Connecting the universe of proteins to address annotation inequality in the microbial proteome

连接蛋白质领域以解决微生物蛋白质组中的注释不平等问题

基本信息

批准号：
10658439
负责人：
Erik Scott Wright
金额：
$ 45.81万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-09 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10658439
关键词：
Acceleration Acute Address Algorithms Antibiotic Resistance Archaeal Genome Architecture Astronomy Bacterial Genome Benchmarking Bioinformatics Biomedical Research Cell physiology Cells Classification Collection Color Computer software Computers Consumption DNA Darkness Data Databases Disease Drug Targeting Evolution Fruit Fungal Genome Genbank Genes Genetic Genome Genomics Guilt Height Hypothetical Protein Inequality Knowledge Label Laboratories Letters Life Light Link Machine Learning Maps Methods Mutation Names Organism Orthologous Gene Output Pathogenicity Pathway interactions Principal Investigator Proteins Proteome Regulation Research Resources Role Signal Transduction Software Validation Surface System Techniques Time Trees Virulence Work comparative genomics dark matter gene function genetic information genome annotation innovation insight knowledge base link protein microbial microbial genome microorganism neglect new therapeutic target novel open data open source pan-genome pathogen pressure programs tool web app web site web-based tool

项目摘要

Principal Investigator (Wright, Erik S.) Abstract: The rapidly increasing number of microbial genomes has revealed an enormous diversity of proteins without any known function. We still know relatively little about over half the proteins encoded in a typical bacterial genome, and traditional laboratory techniques are too time consuming to characterize even a small fraction of the observable universe of proteins. This neglected "dark proteome" potentially contains many important determinants of virulence, antibiotic resistance, and disease. Casting a light on the dark proteome is possible through comparative genomics because proteins interacting across evolutionary timescales leave behind a signature of coevolution that can be used to connect proteins of unknown function with proteins of known function. This 'guilt-by-association' analysis helps to generate hypotheses about the cellular role of unexplored proteins. Here, we develop novel methods for quantifying coevolutionary signals, and we apply these methods to an unprecedently large collection of genomes spanning the microbial tree of life. This project will result in a network of coevolving genes that we will make publicly accessible as a web tool for biomedical research. To further harness the power of comparative genomics, we will develop and deploy web applications that provide deeper insights into the universe of microbial proteins.

首席研究员（赖特，埃里克·S.）抽象的：微生物基因组数量的迅速增加揭示了蛋白质的巨大多样性，而无需任何已知的函数。我们对典型细菌编码的一半以上的蛋白质仍然知之甚少基因组，而传统的实验室技术太耗时，无法表征甚至一小部分可观察到的蛋白质宇宙。这个被忽视的“暗蛋白质组”可能包含许多重要的毒力、抗生素耐药性和疾病的决定因素。照亮黑暗的蛋白质组是可能的通过比较基因组学，因为跨进化时间尺度相互作用的蛋白质留下了共同进化的特征，可用于将未知功能的蛋白质与已知的蛋白质连接起来功能。这种“关联罪”分析有助于产生关于未探索的细胞作用的假设蛋白质。在这里，我们开发了量化共同进化信号的新方法，并应用这些方法涵盖微生物生命树的前所未有的大量基因组集合。该项目将导致共同进化基因网络，我们将作为生物医学研究的网络工具公开访问。到进一步利用比较基因组学的力量，我们将开发和部署提供网络应用程序对微生物蛋白质领域有更深入的了解。