High-resolution modeling of protein-RNA interfaces

蛋白质-RNA 界面的高分辨率建模

• 批准号: 10013238
• 负责人: Philip Bradley
• 金额: $ 30.02万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10013238
• 关键词: 3-Dimensional; Acceleration; Accounting; Address; Adoption; Affinity; Algorithm Design; Algorithms; Alleles; Area; Attention; Award; Benchmarking; Binding; Binding Proteins; Biological; Biological Models; Biomedical Research; Blinded; Capsid Proteins; Cells; Communicable Diseases; Communities; Complex; Computer Models; Computer software; Computing Methodologies; Cryoelectron Microscopy; Crystallization; DNA; Data Set; Development; Disease; Double-Stranded RNA; Engineering; Event; Experimental Designs; Funding; Gene Expression; Generations; Genetic; Genome; Grant; HIV; Hereditary Disease; High-Throughput Nucleotide Sequencing; Human; Human Pathology; Left; Life; Link; Malignant Neoplasms; Manuals; Maps; Measures; Mediating; Methods; Modeling; Molecular Medicine; Mutation; Nerve Degeneration; Nucleic Acids; Organism; Parkinson Disease; Pathogenicity; Play; Post-Transcriptional Regulation; Protein Engineering; Proteins; Proteome; Protocols documentation; Publishing; RNA; RNA Binding; RNA Folding; RNA Processing; RNA Recognition Motif; RNA-Binding Proteins; RNA-Protein Interaction; Regulation; Research; Research Personnel; Resolution; Resources; Ribosomes; Role; Sampling; Savings; Site; Specificity; Spliceosomes; Structure; System; Telomerase; Telomere Maintenance; Tertiary Protein Structure; Testing; Time; Translations; Variant; Viral; Virus; Virus Diseases; Work; blind; career; cell type; computerized tools; density; design; experimental study; genetic information; human disease; in vivo; model design; molecular modeling; mutant; novel; off-target site; programs; protein complex; success; targeted treatment; tool

SUMMARY RNA-protein interactions mediate multiple critical regulatory steps in the translation of information from the genome to the cellular machine, and corruptions of these fundamental interactions are implicated throughout infectious and inherited disease, neurodegeneration, and cancer. Unfortunately, a scarcity of predictive computational tools for RNA-protein interactions is slowing the development of potentially life-saving efforts that either target or repurpose these interactions to address human disease. This proposal brings together four labs to resolve this bottleneck, building on our recent studies that have achieved – all for the first time – blind protein-RNA structure predictions reaching near-atomic resolution, large-scale prediction of protein-RNA binding energetics with accuracy and precision of better than 1 kcal/mol, and redesign of a complex protein- RNA interface to accurately retarget silencing complexes in vivo. We propose herein to unify, rigorously test, and disseminate our labs’ methods to tackle three separate but synergistic computational problems in protein- RNA research: automated correction of errors that pervade experimental protein-RNA complex structures (Aim 1), our richest resources of protein-RNA information; prediction of impacts of mutation on RNA-protein interaction energetics (Aim 2) that could highlight new regulatory links in disease-associated alleles; and design of novel engineered RNA-protein interactions (Aim 3) to facilitate rational perturbation of genetic events to aid biological inquiry and eventually to ameliorate disease. We will evaluate success within each of our Aims through true blind predictions tested through rapidly emerging cryoelectron microscopy maps and repurposed sequencers that can measure hundreds of thousands of RNA-protein affinities in single experiments and, more broadly, by adoption of our Rosetta software and online tools by the general biomedical research community. The proposed protein-RNA-focused research addresses an area of molecular modeling that has received surprisingly little attention in the computational community but is unambiguously important for accelerating biological understanding and molecular medicine.

摘要 RNA-蛋白质相互作用在信息翻译中的多个关键调控步骤中起中介作用 基因组到细胞机器，这些基本相互作用的破坏贯穿始终 传染病和遗传性疾病、神经变性和癌症。不幸的是，缺乏预测性 RNA-蛋白质相互作用的计算工具正在减缓可能拯救生命的努力的发展 以这些互动为目标或重新调整用途，以应对人类疾病。这项提案汇集了四个 解决这一瓶颈的实验室，建立在我们最近的研究基础上，所有这些都是第一次实现盲目 蛋白质-RNA结构预测达到近原子分辨率，蛋白质-RNA的大规模预测 结合能量学的准确度和精密度优于1千卡/摩尔，并重新设计了一种复杂的蛋白质- RNA接口，以准确地在体内重定沉默复合体。我们在此建议统一，严格测试， 并传播我们实验室的方法来解决蛋白质中三个独立但协同的计算问题- RNA研究：对实验蛋白质-RNA复杂结构中普遍存在的错误进行自动纠正(AIM 1)，我们最丰富的蛋白质-RNA信息来源；预测突变对RNA-蛋白质的影响 相互作用能量学(目标2)，可以突出疾病相关等位基因中新的调控联系；以及 设计新的工程RNA-蛋白质相互作用(目标3)以促进遗传事件的合理扰动 以帮助生物研究，并最终改善疾病。我们将在我们的每个目标中评估成功 通过通过快速出现的冷冻电子显微镜图谱和重新调整用途进行测试的真盲预测 测序仪可以在一次实验中测量数十万个RNA-蛋白质亲和力，以及更多 广泛地说，通过普通生物医学研究社区采用我们的Rosetta软件和在线工具。 拟议的以蛋白质-RNA为重点的研究解决了分子建模的一个领域，该领域已收到 令人惊讶的是，在计算社区中很少有人关注，但对加速来说无疑是重要的 生物学理解和分子医学。