Exploring the unknown protein universe using evolutionary information

利用进化信息探索未知的蛋白质宇宙

• 批准号: 10247669
• 负责人: Sergey L. Ovchinnikov
• 金额: $ 42.25万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-07 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10247669
• 关键词: Award; Biology; Code; Communities; Data; Databases; Disease; Drops; Drug Targeting; Eukaryota; Evolution; Expeditions; Faculty; Family; Future; Genes; Genome; Genomics; Goals; Habitats; Homologous Protein; Human; Institution; Lead; Learning; Life; Mentors; Metagenomics; Microbe; Modeling; Molecular; Mutation; Natural Selections; Nature; Organ; Pattern; Persons; Phylogenetic Analysis; Principal Investigator; Process; Protein Engineering; Protein Family; Proteins; Public Health; Research; Sampling; Science; Sequence Homology; Statistical Models; Structure; System; Systems Biology; Therapeutic; Time; Training; Trees; Virus; commune; computerized tools; cost; dark matter; experience; experimental study; improved; insight; interest; meetings; metagenome; microbiome; protein complex; protein protein interaction; protein structure; protein structure prediction; single cell sequencing; skills; supportive environment; three dimensional structure; training opportunity

Project Summary/Abstract: For billions of years, nature has been conducting the greatest experiment of all time. Imagine one day gaining access to the detailed notes from these experiments. Today, with worldwide expeditions to collect samples from all habitats, single-cell sequencing of unculturable microbes and the rapid drop in sequencing costs, we can finally tap into nature and gain access to these notes. All that is missing is a Rosetta Stone to interpret this data. The traditional approach, to interpreting sequence data, is through comparison to known information, such as annotated genomes and/or experimentally characterized protein families. Unfortunately, nearly half of metagenomic data (coming from either environmental samples or microbiomes) lacks any detectable sequence homology to any protein family, let alone to any isolated genome. Furthermore, the rate at which this “dark matter” is discovered, far exceeds the rate at which experiments can be done to characterize it. An alternative approach is to learn a generative, statistical model of the evolutionary process itself. The parameters of this model should in turn provide the constraints on natural selection. For protein-coding genes, the constraints includes folding, stability, and function. Recently, it was shown that a global statistical model of a protein family that captures both conservation and coevolution patterns in the family possesses this quality. The strength of coevolution term is correlated with residue-residue contacts in 3D structure. These contacts have since been used to computationally determine the 3D structures of hundreds of unknown protein families and complexes. These in turn, have been used to predict the function by looking at arrangement of conserved residues and structural similarity to known protein structures. Structural matches can occur in the absence of detectable sequence similarity because structural similarity is retained over larger evolutionary distances. I propose to 1) Develop an improved, unified, statistical model of protein evolution that takes into account functional and lineage constraints; 2) Apply the model to mine metagenomic “dark matter” sequences for new protein families, functions and protein-protein interactions; 3) Probe evolution of multicellularity through comparison of structures and interactions in the early tree of life. One of the results of the research will be a public database of new protein families and their predicted 3D structure and function. These will be used by structural, molecular and evolutionary biologists as a reference for future studies into the unknown protein universe.

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