High-resolution modeling of protein-RNA interfaces

蛋白质-RNA 界面的高分辨率建模

• 批准号: 10641354
• 负责人: Philip Bradley
• 金额: $ 11.4万
• 依托单位: FRED HUTCHINSON CANCER 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 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10641354
• 关键词: resolution; modeling; protein; RNA; interfaces

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.

总结 RNA-蛋白质相互作用介导多个关键的调控步骤，在翻译的信息，从 基因组到细胞机器，这些基本相互作用的破坏贯穿始终 传染性和遗传性疾病、神经变性和癌症。不幸的是，缺乏预测 RNA-蛋白质相互作用的计算工具正在减缓潜在的拯救生命努力的发展 这些相互作用的目标或重新利用这些相互作用来解决人类疾病。该提案汇集了四个 实验室来解决这个瓶颈，建立在我们最近的研究基础上，这些研究首次实现了盲 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确度优于1 kcal/mol，并重新设计复杂的蛋白质- RNA接口在体内精确地重新靶向沉默复合物。我们在此建议统一，严格测试， 并传播我们实验室的方法，以解决蛋白质中三个独立但协同的计算问题， RNA研究：实验蛋白质-RNA复合物结构中普遍存在的错误的自动校正 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中新的调控联系; 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 来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 通过快速出现的冷冻电子显微镜图测试真正的盲预测， 测序仪可以在单个实验中测量数十万个RNA-蛋白质亲和力， 广泛地说，通过一般生物医学研究社区采用我们的Rosetta软件和在线工具。 拟议中的蛋白质-RNA重点研究解决了分子建模领域， 令人惊讶的是，在计算界很少有人关注，但对于加速计算来说， 生物学理解和分子医学。