Using Heterogeneous Information Sources for Understanding the Mode of Action of Compounds
Using Heterogeneous Information Sources for Understanding the Mode of Action of Compounds
批准号:
1944644
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:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --
中文摘要
化合物在生物系统中的作用方式可以在不同的水平上描述,例如在配体-蛋白质相互作用水平上(例如与靶标结合,抑制酶等),以及在生物系统中的作用水平上(例如使用基因组或蛋白质组学读数,细胞形态学读数等)。所有这些观点都是同样有效的,因此整合不同类型的信息,得出一个关于生物系统中化合物作用模式的综合观点是非常可取的。这就是当前项目的目标所在。我们将编制一个复合数据集,将分子结构与不同水平的生物活性和生物效应联系起来,例如(但不限于)上述内容。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点的相互作用将导致特定的下游(通常是可观察到的)效应。在这些联系中,下一步是确定模式,即能够概括出哪种类型的相互作用,与哪种蛋白质(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生物系统(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表我们的发现,并与公司赞助商阿斯利康(AstraZeneca)在特别感兴趣的领域进行前瞻性验证。
英文摘要
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.
期刊论文(5)
专著(0)
科研奖励(0)
会议论文
Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions.
多任务生物活性预测的化学结构和细胞形态信息比较。
DOI:
10.1021/acs.jcim.0c00864
发表时间:
2021
期刊:
Journal of chemical information and modeling
影响因子:
5.6
作者:
[Trapotsi MA]
通讯作者:
Trapotsi MA
Multitask Bioactivity Predictions Using Structural Chemical and Cell Morphology Information
使用结构化学和细胞形态信息进行多任务生物活性预测
DOI:
10.26434/chemrxiv.12571241.v1
发表时间:
2020
期刊:
影响因子:
--
作者:
[Trapotsi M]
通讯作者:
Trapotsi M
Using Heterogeneous Information Sources for Understanding and Predicting Biological Effects of Compounds
使用异质信息源来理解和预测化合物的生物效应
DOI:
10.17863/cam.83454
发表时间:
2021
期刊:
影响因子:
--
作者:
[Trapotsi M]
通讯作者:
Trapotsi M
海外基金