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：
10216988
负责人：
Lydia Freddolino
金额：
$ 71.59万
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 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10216988
关键词：
Acute Address Algorithms Amino Acid Sequence Animal Model Attention Automated Annotation Bacteria Bacterial Genome Bacterial Physiology Base Sequence Binding Binding Proteins Binding Sites Biochemical Biochemical Genetics Bioinformatics Biological Process Biology Chemicals Communities Computational Biology Consensus Data Databases Detection Development Disease Distant Enzymes Escherichia coli Escherichia coli K12 Escherichia coli Proteins Experimental Designs Explosion Feedback Genes Genome Genomics Goals Health High-Throughput Nucleotide Sequencing Human Investigation Laboratories Ligand Binding Low Prevalence Methods Microbiology Modeling Modernization Molecular Biology Nucleic Acid Sequence Homology Ontology Organism Pathogenicity Pathway interactions Pattern Performance Physiology Play Protein Region Proteins Proteome Protocols documentation Recording of previous events Resistance Resolution Resources Role Sequence Homology Stress Structural Models Structure Systems Biology Taxonomy Testing Time Validation base biological adaptation to stress blind computational pipelines design drug discovery experimental study follow-up genome annotation genome database genome wide screen genome-wide improved indexing insight knockout gene method development microbial microbial genome novel novel therapeutics protein folding protein function protein protein interaction protein structure protein structure prediction screening small molecule structural biology success synergism three dimensional structure transcription factor

项目摘要

Abstract Given the recent explosion in the number of sequenced genomes and the relative lack of functional information on their contents, annotating the biological functions of all proteins across different genomes represents a major challenge to modern molecular and computational biology. The problem of genome annotation is particularly acute for bacteria; a vast range of commensal and pathogenic bacterial species impact human health, and only computational approaches, when appropriately combined with carefully targeted biochemical experiments, can provide the reliable, high-throughput annotations necessary to understand their physiology. The current approach to computational function prediction is mainly based on transfer from known proteins of similar sequence, which however becomes increasingly unreliable when the homology level is low. Recently, significant progress has been achieved in protein 3D structure prediction as witnessed by the community-wide blind testing experiments, and current state of the art methods can construct correct protein folds for the majority of genome sequences without using close homologous templates. Building on the hypothesis that biological function is more directly associated with 3D structure than sequence, this proposal aims to initiate a paradigm shift from protein structure prediction to structure-based function annotations. Combining expertise from computational biology, microbiology, and structural biology, the PIs will systemically examine the potential and scope of how computational structure models from cutting-edge modeling methods can help provide reliable high-throughput annotations of bacterial genomes, with a particular focus on the difficult targets that cannot be addressed by the existing sequence homology-based approaches. This project is designed to develop and test several cutting-edge approaches for protein function prediction using low-resolution (but correctly folded) models from the structure predictions. The specific aims include the development of novel structure-based methods for modeling of the protein-ligand binding sites, and enzyme and gene ontologies. The modeling methods and results will be tested by a set of carefully designed experiments, including high-throughput chemical screening and detailed structural-biology based characterizations. At all stages, iterative prediction-to-experiment-to-refinement loops will be established between the experiments and computational annotations to guide the functional modeling method development and advances. The studies of this project will be focused on E. coli K12 strain, for which >10% of the genome remains un-annotated despite a long history of use as a model organism; but the long-term goal is to build up a novel and robust framework which can be used as a resource for reliable function annotations for various other microbial genomes. Compared with current sequence-based approaches, the success of the structure-based pipelines could potentially convert nearly 10 million (or 30%) of the non- or distant-homologous targets in the current genome database into the reliable function annotation regime.

抽象的鉴于最近测序基因组数量的爆炸性增长以及功能信息的相对缺乏就其内容而言，注释不同基因组中所有蛋白质的生物学功能代表了一个主要的对现代分子和计算生物学的挑战。基因组注释的问题尤为突出对细菌很敏感；大量的共生菌和致病菌会影响人类健康，并且只有计算方法，当与仔细有针对性的生化实验适当结合时，可以提供了解其生理学所需的可靠、高通量注释。目前的计算功能预测的方法主要基于从相似的已知蛋白质的转移然而，当同源性水平较低时，该序列变得越来越不可靠。近期，重大全社区盲测见证蛋白质3D结构预测取得进展实验和当前最先进的方法可以为大多数基因组构建正确的蛋白质折叠序列而不使用紧密的同源模板。建立在生物功能更重要的假设之上与 3D 结构而不是序列直接相关，该提案旨在启动蛋白质的范式转变结构预测到基于结构的功能注释。结合计算生物学的专业知识，微生物学和结构生物学，PI 将系统地研究如何进行的潜力和范围来自尖端建模方法的计算结构模型可以帮助提供可靠的高通量细菌基因组注释，特别关注细菌基因组无法解决的困难目标现有的基于序列同源性的方法。该项目旨在开发和测试几种用于蛋白质功能预测的前沿方法，使用来自结构预测的低分辨率（但正确折叠）模型。具体目标包括开发基于结构的新型方法来建模蛋白质-配体结合位点以及酶和基因本体论。建模方法和结果将通过一组精心设计的实验进行检验，包括高通量化学筛选和详细的基于结构生物学的表征。根本阶段，将在实验和实验之间建立迭代预测-实验-细化循环计算注释指导功能建模方法的发展和进步。的研究该项目将重点关注大肠杆菌 K12 菌株，尽管其基因组的 10% 以上仍未注释作为模式生物的悠久历史；但长期目标是建立一个新颖而强大的框架它可以用作各种其他微生物基因组的可靠功能注释的资源。比较的通过当前基于序列的方法，基于结构的管道的成功可能会转化为将当前基因组数据库中近 1000 万（或 30%）的非同源或远距离同源目标导入到可靠的函数注释机制。