Machine Learning to improve the outputs of an antibody synthetic library

机器学习提高抗体合成库的输出

• 批准号: 2736498
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2736498
• 关键词: Machine; Learning; improve; outputs; antibody

Antibodies are essential proteins of the adaptive immune system that bind to their target proteins, called antigens, with great specificity and affinity. Antibodies are one of the most important classes of pharmaceuticals with over 100 antibody therapeutics approved. However, the majority of therapeutic antibody candidates fail before regulatory approval and the antibodies that do eventually progress cost more than $2bn to develop and can typically take around 10 years to bring to market. This time-consuming and cost-intensive process of developing these therapeutics would benefit from computational and machine learning-driven methods for predicting antibody properties. This offers an immense promise for the successful development of next-generation biologics. This project will focus on machine learning techniques to select antibodies with the best biophysical properties. The project will be performed alongside experts in the field of applying machine learning to antibody development at the Oxford Protein Informatics Group of Professor Charlotte Deane and in collaboration with Fusion Antibodies. Fusion Antibodies have been experts in the antibody space for more than 20 years. Besides from harnessing this expertise, Fusion Antibodies will provide a wealth of valuable data linking antibody sequences to expression and various biophysical properties. This data will be used to design and create machine learning protocols and will be beneficial for this project as these types of datasets are rarely available to the public. Furthermore, experimental validation of algorithms could be performed by the company. This project will initially focus on the biophysical property immunogenicity and expression. Animal models are often used to derive therapeutic antibodies potentially leading to an immune response in humans. Humanising is the process of making to antibody more human-like while maintaining antibody expression levels and its ability to bind their target epitope with high affinity. To humanise the antibody the complementarity-determining region, the antibody variable domain involved in binding the target, of the animal derived antibody is grafted into a human framework. Fusion Antibodies has performed this task, and applied structure- and expertise-driven back mutations, for a set of targets. In order to generate a predictive algorithm of this antibody property of interest based on this dataset the value of the dataset should be evaluated. This should provide insights into volume, diversity, and the type of data needed to generalise predictions. This project will contribute to maximizing efficacy in a shorter timeframe and at a reduced cost by guiding antibody therapeutic development towards antibodies with better biophysical properties. Machine learning and artificial intelligence technologies would be used to create predictive algorithms for biophysical properties. The project is interdisciplinary and involves immunoinformatics, machine learning, (structure-based) antibody design, as well as experimental validations. Therefore, this project falls within the EPSRC research areas: Chemical biology and biological chemistry, Synthetic biology, Biological informatics, and Artificial intelligence technologies.

抗体是适应性免疫系统的必需蛋白质，它们以极高的特异性和亲和力与称为抗原的靶蛋白结合。抗体是最重要的药物类别之一，有超过100种抗体治疗药物获得批准。然而，大多数治疗性候选抗体在获得监管机构批准之前就失败了，最终取得进展的抗体的开发成本超过20亿美元，通常需要10年左右的时间才能推向市场。开发这些疗法的耗时和成本密集型过程将受益于用于预测抗体特性的计算和机器学习驱动的方法。这为成功开发下一代生物制剂提供了巨大的希望。该项目将专注于机器学习技术，以选择具有最佳生物物理特性的抗体。该项目将与夏洛特迪恩教授的牛津蛋白质信息学小组将机器学习应用于抗体开发领域的专家一起进行，并与融合抗体合作。Fusion Antibodies是抗体领域的专家已有20多年。除了利用这一专业知识，融合抗体将提供大量有价值的数据，将抗体序列与表达和各种生物物理特性联系起来。这些数据将用于设计和创建机器学习协议，并将有利于该项目，因为这些类型的数据集很少向公众提供。此外，该公司可以进行算法的实验验证。该项目将首先关注生物物理特性、免疫原性和表达。动物模型通常用于获得可能导致人类免疫应答的治疗性抗体。人源化是使抗体更像人的过程，同时保持抗体表达水平及其以高亲和力结合其靶表位的能力。为了使抗体人源化，将动物来源的抗体的互补决定区（参与结合靶标的抗体可变结构域）移植到人框架中。Fusion Antibodies已经完成了这项任务，并针对一组靶标应用了结构和专业知识驱动的回复突变。为了基于该数据集生成该感兴趣的抗体特性的预测算法，应当评估数据集的值。这应该提供对数量，多样性和概括预测所需的数据类型的见解。该项目将通过指导抗体治疗开发具有更好生物物理特性的抗体，有助于在更短的时间内以更低的成本实现疗效最大化。机器学习和人工智能技术将用于创建生物物理特性的预测算法。该项目是跨学科的，涉及免疫信息学，机器学习，（基于结构的）抗体设计以及实验验证。因此，该项目福尔斯属于EPSRC研究领域：化学生物学和生物化学、合成生物学、生物信息学和人工智能技术。