Computational methods for rapid structural modelling of antigen-antibody interactions to improve identification of antigen-specific antibodies from Ig

抗原抗体相互作用快速结构建模的计算方法，以改进 Ig 中抗原特异性抗体的识别

• 批准号: 2117164
• 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: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2117164
• 关键词: Computational; methods; rapid; structural; modelling

The exquisite antigen recognition specificity of antibodies has made them useful as diagnostics, research agents and the most successful class of biopharmaceuticals. The ability to discover better antibody-based therapeutics needs knowledge of the 3D shape of individual antibodies within the context of the entire antibody repertoire. Next-generation sequencing methodologies (Ig-seq) can rapidly yield millions of antibody gene sequences. However, so far, the inability to routinely overlay antibody structure on large Ig-seq datasets has limited their potential for antibody drug discovery. In this project we will use computational methodologies to bridge between the two fields by allowing structural annotation of Ig-seq experiments which will pave the way for more advanced antibody-based therapeutics.The project falls within the remit of the MRC delivery plan in both its connection to priority challenges (e.g. applications in serology and thus both outbreak control and in vaccine development, the latter of which will be realised through working with Oxford Vaccine Group, as well as accelerating discovery of antibody-based therapeutics) and its alignment with MRC skill priorities. The relevant MRC skill priorities are quantitative and interdisciplinary skills. The key quantitative skills of mathematics, statistics and computation are central to the project in the form of protein structure prediction. Structure prediction itself is interdisciplinary, drawing on chemistry, statistics and insight from the life sciences. Furthermore, the aim of the project is to connect these structural models, produced computationally, with the kind of large-scale and rapidly accruing sequence data that is at the focus of the MRC's research spotlight on informatics. Informatics and computation have been identified as key steps in the MRC's strategy for transforming health research, and thus the project, in linking high throughput sequence data to structural biology via computation, fits neatly into the MRC's initiative for biomedical informatics. The project is co-supervised and funded by Kymab.

抗体的抗原识别特异性使其成为诊断、研究试剂和最成功的一类生物药物。发现更好的基于抗体的疗法的能力需要在整个抗体库的背景下了解单个抗体的3D形状。下一代测序方法（Ig-seq）可以快速产生数百万个抗体基因序列。然而，到目前为止，无法在大型Ig-seq数据集上常规地覆盖抗体结构限制了它们用于抗体药物发现的潜力。在这个项目中，我们将使用计算方法学在两个领域之间架起桥梁，通过允许Ig-seq实验的结构注释，这将为更先进的基于抗体的治疗铺平道路。该项目福尔斯属于MRC交付计划的职权范围，无论是与优先挑战的连接，还是与免疫缺陷病毒的连接。（例如，在血清学中的应用，从而在爆发控制和疫苗开发中的应用，后者将通过与牛津疫苗集团合作实现，以及加速发现基于抗体的疗法）及其与MRC技能优先级的一致性。相关的MRC技能优先级是定量和跨学科的技能。数学，统计学和计算的关键定量技能是蛋白质结构预测形式的项目的核心。结构预测本身是跨学科的，利用化学，统计学和生命科学的洞察力。此外，该项目的目的是将这些计算产生的结构模型与MRC信息学研究焦点的大规模和快速积累的序列数据相连接。信息学和计算已被确定为MRC转变健康研究战略的关键步骤，因此该项目通过计算将高通量序列数据与结构生物学联系起来，巧妙地融入了MRC的生物医学信息学倡议。该项目由Kymab共同监督和资助。