Computational Prediction of RNA Viral Genome Structures

一些致病病毒的基因组是由RNA组成的，RNA可以直接参与致病过程。 产生对病毒繁殖和感染至关重要的蛋白质。这些快速突变的RNA病毒显示出 不同种属间交叉感染的高发生率（Plant等人，2005年）。是可行 对病毒基因组进行计算分析，获得有用的信息，为病毒的研究提供线索。 病毒的起源、自然宿主和进化，有助于理解免疫反应 研究这些病毒和病毒性疾病的发病机制，促进抗病毒药物的开发。我们 最近报道，冠状病毒RNA基因组中具有统计学显著簇的区域， 回文与茎环或假结的存在有关（Chew等人，2004）。这些RNA 已经提出结构负责基因表达期间的移码机制， 其中两种不同的蛋白质可以在同一区域产生，只要将阅读框移动一个， 基地我们假设，通过利用紧密反演的非随机簇与 RNA分子中的茎环和假结结构，一种预测 RNA病毒基因组的二级结构可以使用当前的异构网格计算来开发 技术.我们提出了一个为期四年的项目来评估这一假设，重点是冠状病毒和 流感病毒的具体目标是：（1）建立一种基于遗传算法的RNA片段定位算法 包含紧密反转的非随机簇。(2)开发一种切割病毒基因组的策略 序列分成长度不超过200个碱基的片段。(3)构建RNA的软件原型 利用网格计算技术进行二级结构预测。(4)实施用户设施， 并预测冠状病毒和流感病毒的基因组结构。在这个项目中，我们 目标是在网格上产生RNA二级结构预测系统的原型， 异构的分布式计算机。该软件将通过一个门户网站向公众开放。我们 长期目标是在网格计算环境中开发一套开源计算工具， 准确预测RNA病毒的基因组结构和动力学及其与细胞RNA的相互作用。 这将为病毒学家设计研究RNA的微调实验提供信息 病毒及其与宿主的致病相互作用，特别是在抗击新的传染性病毒疾病的时间有限的情况下。

The genomes of some pathogenic viruses are made up of RNA that can be involved directly in the production of proteins crucial to viral reproduction and infection. These fast-mutating RNA viruses showed a high incidence of cross-infections among different species by variant forms (Plant et al. 2005). It is feasible to perform computational analyses on the viral genome and obtain useful information which give clues to the origin, natural reservoir, and evolution of the virus, contributing to the understanding of the immune response to these viruses and the pathogenesis of viral diseases and facilitate the development of antiviral drugs. We have recently reported that regions in coronavirus RNA genomes with statistically significant clusters of palindromes are associated with the presence of stem-loops or pseudoknots (Chew et al 2004). These RNA structures have been suggested to be responsible for frame-shifting mechanisms during gene expression, where two different proteins can be produced in the same region just by shifting the reading frame by one base. We hypothesize that by exploiting the correlation between nonrandom clusters of close inversions with stem-loop and pseudoknot structures in RNA molecules, an efficient and utilitarian tool for predicting secondary structures on RNA viral genomes can be developed using current heterogeneous Grid Computing technology. We propose a four-year project to evaluate this hypothesis focusing on coronaviruses and influenza viruses with specific aims to: (1) Establish a statistics-based algorithm to locate RNA segment containing nonrandom clusters of close inversions. (2) Develop a strategy for cutting of the viral genome sequences into segments of length no greater than 200 bases. (3) Construct a software prototype for RNA secondary structure prediction using Grid Computing technology. (4) Implement user facilities for the software and predict genome structures in coronaviruses and Influenza viruses. For this project, our objective is to produce the prototype of an RNA secondary structure prediction system on a grid of heterogeneous, distributed computers. The software will be publicly accessible through a web portal. Our long-term goal is to develop a set of open-source computational tools in a Grid Computing environment to accurately predict genome structures and dynamics in RNA viruses and their interactions with cellular RNA. This will provide information to be used by virologists to design finely tuned experiments to study RNA viruses and their pathogenic interactions with their hosts, especially when time is limiting in combating new infectious viral diseases.