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Toward Atomic-Accuracy Design of Functional RNAs

功能性 RNA 的原子精度设计

基本信息

批准号：
8982079
负责人：
Joseph Yesselman
金额：
$ 5.42万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-12-01 至 2017-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8982079
关键词：
Adopted Algorithm Design Big Data Binding Biological Assay Catalysis Cell Culture Techniques Chemicals Clinical Treatment Collaborations Communities Complex Coupled Crystallography Data Databases Development Diagnostic Disease Engineering Faculty Fluorescence Resonance Energy Transfer Generations Genes Genetics and Medicine HIV Heart Heart Diseases Hydroxyl Radical Hypertrophic Cardiomyopathy Maps Measurement Methods MicroRNAs Modeling Molecular Machines Nucleotides Performance Phase II Clinical Trials Physics RNA RNA Folding Reagent Reporting Research Personnel Resolution Resources Shapes Structure Sudden Death System Technology Testing Therapeutic Time Untranslated RNA Validation Viral Work base career design in vivo malignant breast neoplasm next generation novel public health relevance receptor research study response scaffold sensor structural biology success three dimensional structure three-dimensional modeling tool young adult

项目摘要

DESCRIPTION (provided by applicant): A new paradigm has emerged to utilize RNA-based molecular machines as sensors to regulate new gene networks and as therapeutics to treat diseases such as HIV and breast cancer. These biomolecular machines harness RNA's extraordinary ability to adopt complex 3D shapes, perform catalysis, and change shapes in response to cellular and viral molecules. Furthermore, RNA can be produced in large quantities using established synthesis and can be coupled to increasingly facile cellular delivery methods. Unfortunately, despite RNA's potential as a design medium, development of RNA-based therapeutics and sensors are significantly hindered by inaccurate models of RNA folding and design, necessitating time-consuming selection methods and trial-and-error refinement. To accelerate the generation of RNA-based technology, I have developed RNAMake, the first automated RNA 3D design toolkit. RNAMake utilizes RNA motifs, the building blocks of RNA 3D structure which, in a few cases, have been shown to be modular. I propose to resolve current barriers to automate the design of RNA-based therapeutics through the following aims: First exhaustively characterizing the modularity of all known motifs by testing them in 3D design problems to generate a curated database of highly modular building blocks, increasing the confidence in RNAMake's designs and second to demonstrate RNAMake's straightforward approach to developing a novel RNA-based sensors to detect mir129, mir212, mir21, and mir208a miRNAs which are critical indicators of hypertrophic cardiomyopathy (HCM). In both of these aims, I will evaluate success through using a combination of massively parallel SHAPE chemical mapping, selective crystallography (Jeffrey Kieft), FRET measurements (William Greenleaf) and cell culture based assays (Euan Ashley). This proposal is highly collaborative, bringing together experiments a wide variety of fields including Structural Biology, Genetics and Medicine. Successful completion of the aims set forth, will yield the first detailed characterizatin of motif modularity in a publically accessible database, the first automated platform for 3D design that can be used by any RNA engineering group, and high-profile illustrations of its use for biomedically relevant RNA-based machines. In addition this work will be pursued subsequently as the focal point of my faculty career.