Using Heterogeneous Information Sources for Understanding the Mode of Action of Compounds

使用异质信息源了解化合物的作用模式

基本信息

批准号：
1944644
负责人：
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：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1944644
关键词：
Using Heterogeneous Information Sources Understanding

项目摘要

The mode of action of a compound in a biological system can be described on different levels, such as on the ligand-protein interaction level (e.g. binding to a target, inhibiting an enzyme etc.), as well as the effect level in a biological system (e.g. using genomic or proteomic readouts, cellular morphology readouts etc.). All of those viewpoints are equally valid, and hence an integration of different types of information to arrive at an integrated view on the mode of action of compounds in a biological system is highly desirable. This is where the current project aims to make a step forward. We will compile a compound data set which links molecular structure to bioactivites, and biological effects, on different levels, such as (but not limited to) the above. Subsequently, data mining algorithms will be employed that aim to establish links between the different levels of chemical and biological effects, i.e. to understand which interaction with which biological targets will lead to a particular downstream (often observable) effect. Within those links the next step is then to identify patterns, i.e. to be able to generalize which type of interaction, with which protein (or signalling cascade, etc.) will lead to a particular effect, which can then be translated to other, related biological systems (such as signalling networks). This work will be performed on both public and proprietary data, which will enable us to both publish our findings in a suitable manner, as well as to perform prospective validations with the company sponsor, AstraZeneca, in particular areas of interest.

化合物在生物系统中的作用模式可以在不同的水平上描述，例如配体-蛋白质相互作用水平（例如与靶标结合、抑制酶等），以及在生物系统中的作用水平。系统（例如使用基因组或蛋白质组读数、细胞形态读数等）。所有这些观点都同样有效，因此非常需要整合不同类型的信息以得出关于生物系统中化合物的作用模式的综合观点。这就是当前项目旨在向前迈出一步的地方。我们将编译一个复合数据集，在不同层面上将分子结构与生物活性和生物效应联系起来，例如（但不限于）上述。随后，将采用数据挖掘算法，旨在建立不同水平的化学和生物效应之间的联系，即了解与哪些生物靶标的相互作用将导致特定的下游（通常可观察到）效应。在这些链接中，下一步是识别模式，即能够概括哪种类型的相互作用，哪种蛋白质（或信号级联等）将导致特定的效果，然后可以将其转化为其他相关的效果。生物系统（例如信号网络）。这项工作将在公共和专有数据上进行，这将使我们能够以适当的方式发布我们的研究结果，并与公司赞助商阿斯利康在特定感兴趣的领域进行前瞻性验证。