in data science, such as linear regression models and neural networks, to analyze the sequence-function relationship of proteins. Using transfer learning technology, we will develop a novel ML-guided directed evolution approach, which will allow us to diversify protein variants. This ML-guided directed evolution approach will further allow us to design AAV capsid proteins with enhanced extracellular vesicle (EV) loading capacity. Through optimizing the production of EV-associated AAVs (EV-AAVs), we will overcome the major bottlenecks keeping EVAAV technology from industrial scale production, cost to manufacture and achieving required yields. Finally, in the field of synthetic biology, scientists follow engineering paradigms to redesign agents found in nature, such as viruses. Following the biological engineering paradigm, we will cycle through steps of measuring, mining, modeling and manipulating during directed evolution to redesign AAVs.