Combinational and Computational Methids for the Analysis, Prediction, and Design

用于分析、预测和设计的组合和计算方法

• 批准号: 7683172
• 负责人: Christine E Heitsch
• 金额: $ 26.34万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-10 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7683172
• 关键词: Address; Algorithms; Base Pairing; Biological; Characteristics; Code; Computational Technique; Data; Disease; Free Energy; Gene Expression; Goals; Human Genome Project; Methods; Modeling; Molecular Models; Outcome; Performance; Prevention; Proteins; RNA; RNA Folding; RNA Viruses; Research; Role; Structure; Structure-Activity Relationship; Viral Genome; Viral Packaging; combinatorial; design; mathematical model; molecular modeling; multicore processor; novel; novel strategies; success; viral RNA

DESCRIPTION (provided by applicant): The Human Genome Project and related efforts have generated enormous amounts of raw biological sequence data. However, understanding how biological sequences encode structural and functional information remains a fundamental scientific challenge. In particular, the information encoded in RNA viral genomes extends well beyond their protein coding role to the role of intra-sequence base pairing in viral packaging, replication, and gene expression. Thus, deciphering the different levels of information encoded in these sequences is essential for a full understanding of structure-function relationships in RNA viruses. Our goal is understanding how secondary structure information, expressed as the selective formation of base pairs, is encoded in large RNA viral genomes. Since current prediction methods cannot reliably and efficiently treat these lengthy sequences, we are developing novel combinatorial and computational approaches to the analysis, prediction, and design of viral RNA secondary structures. The outcomes of our research will be a discrete mathematical model of RNA folding and high-performance combinatorial algorithms for predicting secondary structures for large RNA molecules. The success of our methods for unenveloped icosahedral RNA viruses would extend to other large RNA molecules and have important implications for the prevention and treatment of numerous RNA-related diseases. Our research addresses 3 specific aims. (1) We will identify and evaluate characteristics of RNA secondary structures which differentiate base pairings that encode significant structural and functional information from those which are not well-determined. By refining our combinatorial model of RNA folding, we will distinguish configurations whose folding follows natural energy minima from base pairings that encode well-determined, and likely functionally significant, substructures. (2) We will predict new structures by developing the mathematical framework and computational techniques needed to construct a low-energy RNA secondary structure from minimal free energy substructures. By exploiting parallel and multicore processors, our novel approach will predict important functional motifs in the secondary structures of large RNA molecules with a greater degree of accuracy. (3) We will compare the compatibility of our predicted secondary structures with experimental information on RNA viruses using three-dimensional molecular modeling methods. These complimentary approaches will be used iteratively to arrive at a final model, and to design experimentally testable hypotheses.

描述（由申请人提供）：人类基因组计划和相关工作已经产生了大量的原始生物序列数据。然而，了解生物序列如何编码结构和功能信息仍然是一个基本的科学挑战。特别是，RNA病毒基因组编码的信息远远超出了它们的蛋白质编码作用，在病毒包装、复制和基因表达中发挥了序列内碱基配对的作用。因此，破译这些序列中编码的不同水平的信息对于充分理解RNA病毒的结构-功能关系至关重要。我们的目标是了解二级结构信息，表达为碱基对的选择性形成，是如何在大RNA病毒基因组中编码的。由于目前的预测方法不能可靠和有效地处理这些长序列，我们正在开发新的组合和计算方法来分析、预测和设计病毒RNA二级结构。我们的研究成果将是RNA折叠的离散数学模型和用于预测大RNA分子二级结构的高性能组合算法。我们的无包膜二十面体RNA病毒方法的成功将扩展到其他大RNA分子，并对许多RNA相关疾病的预防和治疗具有重要意义。我们的研究有三个具体目标。(1)我们将识别和评估区分编码重要结构和功能信息的碱基对与未确定的碱基对的RNA二级结构的特征。通过改进我们的RNA折叠组合模型，我们将区分其折叠遵循自然能量最小值的构型与编码良好确定且可能具有重要功能的亚结构的碱基对。(2)我们将通过开发从最小自由能子结构构建低能RNA二级结构所需的数学框架和计算技术来预测新的结构。通过利用并行和多核处理器，我们的新方法将以更高的精度预测大RNA分子二级结构中的重要功能基序。(3)我们将利用三维分子建模方法比较我们预测的二级结构与RNA病毒实验信息的相容性。这些互补的方法将反复使用，以达到最终的模型，并设计实验可测试的假设。