Structure-based functional annotation of microbial genomes

微生物基因组基于结构的功能注释

基本信息

批准号：
10535650
负责人：
Lydia Freddolino
金额：
$ 77.7万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10535650
关键词：
Acute Amino Acid Sequence Attention Back Bacteria Bacterial Genome Bacterial Proteins Behavior Binding Sites Biochemical Biochemistry Biological Biological Markers Biological Models Biology Communities Computing Methodologies Data Databases Development Disease Drug Design Escherichia coli Escherichia coli K12 Escherichia coli Proteins Failure Feedback Follow-Up Studies Future Genes Genetic Genome High-Throughput Nucleotide Sequencing Hospitalization Human Human Resources In Vitro Intervention Knowledge Laboratories Libraries Ligands Methods Microbe Modernization Mycoplasma Network-based Ontology Organism Pathogenesis Performance Pharmacologic Substance Physiological Physiology Protein Structure Databases Proteins Proteome Public Health Research Resolution Role Sequence Homology Set protein Structure Technology Testing Translating United States Urinary tract infection Uropathogenic E. coli Virulence Virulence Factors Virus Work X-Ray Crystallography Yang bacterial genetics base biological systems clinically relevant cofactor computerized tools deep learning experimental study genome-wide host colonization human pathogen improved in vivo innovation insight interest method development microbial genome mouse model neural network next generation novel pathogenic bacteria pathogenic microbe predictive modeling protein folding protein function protein protein interaction protein structure function protein structure prediction public database therapeutic target tool

项目摘要

Abstract One of the most pressing challenges in modern biology is that of translating the massive amounts of information on biological sequences that has been made available by recent advances in sequencing technologies, into corresponding insights into the behavior of biological systems. Determining the functions and physiological roles of proteins remains a major component of this challenge; for many species, especially non-model microbes such as microbial pathogens, the fraction of the proteome consisting of poorly annotated proteins may approach 50%, severely limiting our ability to even identify mechanisms of pathogenesis and potential therapeutic targets. The massive number of poorly annotated proteins of potential biological importance necessitates the ongoing development of efficient and reliable computational approaches for functional annotation of proteins. Over the past few years, we have developed and applied several new workflows for whole-proteome structure prediction and functional annotation of bacterial genomes, with applications to laboratory strain E. coli K12 and to the minimal genome mycoplasma JCVI-syn3.0. Our workflows are distinguished by the integration of structural information (including high-accuracy protein structure prediction) in functional annotations, alongside classical methods such as sequence homology and syntenty, and recent developments such as the inclusion of deep-learning based predictors; we find that collectively, our workflows provide highly accurate functional annotations that are especially useful for ‘difficult’ protein targets without clear annotated homologs. We will now shift our focus to applying our tools to the proteomes of bacterial pathogens, with an initial emphasis on uropathogenic E. coli. Specifically, we will continue to develop our structure/function prediction capabilities to further improve accuracy and increase the richness of information delivered (Aim 1), perform prediction-guided biochemical characterization of likely virulence genes to assess predictive performance and identify potential pharmaceutical targets (Aim 2), obtain experimental structures for proteins that are identified as difficult structural targets which likely represent novel folds or unusual sequences for known folds (Aim 3), and test the physiological importance of likely newly-identified virulence factors in an in vivo mouse model (Aim 4). The experimental data gathered under Aims 2-4 will be continuously integrated with the ongoing methods development under Aim 1 to maximize the performance and utility of the developed tools. The results of this project will include further improvements to widely used and cited tools for rapid structure/function prediction, identification of specific virulence determinants in uropathogenic E. coli and preliminary insights into how they may be targeted for pharmaceutical intervention, and additional structural data of potential virulence factors that will aid in structure-based drug design and improve coverage of existing structural template libraries to guide future protein structure and function prediction.

抽象的现代生物学最紧迫的挑战之一是翻译大量的通过测序的最新进展提供的生物序列信息技术，深入了解生物系统的功能和行为。蛋白质的生理作用仍然是这一挑战的主要组成部分，对于许多物种来说尤其如此。非模型微生物，例如微生物病原体，蛋白质组中由注释不良的部分组成蛋白质可能接近 50%，严重限制了我们识别发病机制和机制的能力潜在的生物治疗靶点的大量注释不明确的蛋白质。重要性需要不断开发高效可靠的计算方法在过去的几年里，我们开发并应用了一些新的蛋白质功能注释。用于细菌基因组的全蛋白质组结构预测和功能注释的工作流程，应用到实验室菌株大肠杆菌 K12 和最小基因组支原体 JCVI-syn3.0。工作流程的特点是结构信息的整合（包括高精度蛋白质结构预测）在功能注释中，与经典方法（例如序列同源性和 syntenty，以及最近的发展，例如包含基于深度学习的预测器；总的来说，我们的工作流程提供了高度准确的功能注释，这对于“困难”的情况特别有用没有明确注释的同系物的蛋白质靶点，我们现在将重点转移到将我们的工具应用于细菌病原体的蛋白质组，首先重点关注尿路致病性大肠杆菌。继续发展我们的结构/功能预测能力，以进一步提高准确性并增加提供的信息丰富（目标 1），执行预测引导的可能的生化表征毒力基因，用于评估预测性能并识别潜在的药物靶点（目标 2），获得蛋白质的实验结构被确定为困难的结构目标，可能代表新的折叠或已知折叠的异常序列（目标 3），并测试可能的生理重要性在体内小鼠模型中新发现的毒力因子（目标 4）下收集的实验数据。目标 2-4 将不断与目标 1 下正在进行的方法开发相结合，以最大限度地提高该项目的结果将包括对所开发工具的性能和实用性的进一步改进。广泛使用和引用的工具，用于快速结构/功能预测、特定毒力的识别泌尿道致病性大肠杆菌的决定因素以及对其如何靶向的初步见解药物干预以及潜在毒力因子的其他结构数据将有助于基于结构的药物设计并提高现有结构模板库的覆盖范围以指导未来蛋白质结构和功能预测。