Computational studies of in vitro selection of RNAs

RNA 体外选择的计算研究

基本信息

批准号：
7901411
负责人：
Tamar Schlick
金额：
$ 30.96万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-01 至 2013-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7901411
关键词：
Address Advanced Development Affinity Algorithms Antibiotics Area Base Sequence Binding Biological Biological Assay Biology Biosensor Biotechnology Catalysis Catalytic RNA Cell physiology Characteristics Chemistry Code Collaborations Communicable Diseases Complex Computational Biology Computer Analysis Computer software Computing Methodologies DNA Development Disease Disease Pathway Effectiveness Elements Engineering Enzymes Evolution Experimental Models Faculty Gene Expression Generations Genes Genomics Graph Guanosine Triphosphate IBM Blue-Gene In Vitro Introns Knowledge Least-Squares Analysis Letters Libraries Ligase Link Malignant Neoplasms Mathematics Measures Messenger RNA Methods MicroRNAs Modeling Molecular Molecular Biology Molecular Medicine Monte Carlo Method Mutation Nanotechnology Nucleic Acids Nucleotides Performance Probability Procedures Process Productivity Proteins Proteomics Purine Nucleotides RNA RNA Processing RNA Sequences RNA library Regression Analysis Research Research Personnel Resources Sampling Scanning Scheme Science Scientist Screening procedure Simulate Site Slide Software Tools Structure Students Sum Tars Techniques Technology Testing Theoretical Studies Therapeutic Thermodynamics Toll-like receptors Training Virus Work abstracting aptamer base computer generated computer studies computerized tools design directed evolution experience functional genomics human VEGF protein improved in vitro Model inhibitor/antagonist innovation novel performance tests planetary Atmosphere protein function public health relevance research study response scaffold simulation stem success symposium synthetic biology theories tool training project

项目摘要

DESCRIPTION (provided by applicant): Project Summary/Abstract. Synthetic RNAs discovered via in vitro selection experiments have wide-ranging applications in biomedical sciences and biotechnology, including therapeutic aptamers that inhibit protein function and ribozymes that control gene expression. The RNA in vitro selection process, however, lacks systematic computational analysis to potentially increase the probability of discovering complex synthetic RNAs. To fill this significant gap, we have developed computational approaches for designing structured RNA pools and optimizing RNA functions to improve the productivity of in vitro selection and directed evolution experiments. In vitro selection experiments and our computational analysis suggest that designed RNA pools possessing diverse structural motifs can enhance discovery of complex RNA motifs which are rarely found in random pools. This is the main hypothesis we aim to demonstrate and apply in this proposal. To address current limitations and develop computational in vitro selection, we aim to develop: (Aim A) computational approaches to structured RNA pool design; (B) methods for screening and testing designed RNA pools; and (Aim C) a computational approach for simulating directed evolution for optimizing RNA functions. In Aim A, we will develop structured pool design approaches that allow generation of user-defined target structures with constant binding or catalytic motifs using a Monte Carlo simulation method. In Aim B, we will computationally test the performance of designed pools using motif scanning and screening (e.g., PI's SVD/TNPACK software tools) methods. In addition, we will experimentally verify that designed pools are superior to random pools via a collaboration with Luc Jaeger, an expert on RNA in vitro selection and nanotechnology. In Aim C, we will develop a computational approach to in vitro evolution by combining the motif scanning/screening methods, the nucleotide transition ("mixing") matrix approach, and the partial least squares method for accumulating beneficial mutations to model RNA motif selection and mutagenic PCR procedures; optimized RNA candidates from our computational in vitro evolution scheme will be experimentally tested by Jaeger's lab. With these algorithmic developments and experimental collaboration, we expect that our computational approaches to pool design and analysis and directed evolution will provide a comprehensive resource to assist experimentalists in designing better in vitro selection experiments and optimizing RNA functions for demanding biomedical applications such as discovering high-binding affinity aptamers targeting proteins in cancer and other diseases. Our project also provides continued excellent interdisciplinary training of students in computational biology, chemistry mathematics, and biomedicine. PUBLIC HEALTH RELEVANCE: Project Narrative Synthetic RNAs discovered via in vitro selection and directed evolution experiments have wide-ranging biological and biomedical applications, including therapeutic RNAs that modulate disease-related proteins. Our work is based on the hypothesis that designed structured RNA libraries are better than random pools for discovering complex RNAs. Our project will develop and test computational approaches for designing structured RNA pools and enhancing directed evolution to assist experimentalists in discovering complex RNAs. We will interact with experimental biomedical researchers to exploit and extend our methods' capabilities for advancing the development of molecular tools for biomedical applications.

描述（申请人提供）：项目摘要/摘要。通过体外选择实验发现的合成RNA在生物医学科学和生物技术中具有广泛的应用，包括抑制控制基因表达的蛋白质功能和核酶的治疗适体。然而，RNA的体外选择过程缺乏系统的计算分析，从而可能增加发现复杂合成RNA的可能性。为了填补这一显着的空白，我们开发了用于设计结构化RNA池并优化RNA功能以提高体外选择和定向进化实验的生产率的计算方法。体外选择实验和我们的计算分析表明，设计具有多种结构基序的RNA池可以增强在随机池中很少发现的复杂RNA基序的发现。这是我们旨在证明和应用此提案的主要假设。为了解决当前局限性并开发体外选择，我们旨在开发：（目标A）结构化RNA池设计的计算方法；（b）筛选和测试设计的RNA池的方法；（目标c）一种用于模拟定向进化的计算方法，以优化RNA功能。在AIM A中，我们将开发结构化的池设计方法，以使用Monte Carlo模拟方法生成具有恒定结合或催化基序的用户定义的目标结构。在AIM B中，我们将使用主题扫描和筛选（例如PI的SVD/TNPACK软件工具）方法在计算上测试设计池的性能。此外，我们将通过与RNA体外选择和纳米技术专家Luc Jaeger的合作来实验验证设计的池优于随机池。在AIM C中，我们将通过组合基序扫描/筛选方法，核苷酸转变（“混合”）矩阵方法来开发一种计算方法来进行体外进化，以及累积有益突变与RNA基序选择和诱变PCR程序的部分最小二乘方法； Jaeger的实验室将对我们的计算体外进化方案的优化RNA候选物进行实验测试。借助这些算法的发展和实验合作，我们希望我们的计算方法进行汇总设计和分析和指示进化，将提供全面的资源，以帮助实验者设计更好的体外选择实验，并优化RNA功能，以促进诸如发现高结合亲和力适应性蛋白质靶向癌症和其他癌症的生物医学应用。我们的项目还为计算生物学，化学数学和生物医学的学生提供了持续的出色跨学科培训。公共卫生相关性：通过体外选择和定向进化实验发现的项目叙事综合RNA具有广泛的生物学和生物医学应用，包括调节疾病相关蛋白质的治疗性RNA。我们的工作基于以下假设：设计的结构化RNA库比发现复杂RNA的随机池更好。我们的项目将开发和测试用于设计结构化RNA池并增强有向进化的计算方法，以帮助实验者发现复杂的RNA。我们将与实验性生物医学研究人员进行互动，以利用和扩展我们的方法的能力，以推进生物医学应用的分子工具的开发。