Global Analysis of Pharmacophoric Space

原理众所周知，许多药物分子与多个蛋白质靶点结合，并对其起作用。最近的研究表明，这种混杂行为是有限数量的蛋白质口袋形状的结果。此外，在这些可连接的口袋中，有一些特殊的高能量局部区域(所谓的热点)，它们驱动小分子与蛋白质的结合。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的形状和化学性质能够被识别出来，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。这些药效团也可能被用于设计或选择一组用于此目的的化合物。基于片段的筛查的高成功率是对这种方法的验证。我们的目标是发现如何通过更大的化合物实现这一成功，这些化合物具有足够的亲和力以产生功能效应。项目概述本项目将与克里斯·拉杜的热点预测工作重叠，并推进他的工作。拉杜是一名博士生，由BBSRC-UCB赞助，CCDC主办。计划是克里斯将开发一种方法，从他的热点预测中自动获得药效团。该项目将运行在PDB的所有口袋使用这种方法，并分析输出。最后，这些药效团将被用来设计和/或选择适合尽可能多的这些药效团的化合物。当然，匹配所有药效团所需的化合物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。

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.