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

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
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

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中文摘要
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英文摘要
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.
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复杂图像处理中的自由非连续问题及其水平集方法研究
  • 批准号:
    60872130
  • 项目类别:
    面上项目
  • 资助金额:
    28.0万元
  • 批准年份:
    2008
  • 负责人:
    刘国才
  • 依托单位:
Computational Methods for Analyzing Toponome Data