High-resolution modeling of protein-RNA interfaces

蛋白质-RNA 界面的高分辨率建模

基本信息

批准号：
9388893
负责人：
Philip Bradley
金额：
$ 45.24万
依托单位：
FRED HUTCHINSON CANCER RESEARCH CENTER
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-15 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9388893
关键词：
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 Communities Complex Computer Simulation 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 Inherited Left Life Link Malignant Neoplasms Manuals Maps Measures Mediating Methods Modeling Molecular Medicine Molecular Models 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 programs protein complex success targeted treatment tool

项目摘要

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.

概括 RNA-蛋白质相互作用介导信息翻译过程中的多个关键调控步骤基因组到细胞机器，这些基本相互作用的破坏贯穿始终不幸的是，缺乏预测性的传染病和遗传性疾病、神经退行性疾病和癌症。 RNA-蛋白质相互作用的计算工具正在减缓潜在挽救生命的努力的发展该提案汇集了四种相互作用来解决人类疾病。实验室以我们最近的研究为基础，解决了这一瓶颈，这些研究首次实现了盲法蛋白质-RNA结构预测达到近原子分辨率，大规模预测蛋白质-RNA 结合能量学的准确性和精度优于 1 kcal/mol，并重新设计了复杂的蛋白质 - RNA 接口可在体内准确地重新定位沉默复合物。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：自动纠正实验蛋白质-RNA 复合结构中普遍存在的错误（目标 1)，我们最丰富的蛋白质-RNA信息资源；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的新调控联系；设计新型工程RNA-蛋白质相互作用（目标3）以促进遗传事件的合理扰动帮助生物学研究并最终改善疾病，我们将评估每个目标的成功程度。通过真正的盲目预测，通过快速出现的冷冻电子显微镜图进行测试并重新利用测序仪可以在单个实验中测量数十万个 RNA-蛋白质亲和力，等等广泛而言，一般生物医学研究界采用我们的 Rosetta 软件和在线工具。拟议的以蛋白质-RNA 为重点的研究涉及分子建模领域，该领域已获得令人惊讶的是，计算界很少关注，但对于加速来说无疑是重要的生物学理解和分子医学。