Investigating B-cell repertoire data using deep learning approaches to aid in the development of antibody therapeutics

抗体是免疫系统的重要蛋白质。它们识别潜在的有害分子，与它们结合并开始将它们从体内清除。迄今为止，大约有100种抗体被批准用于临床，它们已成为一类重要且不断增长的药物。然而，治疗性抗体开发因许多要求而复杂化，包括化学稳定性、溶解性、低粘度、生物利用度、长血清半衰期、非免疫原性和对片段化、聚集、翻译后修饰和蛋白水解切割的抗性，同时还保留其期望的功能（结合亲和力、特异性和功能活性）。虽然用于治疗性抗体发现的方法不断发展，但这仍然是一个昂贵和繁琐的过程。因此，高度寻求通过计算机模拟方法引入的见解，沿着机器学习算法，其可以从先前的实验中提取和利用信息来预测新抗体的性质和功能。最近，UniRep等方法已经显示出通过在蛋白质数据上应用自然语言处理（NLP）启发的方法（特别是迁移学习）来改善蛋白质预测的可能性。迁移学习是指将来自一个领域的信息用于另一个相关领域，当后一个领域只有小数据集时，这可能特别强大，这在抗体数据中很常见。因此，开发基于最先进的NLP技术构建的抗体的新ML工具可以对治疗性抗体发现产生很大影响。该项目的目的是探索抗体数据并开发新的机器学习工具，以改善抗体特性和功能的预测。这包括：调查观察抗体空间数据库中可用的大量序列数据。开发新的ML技术，并适应新的NLP技术来处理生物数据。这个博士项目是牛津大学牛津蛋白质信息学组（OPIG）的夏洛特迪恩教授和Iain H. Moal在葛兰素史克（GSK），伦敦。该项目与EPSRC的几个战略和研究领域保持一致。它主要属于EPSRC生物信息学研究领域的福尔斯，用于开发新的计算技术来建模和分析生物数据（用于抗体预测的机器学习工具）。此外，该项目也属于EPSRC分析科学和人工智能技术研究领域的福尔斯，因为我们使用新型ML技术从大型数据集中提取信息，用于分析和预测抗体的特性。

Antibodies are important proteins of the immune system. They recognize potentially harmful molecules, binding to them and initiating their removal from the body. With approximately 100 antibodies approved for clinical use to date, they have become an important and growing class of pharmaceuticals. However, therapeutic antibody development is complicated by numerous requirements, including chemical stability, solubility, low viscosity, bioavailability, long serum half-life, non-immunogenicity, and resistance to fragmentation, aggregation, post-translational modification and proteolytic cleavage, while also retaining their desired functions (binding affinity, specificity and functional activity).Whilst methodologies for therapeutic antibody discovery are constantly evolving, it remains an expensive and cumbersome process. Insights introduced by in silico approaches, along with machine learning algorithms, which can extract and utilize information from previous experiments to predict the properties and functions of new antibodies, are therefore highly sought. Recently, methods such as UniRep have shown the possibilities of improving protein predictions by applying Natural Language Processing (NLP) inspired methods, notably transfer learning, on protein data. Transfer learning is when information from one domain is used in another related domain, which can be particularly powerful when only small data sets are available for the latter domain, a common occurrence with antibody data. Developing new ML tools for antibodies built on state-of-the-art NLP techniques can therefore have a large impact on the therapeutic antibody discovery.The aim of this project is to explore antibody data and develop novel machine learning tools for improving the predictions of antibody properties and functions. This includes; Investigating the large amount of sequence data available in the Observed Antibody Space database. Develop novel ML techniques, and adaptation of novel NLP techniques to work on biological data. Explore the use of these new techniques in antibody property and function prediction.This DPhil project is a collaboration between Prof. Charlotte Deane at the Oxford Protein Informatics Group (OPIG), University of Oxford and Dr. Iain H. Moal at GlaxoSmithKline (GSK), London. This project aligns with several of EPSRC's strategies and research areas. It mainly falls within the EPSRC Biological Informatics research area for its development of novel computational techniques to model and analyse biological data (machine learning tools for antibody predictions). Additionally, the project also falls within the EPSRC Analytical Science and Artificial Intelligence Technologies research areas, for our use of novel ML techniques to extract information from large datasets for analyzing and predicting properties of antibodies.