Machine Learning to improve the outputs of an antibody synthetic library

抗体是适应性免疫系统的基本蛋白质，它与被称为抗原的目标蛋白质结合，具有很强的特异性和亲和力。抗体是最重要的药物类别之一，已有100多种抗体疗法获得批准。然而，大多数候选治疗性抗体都没有获得监管部门的批准，最终取得进展的抗体的研发成本超过20亿美元，通常需要大约10年的时间才能推向市场。开发这些疗法的这一耗时和成本密集的过程将受益于预测抗体特性的计算和机器学习驱动的方法。这为下一代生物制剂的成功开发提供了巨大的希望。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。融合抗体公司在抗体领域已经有20多年的历史了。除了利用这些专业知识，融合抗体还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，并将对该项目有利，因为这些类型的数据集很少向公众开放。此外，该公司还可以对算法进行实验验证。本项目将初步集中在生物物理性质、免疫原性和表达方面。动物模型经常被用来获得治疗性抗体，可能会导致人类的免疫反应。人源化是使抗体更接近人类的过程，同时保持抗体的表达水平及其与目标表位高亲和力结合的能力。为了使抗体人性化，将动物来源抗体的互补决定区，即与靶标结合的抗体可变区嫁接到人的骨架中。融合抗体已经完成了这项任务，并将结构和专业知识驱动的突变应用于一组靶标。为了基于该数据集生成该感兴趣的抗体属性的预测算法，应该评估该数据集的值。这应该提供对数量、多样性和概括预测所需的数据类型的洞察。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，在较短的时间内以较低的成本最大限度地提高疗效。机器学习和人工智能技术将被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，本项目属于EPSRC的研究领域：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。

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.