Structure-based functional annotation of microbial genomes

微生物基因组基于结构的功能注释

• 批准号: 10674978
• 负责人: Lydia Freddolino
• 金额: $ 74.66万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 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/10674978
• 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; Crystallography; Data; Databases; Development; Disease; Drug Design; Escherichia coli; Escherichia coli K12; Escherichia coli Proteins; Experimental Genetics; Failure; Feedback; Follow-Up Studies; Future; Genes; Genetic; Genome; High-Throughput Nucleotide Sequencing; Homologous Gene; 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; bacterial genetics; 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; overexpression; pathogenic bacteria; pathogenic microbe; predictive modeling; protein folding; protein function; protein protein interaction; protein purification; protein structure; protein structure prediction; public database; therapeutic target; tool; virulence gene

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.

摘要 现代生物学中最紧迫的挑战之一是如何将大量的 测序方面的最新进展所提供的有关生物序列的信息 技术，对生物系统的行为进行相应的洞察。确定功能和 蛋白质的生理作用仍然是这一挑战的主要组成部分；对许多物种来说，特别是 非模式微生物，如微生物病原体，蛋白质组的一部分，由注释不佳的 蛋白质可能接近50%，严重限制了我们识别发病机制和 潜在的治疗靶点。大量注释不佳的潜在生物蛋白质 重要的是需要不断开发高效可靠的计算方法 蛋白质的功能注释。在过去的几年里，我们开发和应用了几种新的 细菌基因组全蛋白质组结构预测和功能注释的工作流程 应用于实验室菌株E.ColiK12和最小基因组支原体JCVI-syn3.0。我们的 工作流以结构信息(包括高精度蛋白质)的集成为特色 结构预测)，以及经典的方法，如序列同源性和 Syntty，以及最近的发展，如包括基于深度学习的预测者；我们发现 总体而言，我们的工作流提供了高度准确的功能注释，这些注释特别适用于“困难的” 蛋白质靶标没有明确的注释同源基因。我们现在将重点转向将我们的工具应用于 细菌病原体的蛋白质组，最初的重点是致泌尿系统的大肠杆菌。具体来说，我们将 继续发展我们的结构/功能预测能力，以进一步提高准确性和增加 所提供信息的丰富性(目标1)，执行预测指导的生物化学表征 毒力基因，以评估预测性能和确定潜在的药物靶标(目标2)，获得 蛋白质的实验结构，被确定为可能代表新结构的困难结构靶点 折叠或已知折叠的不寻常序列(目标3)，并测试可能的生理重要性 在活体小鼠模型中新发现的毒力因子(目标4)。收集到的实验数据是在 目标2-4将继续与目标1下的持续方法开发相结合，以最大限度地提高 所开发工具的性能和实用性。该项目的成果将包括进一步改进 广泛使用和引用的工具，用于快速结构/功能预测，鉴定特定毒力 尿路致病性大肠杆菌的决定因素及其靶向的初步研究 药物干预，以及潜在毒力因素的额外结构数据，将有助于 基于结构的药物设计和提高现有结构模板库的覆盖率，以指导未来 蛋白质结构和功能预测。

项目成果

Underestimation-Assisted Global-Local Cooperative Differential Evolution and the Application to Protein Structure Prediction.

低估辅助全局局部协同差异进化及其在蛋白质结构预测中的应用

• DOI: 10.1109/tevc.2019.2938531
• 发表时间: 2020-06
• 期刊: IEEE transactions on evolutionary computation : a publication of the IEEE Neural Networks Council
• 作者: Zhou XG;Peng CX;Liu J;Zhang Y;Zhang GJ
• 通讯作者: Zhang GJ

Changing the Apoptosis Pathway through Evolutionary Protein Design.

• DOI: 10.1016/j.jmb.2018.12.016
• 发表时间: 2019-02
• 期刊: Journal of molecular biology
• 影响因子: 5.6
• 作者: D. Shultis;Pralay Mitra;Xiaoqiang Huang;Jarrett S Johnson;Naureen Aslam Khattak;F. Gray;Clint Piper;Jeff Czajka;Logan Hansen;B. Wan;Krishnapriya Chinnaswamy;Liu Liu-Liu;Mi Wang;Jingxi Pan;J. Stuckey;T. Cierpicki;C. Borchers;Shaomeng Wang;M. Lei;Yang Zhang

Progressive assembly of multi-domain protein structures from cryo-EM density maps.

• DOI: 10.1038/s43588-022-00232-1
• 发表时间: 2022-04
• 期刊: NATURE COMPUTATIONAL SCIENCE
• 作者: Zhou, Xiaogen;Li, Yang;Zhang, Chengxin;Zheng, Wei;Zhang, Guijun;Zhang, Yang
• 通讯作者: Zhang, Yang

Cryo-EM density map fitting driven in-silico structure of human soluble guanylate cyclase (hsGC) reveals functional aspects of inter-domain cross talk upon NO binding.

• DOI: 10.1016/j.jmgm.2019.04.009
• 发表时间: 2019-07
• 期刊: Journal of molecular graphics & modelling
• 影响因子: 2.9
• 作者: Khalid RR;Maryam A;Fadouloglou VE;Siddiqi AR;Zhang Y
• 通讯作者: Zhang Y

MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.

Metago：通过低分辨率蛋白质结构预测和蛋白质 - 蛋白质网络映射预测非同源蛋白的基因本体论。

• DOI: 10.1016/j.jmb.2018.03.004
• 发表时间: 2018-07-20
• 期刊: Journal of molecular biology
• 影响因子: 5.6
• 作者: Zhang C;Zheng W;Freddolino PL;Zhang Y
• 通讯作者: Zhang Y