DE NOVO RNA STRUCTURES WITH EXPERIMENTAL VALIDATION

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 过去的十年揭示了RNA的广泛功能，包括信息存储、催化和细胞调节。然而，对RNA结构的计算方法已经落后于那些涉及蛋白质的方法。我们提出了两个计算项目，将使我们能够产生高质量的RNA结构模型。我们开发了一个高分辨率的RNA力场，该力场已经包含在Rosetta建模软件中[1]。我们的第一个目标是利用这个力场开发和改进新的构象搜索算法。然而，要验证这些程序，需要对广泛的现有结构进行培训和基准测试。只有扩大到更大的超级计算环境，这样的基准才有可能。其次，我们计划使用CUDA优化的分子动力学程序(OpenMM)对小RNA系统的动力学进行建模。通过GPGPU技术实现的性能提升(5倍-100倍)将允许在原子分辨率下模拟RNA折叠。过去，此类计算仅限于Folding@Home等分布式计算环境。TeraGrid基础设施提供的资源将使我们能够在范围和速度上扩大我们的研究。我们预计，TeraGrid资源提供的快速周转时间将允许理论和实验之间更动态的关系：定量预测将在我们的实验室通过实现单个残基解析的高通量结构映射方法快速验证[2]。[1]Das，R.和Baker，D.(2007年)“类天然核糖核酸三级结构的自动从头预测”，美国国家科学院学报，第104期，14644“14669。[2]Das，R.，Kudaravalli，M.，Jonikas，M.，Laederach，A.，Fong，R.，Schwans，J.P.，Baker，D.，PicCirilli，J.A.，Altman，R.B.，和Herschlag，D.(2007)《通过高通量接触映射对天然和部分折叠的RNA的结构推断》，美国国家科学院院刊，105,4144-4149。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The past decade has revealed the breadth of RNA's functions, which include information storage, catalysis, and cellular regulation. However, computational approaches to RNA structure have lagged behind those involving proteins. We propose two computational projects that will allow us to produce quality models of RNA structure. We have developed a high resolution RNA force field that has been included in the Rosetta modeling software [1]. Our first goal is to develop and refine novel conformational search algorithms using this force field. However, to validate such procedures requires training and benchmarking on a wide array of existing structures. Such a benchmark will be possible only by scaling up to a larger supercomputing environment. Secondly, we plan to model the dynamics of small RNA systems using CUDA-optimized molecular dynamics code (OpenMM). The performance gains (5x-100x) enabled by GPGPU technology will allow the simulation of RNA folding at atomic resolution. In the past, such computations have been limited to distributed computing environments such as Folding@Home. The resources afforded by the Teragrid infrastructure will allow us to scale up our research both in scope and in speed. We expect that the quick turnaround time afforded by TeraGrid resources will allow a more dynamic relationship between theory and experiment: quantitative predictions will be quickly verified in our lab by high throughput structure mapping approaches that achieve single residue resolution [2]. [1] Das, R. and Baker, D. (2007) "Automated de novo prediction of native-like RNA tertiary structures", Proceedings of the National Academy of Sciences U.S.A. 104, 14644"14669. [2] Das, R., Kudaravalli, M., Jonikas, M., Laederach, A., Fong, R., Schwans, J.P., Baker, D., Piccirilli, J.A., Altman, R.B., and Herschlag, D. (2007) "Structural inference of native and partially folded RNA by high throughput contact mapping", Proceedings of the National Academy of Sciences U.S.A 105, 4144-4149.