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: 9976447
• 负责人: Yang Zhang
• 金额: $ 72.24万
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9976447
• 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

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.

摘要 鉴于最近测序基因组数量的爆炸性增长和功能信息的相对缺乏， 在其内容上，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其是 急性细菌;广泛的寄生虫和致病细菌物种影响人类健康，只有 计算方法，当适当地结合有针对性的生物化学实验，可以 提供了解其生理所需的可靠、高通量注释。当前 一种计算功能预测的方法主要基于从已知蛋白质的相似性转移， 然而，当同源性水平较低时，该序列变得越来越不可靠。最近，重大 在蛋白质3D结构预测方面取得了进展，这一点在全社会范围的盲测中得到了证实 实验和现有技术的方法可以为大多数基因组构建正确的蛋白质折叠 序列而不使用紧密同源的模板。建立在生物功能更重要的假设之上， 与3D结构直接相关，而不是序列，该建议旨在启动从蛋白质到蛋白质的范式转变。 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 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 细菌基因组的注释，特别关注无法通过 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尖端的蛋白质功能预测方法， 低分辨率（但正确折叠）模型的结构预测。具体目标包括： 开发新的基于结构的方法，用于蛋白质-配体结合位点的建模，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测试， 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，将在实验之间建立迭代预测-实验-细化循环， 计算注释，以指导功能建模方法的发展和进步。的研究 本项目将重点研究E. coliK 12菌株，其中>10%的基因组保持未注释，尽管 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 其可用作各种其它微生物基因组的可靠功能注释的资源。相比 利用目前的基于序列的方法，基于结构的管道的成功可能会将 将当前基因组数据库中的近1000万个（或30%）非同源或远距离同源靶标插入到 可靠的函数注释机制。