TOOLS-PGR: Computational Infrastructure to Enable High-throughput, High-quality Annotations of Compartmentalized Metabolic Networks for Plant Genomes

TOOLS-PGR：计算基础设施可实现植物基因组分区代谢网络的高通量、高质量注释

• 批准号: 1546838
• 负责人: Seung Rhee
• 金额: $ 219.33万
• 依托单位: Carnegie Institution of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546838&HistoricalAwards=false
• 关键词: TOOLS; PGR; Computational; Infrastructure; Enable

It has been estimated that agricultural productivity needs to be increased to meet the demands imposed by population growth and climate change. Changing the metabolism of crop species is one way to improve productivity. Thus, increasing our knowledge of plant metabolism can significantly accelerate crop improvement efforts. New DNA sequencing technologies have produced an enormous amount of data. However, it has been difficult to obtain useful metabolic information from those DNA sequences. The plant research community needs efficient tools that can extract information related to metabolism from those DNA sequences. This project will produce the tools and datasets that will be used to systematically characterize the components of metabolism: enzymes, transporters, and pathways. These tools will make it easy to compare the metabolic genetic potential of two or more species, and enable the identification of targets for crop improvement. This project will also offer training opportunities in biochemistry and computer sciences to postdoctoral associates and students. In addition, workshops will be offered at professional meetings to train members of the plant research community on the use of the tools developed by the project. Finally, the tools developed by this project will be made available to the scientific community through a web portal.Accurate and rapid annotation of metabolic enzymes and transporters from sequenced genomes and their metabolic network reconstructions are essential resources for interpreting the results of 'omics' data systematically and enabling the generation of new hypotheses. This proposal aims to meet these needs by developing a computational pipeline to enable rapid and accurate prediction of genome-scale metabolic complements of any sequenced plant based on the large pool of experimentally characterized information. First, the team will improve the accuracy of enzyme function prediction by adding new classifiers and features to a redesigned machine-learning framework. Additions of new classifiers such as phylogenomics-based function prediction and new features such as conserved protein domain architecture and conserved residues would reduce false positive predictions of proteins that share high sequence similarity with known enzymes but catalyze distinct functions. The team will also develop a new learning­ based algorithm to predict subcellular locations of enzymes and reactions for any plant species. The algorithm will combine the localization likelihoods of enzymes derived from the experimentally determined localization information of their orthologs and the localization information of the neighboring reactions in the metabolic network to propagate the localization likelihoods among all the reactions in the network. Another new algorithm will be developed to predict transporters and the substrates of transporters. All data generated from this project will be integrated into the PMN databases. In addition, a pipeline will be packaged to enable users to submit their genome sequences online and obtain the prediction results through a web server. Finally, innovative, integrated views of metabolic pathways with gene co-expression, transporters and subcellular compartments will be developed.

据估计，需要提高农业生产力，以满足人口增长和气候变化带来的需求。改变作物品种的新陈代谢是提高产量的一种方法。因此，增加我们对植物代谢的了解可以显着加快作物改良的努力。新的DNA测序技术产生了大量的数据。然而，很难从这些DNA序列中获得有用的代谢信息。植物研究界需要有效的工具，可以从这些DNA序列中提取与代谢相关的信息。该项目将产生工具和数据集，用于系统地表征代谢的组成部分：酶，转运蛋白和途径。这些工具将使比较两个或多个物种的代谢遗传潜力变得容易，并能够确定作物改良的目标。该项目还将为博士后助理和学生提供生物化学和计算机科学方面的培训机会。此外，还将在专业会议上举办讲习班，培训植物研究界成员使用该项目开发的工具。最后，该项目开发的工具将通过一个门户网站提供给科学界。从已测序的基因组及其代谢网络重建中准确快速地注释代谢酶和转运蛋白，是系统地解释“组学”数据结果并使新假设得以产生的重要资源。该提案旨在通过开发计算管道来满足这些需求，以基于大量的实验表征信息来快速准确地预测任何测序植物的基因组规模的代谢互补物。首先，该团队将通过向重新设计的机器学习框架中添加新的分类器和功能来提高酶功能预测的准确性。添加新的分类器，如基于基因组学的功能预测和新的功能，如保守的蛋白质结构域结构和保守的残基，将减少与已知酶具有高序列相似性但催化不同功能的蛋白质的假阳性预测。该团队还将开发一种新的基于学习的算法，以预测任何植物物种的酶和反应的亚细胞位置。该算法将联合收割机从实验确定的酶的直系同源物的定位信息和代谢网络中的相邻反应的定位信息导出的酶的定位可能性，以在网络中的所有反应之间传播定位可能性。另一种新的算法将被开发来预测转运蛋白和转运蛋白的底物。该项目产生的所有数据将被整合到PMN数据库中。此外，还将打包一个管道，使用户能够在线提交他们的基因组序列，并通过网络服务器获得预测结果。最后，创新的，综合的观点与基因共表达，转运蛋白和亚细胞室的代谢途径将被开发。