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了来自从蜂窝机的基因组，以及这些基本互动的损坏均涉及传染性和遗传性疾病，神经退行性和癌症。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慢潜在的挽救生命努力的发展目标或重新利用这些相互作用以解决人类疾病。该提议汇集了四个解决这一瓶颈的实验室，基于我们最近获得的研究 - 首次获得的研究 - 蛋白-RNA结构预测达到接近原子分辨率，大规模预测蛋白-RNA 与1 kcal/mol的精度和精度相结合，并重新设计了复杂蛋白 RNA接口在体内准确重新定位沉默复合物。我们在此建议统一，严格测试，并传播我们实验室的方法，以解决蛋白质中的三个单独但协同的计算问题 RNA研究：自动校正遍及实验蛋白RNA复合结构的误差（AIM 1），我们最丰富的蛋白质-RNA信息资源；预测突变对RNA蛋白的影响相互作用的能量（AIM 2）可以强调与疾病相关等位基因的新调节联系；和新型工程RNA - 蛋白质相互作用的设计（AIM 3），以促进遗传事件的合理扰动帮助生物学调查，并最终改善疾病。我们将在每个目标中评估成功通过通过快速新兴的冷冻显微镜图测试并重新使用的真实盲目预测可以在单个实验中测量数十万RNA-蛋白亲和力的测序仪，更多从广义上讲，通过一般生物医学研究社区采用了我们的Rosetta软件和在线工具。拟议的以蛋白质-RNA为中心的研究涉及已接受的分子建模领域令人惊讶的是，在计算社区中很少关注，但对于加速而言明确很重要生物学理解和分子医学。