Internet-scale discovery of RNA bioengineering rules

互联网规模发现RNA生物工程规则

• 批准号: 8481559
• 负责人: Rhiju Das
• 金额: $ 44.47万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-05 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8481559
• 关键词: Adenine; Adoption; Algorithms; Anti-Bacterial Agents; Antiviral Therapy; Automobile Driving; Bacteria; Base Pairing; Benchmarking; Biomedical Computing; Biomedical Engineering; Chemicals; Communities; Computational algorithm; Computer Simulation; Computing Methodologies; Crowding; Data; Data Analyses; Databases; Development; Disease; Elements; Failure; Feedback; Functional RNA; Funding; Genetic; Glean; HIV; Human; In Vitro; Internet; Intuition; Knowledge; Life; Ligand Binding; Maps; Marketing; Masks; Methods; Mining; Modeling; Nanotechnology; Nucleic Acids; Nucleotides; Organism; Pattern; Performance; Play; Positioning Attribute; Publications; RNA; RNA Biochemistry; RNA Conformation; RNA Folding; RNA Sequences; RNA chemical synthesis; Research; Resolution; Retroviridae; Role; Science; Scientist; Seeds; Shapes; Social Network; Solutions; Source; Staging; Structure; Testing; Time; United States National Institutes of Health; Validation; Viral; base; cost; design; high throughput technology; insight; nanoengineering; neoplastic cell; next generation; novel; programs; repository; research study; sensor; success; tool; trend

DESCRIPTION (provided by applicant): Recent discoveries of biologically fundamental non-coding RNAs are inspiring novel antibacterial, antitumor, and antiviral therapies that might disable or manipulate the molecules involved. However, in silico folding models cannot yet confidently predict RNA conformations in vitro, slowing the development of these potentially life-saving efforts. To develop the next-generation of robust and rigorously tested RNA bioengineering rules, we are exploring an unconventional strategy: last year, we released a citizen science project enabling non-experts to develop and test folding hypotheses in internet-scale RNA design competitions that are rigorously scored through wet-lab feedback. Launched in early 2011, the 25,000-player EteRNA project already outperforms existing previous methods for designing novel RNA structures which fold properly in vitro. The critical next stage is to sustain and formalize the RNA nanoengineering insights preserved within the EteRNA community. We propose steps to consolidate the community's experimentally validated human computation into a suite of automated design algorithms (EteRNAbot) usable by the entire RNA nanoenginering community. Beyond compiling such cases, we will crowd-source the rigorous search and resolution of in silico and in vitro design failures with thousands of new experiments. Finally, we will deploy the EteRNA- 3D interface for both expert and crowd-sourced three-dimensional design, leveraging a growing database of 3D building blocks and the ROSETTA RNA folding/design algorithms. We will evaluate success in each aim via synthesis and single-nucleotide- resolution chemical mapping of novel designs through our high-throughput wet-lab pipeline. More generally, we will evaluate success by assessing the extent of utilization and citation of the automated design tools; the extent of mining and citation of the publically available RNA Mapping Database generated for the project; and the adoption of the EteRNA paradigm for Internet-scale scientific discovery in biomedical computation problems beyond nucleic acid design.

描述（由申请人提供）：生物学基础非编码RNA的最新发现正在激发新的抗菌、抗肿瘤和抗病毒疗法，这些疗法可能使相关分子失效或对其进行操纵。然而，计算机模拟折叠模型还不能自信地预测RNA在体外的构象，减缓了这些潜在的拯救生命的努力的发展。为了开发下一代强大且经过严格测试的RNA生物工程规则，我们正在探索一种非传统的策略：去年，我们发布了一个公民科学项目，使非专家能够在互联网规模的RNA设计竞赛中开发和测试折叠假设，这些竞赛通过湿实验室反馈进行严格评分。EteRNA项目于2011年初启动，拥有25，000名参与者，在设计新型RNA结构方面已经优于现有的方法，这些结构可以在体外正确折叠。关键的下一个阶段是维持和正式化保存在EteRNA社区内的RNA纳米工程见解。我们提出了一些步骤，将社区经过实验验证的人类计算整合到一套可供整个RNA纳米工程社区使用的自动设计算法（EteRNabot）中。除了汇编这些案例之外，我们还将通过数千个新的实验，对计算机和体外设计失败进行严格的搜索和解决。最后，我们将为专家和众包三维设计部署EteRNA- 3D界面，利用不断增长的3D构建块数据库和ROSETTA RNA折叠/设计算法。我们将通过我们的高通量湿实验室管道，通过合成和新颖设计的单核苷酸分辨率化学图谱来评估每个目标的成功。更一般地说，我们将通过评估自动化设计工具的利用和引用程度来评估成功;为该项目生成的生物可用RNA映射数据库的挖掘和引用程度;以及在核酸设计之外的生物医学计算问题中采用EteRNA范式进行互联网规模的科学发现。