Neural networks applied to antibody structure determination

神经网络应用于抗体结构测定

• 批准号: 2269640
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2269640
• 关键词: Neural; networks; applied; antibody; structure

Antibodies are important proteins of the immune system. They recognise potentially harmful molecules, binding to them and initiating their removal from the body. Their ability to bind with high affinity and specificity to almost any antigen means they can be used as therapeutics - in fact they are the most successful class of biologics, with 93 approved for clinical use to date.Since it is the three-dimensional structure of the antibody that determines its binding properties, knowledge of this structure is very useful. However, experimental structure determination is low-throughput and therefore cannot be used routinely during therapeutic development. Computational modelling tools have hence become increasingly important, allowing researchers to predict large numbers of antibody structures which can then be used to infer and improve binding properties.This project will build on previous research into loop modelling carried out by the Oxford Protein Informatics Group (OPIG) and will explore the use of machine learning methods in the context of protein structure prediction. It will explore 3 potential areas of research involving computational antibody structure prediction. The first area involves using deep residual neural networks to assist in predicting the structure of challenging antibody segments. In particular the structure and the dynamics of CDRH3 loops.Other potential areas of research would involve the incorporation of multiple sequence information into methods for CDRH3 prediction, examination of the interplay of the CDR loops and improving the prediciton of sidechain conformations.This project falls within the EPSRC Synthetic Biology research area, but also contributes to the Artificial Intelligence Technologies and Synthetic Organic Chemistry. . It is a collaboration with F.Hoffmann-La Roche AG.

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。它们与几乎所有抗原都有高亲和力和特异性结合的能力，这意味着它们可以用作治疗药物--事实上，它们是最成功的一类生物制剂，迄今为止有93种被批准用于临床。由于抗体的三维结构决定了其结合特性，因此了解这种结构非常有用。然而，实验结构测定是低通量的，因此不能在治疗开发期间常规使用。因此，计算建模工具变得越来越重要，使研究人员能够预测大量的抗体结构，然后可以用来推断和改善结合特性。该项目将建立在牛津蛋白质信息学小组（OPIG）之前进行的环建模研究的基础上，并将探索机器学习方法在蛋白质结构预测方面的应用。它将探索涉及计算抗体结构预测的3个潜在研究领域。第一个领域涉及使用深度残差神经网络来帮助预测挑战性抗体片段的结构。特别是CDRH 3环的结构和动力学。其他潜在的研究领域包括将多个序列信息纳入CDRH 3预测方法，检查CDR环的相互作用和改进侧链构象的预测。该项目福尔斯EPSRC合成生物学研究领域，但也有助于人工智能技术和合成有机化学。.它是与F.Hoffmann-La Roche AG合作的。