Computational Annotation of Orphan Metabolic Activities

孤儿代谢活动的计算注释

基本信息

批准号：
8697980
负责人：
Dennis Vitkup
金额：
$ 51.11万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-05-15 至 2018-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8697980
关键词：
Active Sites Anti-Bacterial Agents Antibiotics Area Bacteria Bacterial Genome Biochemical Pathway Biology Collaborations Communities Computing Methodologies Data Development Dreams Enzymes Funding Genome Genomics Growth Human Joints Lead Left Letters Life Malaria Measures Medical Research Metabolic Metabolism Metagenomics Methodology Methods Microbe Military Personnel Modality Modeling Mycobacterium tuberculosis Nutrient Online Systems Orphan Parasites Pathway interactions Phenotype Positioning Attribute Property Proteins Publications Research Research Personnel Role Route Sampling Sequence Homology Services Source Structural Protein Study models System Technology Therapeutic Work abstracting base improved innovation interest killings metabolomics microbial pathogen pathogenic bacteria protein structure public health relevance reconstruction resistant strain simulation success

项目摘要

DESCRIPTION: An avalanche of sequencing and genomic data has a potential to revolutionize our understanding of microbial biology and transform medical research. Accurate reconstructions of bacterial metabolism often provide the most direct route toward understanding the biology of sequenced species, their growth and environmental properties, and possible interactions with other species in metagenomic communities. Reconstructed metabolic networks can also guide the development of new antibacterial therapeutics. In this application we propose to use the GLOBUS framework to build an integrated, accurate, and fully probabilistic system for the annotation of bacterial metabolism; we will integrate into the framework key data modalities that, according to our preliminary results, will significantly improve the method's coverage and accuracy. Specifically, we will a.) integrate protein structural information into GLOBUS, using analyses of enzyme active sites; b.) extend the GLOBUS framework to accommodate metabolomics data by joint sampling of metabolomics and protein annotations; and c.) integrate into the annotation framework phenotypic information, including growth on multiple nutrient sources measured by the widely used BiOLOG platform for high-throughput bacterial phenotyping. We will implement and make the developed methodology publically available through a transparent web-based portal. This will make it possible for other researchers to use the GLOBUS methodology to annotate any sequenced bacterial species of interest. The portal will be transparent, enabling users to identify the sources of the predictions We will also obtain relevant phenotypic information from our experimental collaborators and use GLOBUS to generate accurate probabilistic annotations for all major bacterial species (~50 bacteria) that are pathogenic to humans.

描述：大量的测序和基因组数据有可能彻底改变我们对微生物生物学的理解并改变医学研究。细菌代谢的准确重建通常为了解已测序物种的生物学、其生长和环境特性以及与宏基因组群落中其他物种可能的相互作用提供了最直接的途径。重建的代谢网络还可以指导新抗菌疗法的开发。在这个应用中，我们建议使用GLOBUS框架构建一个集成的、准确的、完全概率的系统来注释细菌代谢；我们将把关键数据模式整合到框架中，根据我们的初步结果，这将显着提高该方法的覆盖范围和准确性。具体来说，我们将 a.) 通过酶活性位点分析将蛋白质结构信息整合到 GLOBUS 中； b.) 通过代谢组学和蛋白质注释的联合采样扩展 GLOBUS 框架以适应代谢组学数据； c.) 将表型信息整合到注释框架中，包括通过广泛使用的用于高通量细菌表型分析的 BiOLOG 平台测量的多种营养源的生长情况。我们将通过透明的网络门户实施并公开开发的方法。这将使其他研究人员能够使用 GLOBUS 方法来注释任何感兴趣的测序细菌物种。该门户将是透明的，使用户能够识别预测的来源。我们还将从实验合作者那里获得相关表型信息，并使用 GLOBUS 为对人类致病的所有主要细菌种类（约 50 种细菌）生成准确的概率注释。