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Entwicklung von Methoden des Maschinellen Lernens zur funktionellen Charakterisierung von Peroxisomen

开发用于过氧化物酶体功能表征的机器学习方法

基本信息

批准号：
189920290
负责人：
Dr. Thomas Lingner
金额：
--
依托单位：
Transkriptomanalyselabor
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2011
资助国家：
德国
起止时间：
2010-12-31 至 2013-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/189920290?language=en
关键词：
Entwicklung von Methoden des Maschinellen

项目摘要

Sequence-based prediction of the precise function of particular cell compartments such as peroxisomes is of growing importance, e.g. to elucidate the response of crop plants to environmental stress or to identify drug targets in pathogens by pathway analysis. The aim of the proposed project is to develop machine learning algorithms for functional characterization of whole cell organelles on the example of peroxisomes in different organisms. For this purpose, approaches to peroxisomal protein prediction and prediction of functional properties and metabolic pathways based on protein domains will be combined. Firstly, known peroxisomal proteins from species of different kingdoms will be collected and used to substantially improve the accuracy of an existing machine learning approach to the prediction of peroxisome-targeted proteins by integration of different sequence features. Secondly, an existing method for microbial phenotype prediction based on the protein domain content of an organism shall be adapted to the problem of deriving functional properties of a peroxisome based on its protein inventory. Furthermore, existing methods for prediction of metabolic pathways from protein sequence sets will be extended and applied to identify relevant peroxisome-specific pathways. Finally, the approach is planned to be extended to metagenomics data such that functional properties of peroxisomes in fungal communities can be described quantitatively.

基于序列预测特定细胞区室（如过氧化物酶体）的精确功能越来越重要，例如阐明作物植物对环境胁迫的反应或通过途径分析确定病原体中的药物靶点。拟议项目的目的是开发机器学习算法，以不同生物体中的过氧化物酶体为例，对整个细胞器进行功能表征。为此，将结合过氧化物酶体蛋白质预测方法和基于蛋白质结构域的功能特性和代谢途径预测方法。首先，将收集来自不同物种的已知过氧化物酶体蛋白，并将其用于通过整合不同序列特征来大幅提高现有机器学习方法预测过氧化物酶体靶向蛋白的准确性。其次，现有的基于生物体蛋白质结构域含量的微生物表型预测方法应适应基于过氧化物酶体蛋白质库存衍生其功能特性的问题。此外，从蛋白质序列集预测代谢途径的现有方法将得到扩展，并应用于识别相关的过氧化物酶体特异性途径。最后，计划将该方法扩展到宏基因组学数据，以便可以定量描述真菌群落中过氧化物酶体的功能特性。