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（Wright，Erik S.）抽象的：迅速增加的微生物基因组揭示了蛋白质的巨大多样性，没有任何已知功能。我们仍然对典型细菌中编码的蛋白质的一半以上的了解相对较少基因组和传统实验室技术太耗时了，无法表征一小部分可观察的蛋白质宇宙。这种被忽视的“深色蛋白质组”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。在黑暗蛋白质组上投下灯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可以用来连接未知功能的蛋白质与已知蛋白的蛋白质的签名功能。这种“内gui”分析有助于产生有关未探索的细胞作用的假设蛋白质。在这里，我们开发了用于量化协同进化信号的新方法，并应用了这些方法跨越了生命的微生物树，这是一大批基因组。这个项目将导致我们将作为生物医学研究的网络工具公开访问的共同发展基因网络。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的Web应用程序深入了解微生物蛋白的宇宙。