Computational analysis and modeling of ribonucleic acid structure, function, and dynamics

背景 RNA在细胞中起着核心作用。它参与了所有的细胞过程，揭示了原始的到达和普遍的生化特性。它的顺序性使其成为存储遗传信息的简单设备。它的结构允许保护和暴露其序列，并提供相互作用表面，促进其与其他分子的相互作用，以及掩埋的结合部位，促进离子捕获和催化。没有其他大分子允许如此大的结构和功能多样性。我的研究项目导致了从序列和结构数据预测RNA结构的计算机模型和算法。几十年来，来自RNA结构和生物学领域的几个研究小组一直在使用我们的软件。我们的方法与其他方法不同，因为我们使用整合领域知识的符号计算，从而定义了一种非常适合科学家的智能工具类别。我们的工具运行于高级物理模型规范，解释实验数据并以定性的术语表述结果。 理论基础和目标 RNA结构预测的目标之一是识别给定RNA序列的天然状态。预测算法提出了由不同的碱基对集合定义的许多2D构象状态。基于从热力学或统计参数获得的自由能，使用目标函数来对每个状态进行评分。我们研究计划的全球目标是预测RNA的结构和功能。我们的方法是计算性的，因为我们实现、基准和验证计算工具，以从序列数据分析和建模RNA结构。我们通过挖掘结构动力学来解释生理条件，以确定基于两个假设的功能基序。首先，在一系列同源序列中发现的序列变异正在留下与它们进化的生理条件有关的进化痕迹。其次，这些痕迹转化为结构元素或主题，尽管很少能以最佳方式揭示结构和功能决定因素。我们的具体目标是： 目标1：改进结构预测？目标2：表征函数行列式 目标2：刻画函数行列式 方法 这项研究涉及计算工作，包括建模(开发RNA动力学模型并通过计算机编程实现它)、基准测试以及预测，这些工作与实验验证一起导致了新的发现。 意义 我们正处于一个时代的开始，在这个时代，RNA将成为环境科学、生物技术和医学中众多迷人的新应用的核心。这项研究计划的结果将为科学界提供计算工具和数据，以无与伦比的精度分析和模拟RNA的结构、功能和动力学。

Background RNA plays a central role in cells. It is involved in all cellular processes, revealing a primordial arrival and universal biochemical properties. Its sequential nature makes it a simple device for storing genetic information. Its structure allows both to protect and expose its sequence and to present interaction surfaces facilitating its interaction with other molecules as well as buried binding sites that promote ion capture and catalysis. No other macromolecule allows for such a large structural and functional diversity. My research program has led to computer models and algorithms to predict RNA structure from sequence and structural data. Several research groups from the fields of RNA structure and biology have been using our software for decades. Our approach differs from that of others because we use symbolic calculations that integrate domain knowledge, thus defining a category of intelligent tools well suited to scientists. Our tools run from high-level physical model specifications, and interpret experimental data and formulate results in qualitative terms. Rationale & Aims One of the goals of RNA structure prediction is to identify the native state of a given RNA sequence. Prediction algorithms propose many 2D conformational states defined by different sets of base pairs. An objective function is used to score each state based on free energy derived from either thermodynamics or statistical parameters. The global aim of our research program is to predict RNA structure and function. Our approach is computational, as we implement, benchmark, and validate computational tools to analyse and model RNA structure from sequence data. We account for the physiological conditions by digging structural dynamics to identify functional motifs based on two hypotheses. First, sequence variations found in a family of homologous sequences are leaving evolutionary traces that relate to the physiological conditions in which they evolved. Second, these traces translate into structural elements, or motifs, which although rarely optimal reveal structural and functional determinants. Our specific goals are to: Aim 1: Improve structure prediction?Aim 2: Characterize functional determinants Aim 2: Characterize functional determinants Approach This research involves computational work that includes modeling (developing the model of RNA dynamics and implementing it by computer programming), benchmarking, as well as making predictions, which, along with experimental validation, lead to new discoveries. Significance We are at the beginning of an era where RNA will be at the heart of numerous new fascinating applications in environmental science, biotechnology and medicine. The results of this research program will provide the scientific community with computational tools and data to analyze and model RNA structure, function, and dynamics with unequaled precision.