Machine learning driven antibody-antigen complex modelling and its applications to antibody therapeutics design

抗体是最重要的药物类别之一，有超过100种抗体治疗药物被批准用于治疗各种疾病，还有更多的药物正在积极开发中。抗体疗法的开发是一个时间和成本密集型的过程，使其成为使用计算和ML驱动的筛选和设计技术加速的有前途的目标。与抗原靶标复合的抗体结构的预测（也称为抗体-抗原对接）仍然是一个具有挑战性的问题，近年来几乎没有重大进展。与蛋白质结构预测的许多其他领域一样，抗体结构的孤立建模自2020年以来已经通过现代机器学习模型的应用进行了彻底改革，在许多情况下受到AlphaFold 2成功的启发。AlphaFold多聚体已经显示出用于一般蛋白质-蛋白质对接的有希望的结果，但是由于抗体结合区域的高度多样性，在抗体-抗原对接上表现不佳。拟议的项目旨在开发一个框架，用于预测抗体-抗原相互作用结构，该框架利用扩散生成模型和等变图神经网络架构的最新进展，这些模型和架构已被证明能够在蛋白质和特别是抗体结构预测中进行阶跃变化。近年来，扩散模型大大提高了人工智能生成的图像质量，并已成功应用于包括蛋白质工程在内的生物任务。目前小分子蛋白质对接的最新技术是DiffDock，一种扩散模型。DiffDock-PP是一个适应性版本，它执行刚体蛋白质-蛋白质对接。在我的轮换项目期间，我研究了DiffDock-PP在增加抗体结构数据库时进行抗体-抗原对接的能力。在我的博士期间，我将开发一种新的基于扩散的蛋白质-蛋白质对接模型，具体目标是对接抗体。这将包括空间表示的变化，更有效的记忆使用，注意力模型，以及引入类似物理学的术语来指导反向扩散过程。该模型将在实验得出的对接3D结构上进行测试，如果成功，将在计算预测的结构上进行测试。为计算预测的3D结构创建可靠的抗体-抗原对接模型将使研究人员能够纯粹从测序数据中了解抗体如何相互作用。这将大大提高治疗性抗体工程的速度，并提高我们对未来大流行期间免疫逃逸的理解。该项目属于EPSRC的“人工智能技术”、“生物信息学研究”和“计算和理论化学”研究领域的福尔斯。它将在牛津大学统计系的夏洛特迪恩教授和Exscientia的康斯坦丁施耐德博士的监督下进行。

Antibodies are one of the most important classes of pharmaceuticals, with over 100 antibody therapeutics approved against a wide variety of diseases and many more in active development. The development of antibody therapeutics is a time- and cost-intensive process, making it a promising target for acceleration using computational and ML-driven screening and design techniques. The prediction of the structure of antibodies in complex with antigen targets (also referred to as antibody-antigen docking) remains a challenging problem, with little significant progress published in recent years. The modelling of antibody structures in isolation, like many other areas of protein structure prediction, has been overhauled since 2020 by the application of modern machine learning models, inspired in many cases by the success of AlphaFold2. AlphaFold-multimer has shown promising results for general protein-protein docking but performs poorly on antibody-antigen docking due to the high diversity of the antibody binding regions. The proposed project would aim to develop a framework for the prediction of antibody-antigen interaction structures drawing on the recent advances in diffusion generative models and equivariant graph neural network architectures which have proven capable of step changes in protein and specifically antibody structure prediction. In recent years, diffusion models have drastically improved AI-generated image quality and have been successfully applied to biological tasks including protein engineering. The current state-of-the art for small molecule protein docking is DiffDock, a diffusion model. DiffDock-PP is an adapted version which performs rigid body protein-protein docking. During my rotation project I investigated the ability of DiffDock-PP to perform antibody-antigen docking when trained on an increased pool of antibody structural data. During my DPhil I will develop a new diffusion-based model for protein-protein docking, with the specific aim of docking antibodies. This will include changes to the spatial representations, more efficient memory usage, an attention model, and incorporation of physics-like terms to guide the reverse diffusion process. The model will be tested on experimentally derived docked 3D structures and, if successful, on computationally predicted structures. The creation of a reliable antibody-antigen docking model for computationally predicted 3D structures would allow researchers to understand how antibodies interact purely from sequencing data. This would greatly improve the pace of therapeutic antibody engineering as well as improve our understanding of immune escape during future pandemics. This project falls within the EPSRC research areas of "artificial intelligence technologies", "biological informatics research", and "computational and theoretical chemistry". It will be performed under the supervision of Professor Charlotte Deane from the Statistics department at the University of Oxford as well as Dr Constantin Schneider from Exscientia.