Global Analysis of Pharmacophoric Space

药效空间的全局分析

• 批准号: 1849980
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1849980
• 关键词: Global; Analysis; Pharmacophoric; Space

Rationale It is well known that many drug molecules bind to, and are active against, multiple protein targets. Recent studies suggest that this promiscuous behaviour is a consequence of the finite number of protein pocket shapes. Furthermore, in these ligandable pockets there are special highly energetic local regions (so called hot spots) that drive the small molecule-protein binding. These regions are thought to be conserved across protein families and conformational ensembles. If the shapes and chemical properties of these hot regions in proteins can be identified, and encoded as pharmacophores, then we will be in position to estimate the number of small molecule ligands needed to cover them. These pharmacophores can also potentially be used to design or select a set of compounds for this purpose. The high success rate of fragment-based screening is a validation of this approach. We aim to discover how to achieve this success with larger compounds, ones which bind with sufficient affinity to have a functional effect. Project Summary This project will overlap with and take forward the hotspot prediction work of Chris Radoux, a PhD student sponsored by BBSRC-UCB and hosted by the CCDC. The plan is that Chris will develop a method to automatically derive pharmacophores from his hotspot predictions. This project will be to run use this method on all pockets in PDB and analyse the output. Finally these pharmacophores will be used to design and/or select compounds that fit as many of these pharmacophores as possible. Of course, the number of compounds needed to match all pharmacophores will be a function of their complexity. Part of this project will be to calculate what this relationship is.

原理众所周知，许多药物分子与多种蛋白质靶点结合并对其具有活性。最近的研究表明，这种混杂行为是蛋白质口袋形状数量有限的结果。此外，在这些可配体口袋中有特殊的高能量局部区域（所谓的热点），驱动小分子-蛋白质结合。这些区域被认为在蛋白质家族和构象集合中是保守的。如果蛋白质中这些热区域的形状和化学性质可以被识别出来，并被编码为药效团，那么我们就可以估计覆盖它们所需的小分子配体的数量。这些药效团也可以潜在地用于设计或选择一组用于此目的的化合物。基于片段筛选的高成功率是对这种方法的验证。我们的目标是发现如何在更大的化合物上实现这一成功，这些化合物具有足够的亲和力以产生功能效果。项目概述本项目将与BBSRC-UCB资助、CCDC承办的博士生Chris Radoux的热点预测工作进行重叠和推进。克里斯的计划是开发一种方法，从他的热点预测中自动提取药效团。该项目将在PDB中的所有口袋上运行并分析输出。最后，这些药效团将用于设计和/或选择适合尽可能多的这些药效团的化合物。当然，与所有药效团相匹配的化合物的数量将取决于它们的复杂性。这个项目的一部分将是计算这种关系是什么。