Computational studies of in vitro selection of RNAs

描述（由申请人提供）：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，包括抑制蛋白质功能的治疗适体和控制基因表达的核酶。然而，RNA体外选择过程缺乏系统的计算分析来潜在地增加发现复杂合成RNA的概率。为了填补这一重大空白，我们已经开发了设计结构化RNA库和优化RNA功能的计算方法，以提高体外选择和定向进化实验的生产力。在体外选择实验和我们的计算分析表明，设计的RNA池具有不同的结构基序，可以提高发现复杂的RNA基序，很少发现在随机池。这是我们在本提案中旨在证明和应用的主要假设。为了解决目前的局限性和发展计算在体外选择，我们的目标是开发：（目标A）计算方法的结构化RNA池设计;（B）筛选和测试设计的RNA池的方法;和（目标C）的计算方法模拟定向进化优化RNA功能。在目标A中，我们将开发结构化池设计方法，允许使用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。在目标B中，我们将使用基序扫描和筛选（例如，PI的SVD/TNPACK软件工具）方法。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，通过实验验证设计的池上级随机池。在目标C中，我们将开发一种计算方法，通过结合基序扫描/筛选方法，核苷酸转换（“混合”）矩阵方法和偏最小二乘法来积累有益的突变，以模拟RNA基序选择和诱变PCR程序;从我们的计算体外进化方案中优化的RNA候选人将由Jaeger实验室进行实验测试。随着这些算法的发展和实验合作，我们希望我们的计算方法池设计和分析和定向进化将提供一个全面的资源，以帮助实验人员设计更好的体外选择实验和优化RNA功能的要求苛刻的生物医学应用，如发现高结合亲和力的适体靶向蛋白质在癌症和其他疾病。我们的项目还为学生提供计算生物学，化学数学和生物医学方面的持续优秀的跨学科培训。公共卫生相关性：通过体外选择和定向进化实验发现的合成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的治疗性RNA。我们的工作是基于这样的假设，即设计的结构化RNA文库比随机池更适合发现复杂的RNA。我们的项目将开发和测试用于设计结构化RNA库和增强定向进化的计算方法，以帮助实验人员发现复杂的RNA。我们将与实验生物医学研究人员互动，以利用和扩展我们的方法的能力，推进生物医学应用分子工具的发展。

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.