Development of quantitative analysis methods to accelerate the optimisation of fragment hits to lead compounds using NMR spectroscopy

开发定量分析方法，利用核磁共振波谱加速先导化合物片段命中的优化

• 批准号: 2726305
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2726305
• 关键词: Development; quantitative; analysis; methods; accelerate

Fragment-based drug discovery (FBDD) is a widely used approach to generate starting points for drug discovery and tools for chemical biology, but a significant bottleneck is time-consuming and labour intensive purification of compounds developed from an initial hit, before they can be evaluated for binding. To eliminate this bottleneck, off-rate screening (ORS) has been developed by Vernalis to screen unpurified crude reaction mixtures (CRMs), using reductions in dissociation rate observed by SPR as a signal of a high affinity binder within a CRM. However, this approach is limited to late stages of FBDD, where ligand dissociation rates are already relatively slow.This project aims to develop new experimental and analytical approaches to accelerate early stages of FBDD pipelines, by using advanced NMR spectroscopy and lineshape analysis methods to extend ORS to faster timescales associated with weak-binding fragments.Two primary objectives are envisaged at this stage, to be refined as necessary according to the results that are generated:1. Investigate and assess the capacity for 1D and 2D protein-observed (PO) and 1D ligand-observed (LO) NMR experiments, employing 1H or 19F detection, to screen CRMs for slow-dissociating ligands.2. Develop and validate open-source software analysis tools, integrated with TITAN lineshape analysis software and/or Vernalis KNIME workflows, that implement the analyses developed in easy-to-use and accessible packages.This project is a clear fit to the MRC remit: training a skilled researcher to develop new technologies that will accelerate the discovery of new medicines, addressing a problem of particular importance to our industrial partner and so improving their economic competitiveness through the know-how that will be developed, and ultimately contributing to improvements in the quality of life by improving the development of new drugs. The project is aligned with the Fundamental Mechanisms of Disease DTP theme, applying structural biology and biophysical methods to better understand interactions of small molecules with disease-relevant macromolecules, streamlining and accelerating the process of drug discovery. The student will be trained in a variety of quantitative skills, particularly mathematics, computation and data analytics, while the proposed project is highly interdisciplinary: NMR method development demands an understanding of quantum statistical mechanics, which will be coupled with classical chemical kinetics and thermodynamics, computational analysis and software engineering, and molecular biology and biophysical skills.

基于碎片的药物发现（FBDD）是一种广泛使用的方法，可以生成用于化学生物学的药物发现和工具的起点，但是重要的瓶颈是耗时的，并且可以对初始命中产生的化合物进行劳动密集的纯化，然后才能对其进行评估以进行结合。为了消除这种瓶颈，Vernalis开发了率筛选（ORS），以筛选未纯净的粗制反应混合物（CRMS），使用SPR观察到的分离率的降低，作为CRM中高亲和力粘合剂的信号。但是，这种方法仅限于FBDD的晚期，在该阶段中，配体解离率已经相对较慢。该项目旨在开发新的实验和分析方法，以加速FBDD管道的早期阶段，通过使用先进的NMR光谱镜检查和线形分析方法来扩展该阶段的范围，以使这些阶段与弱点相关。根据生成的结果：1。研究并评估使用1H或19F检测的1D和2D蛋白观察（PO）和1D配体（LO）NMR实验的能力，以筛选CRMS以缓慢分解配体。2。 Develop and validate open-source software analysis tools, integrated with TITAN lineshape analysis software and/or Vernalis KNIME workflows, that implement the analyses developed in easy-to-use and accessible packages.This project is a clear fit to the MRC remit: training a skilled researcher to develop new technologies that will accelerate the discovery of new medicines, addressing a problem of particular importance to our industrial partner and so improving their economic competitiveness through the know-how这将是发展，并最终通过改善新药的开发来改善生活质量。该项目与疾病DTP主题的基本机制保持一致，采用结构生物学和生物物理方法，以更好地了解小分子与疾病相关的大分子的相互作用，从而精简和加速药物发现过程。该学生将接受各种定量技能的培训，尤其是数学，计算和数据分析，而拟议的项目则是高度跨学科的：NMR方法开发需要对量子统计力学的了解，该机制将与经典的化学动力学和热力学，计算分析和软件工程以及软件工程以及杂草杂质的生物学和生物学生物学和生物学能力和生物学能力和生物学能力和生物学能力和生物学能力和生物学能力和生物学能力。