Computational prediction of RNA 3D structure using low resolution data and a new

描述（由申请人提供）：自然界已经开发出保护生物体免受外部入侵者（如病毒）或导致疾病（如癌症）的内部功能障碍的机制。其中一种安全机制是基因沉默，这是生物体发育和物种形成的核心。特别是基因沉默被广泛用于研究人类和模式生物的遗传学。越来越多的证据表明，癌症等疾病是多个基因失调的结果。正常的细胞反应是关闭这些基因。然而，在癌症等疾病的情况下，由于未知的原因，细胞无法科普这种情况，导致肿瘤的发展和进展。如果我们想知道为什么细胞在这些情况下不能自我防御，或者如果我们想有效地干预基因沉默以对抗这些疾病，我们需要更好地了解基因沉默机制。基因沉默是基于一类称为microRNA的核糖核酸。本研究的目的之一是研究这些microRNA的分子基础，特别是它们与靶基因的信使RNA结合的机制。准确预测microRNA的靶基因是目前基因沉默最重要的未解决问题之一。该领域的许多人都受到了过度依赖核糖核苷酸链二级结构的阻碍，这严重限制了当前计算模型的实用性和预测价值。核糖核苷酸链的功能主要取决于它们的三级结构。这就是为什么对于目前从事RNA和microRNA研究的所有实验室来说，能够快速准确地产生具有原子精度的RNA三级结构的建模程序变得至关重要。在这项研究中，我提出了一个RNA计算机建模框架的开发和应用，该框架建立在我过去20年开发的框架之上，以获得microRNA和靶向信使RNA的高分辨率三级结构。这些三级结构将帮助我们深入了解microRNA如何以及在何处结合其靶标，并准备生物测定来证实这些见解。我们的目标是将这些新的强大工具交给实验人员，因为快速可靠地产生RNA结构将对生物医学领域理解和实验这些最近发现的基因沉默途径的能力产生巨大的好处。 公共卫生相关性：越来越多的证据表明，RNA分子在保护我们免受病毒等外部入侵者或导致疾病的内部功能障碍的机制中起着至关重要的作用。确定RNA三维（3D）结构是研究RNA功能的关键，但使用物理方法成本高昂且费力。在许多方面，这种情况阻碍了RNA生物医学领域，这里的研究旨在为所有计算工具提供快速可靠的RNA 3D结构，为研究人员理解和操纵RNA的能力带来巨大的好处，包括我们自己的保护机制。

DESCRIPTION (provided by applicant): Nature has developed mechanisms to protect living organisms against external intruders such as viruses, or internal dysfunction leading to diseases, such as cancer. One such security mechanism is gene silencing, which is at the heart of organisms< development and speciation. In particular, gene silencing is widely used to study human and model organisms' genetics. Increasing evidence suggests that diseases such as cancer are the result of multiple gene deregulations. A normal cellular reaction would be to shutting down these genes. However, in the case of diseases such as cancer, for unknown reasons the cell is unable to cope with the situation, leading to tumor development and progression. We need to understand better gene silencing mechanisms if we wish to discover why the cell cannot defends itself in these situations, or if we want to intervene efficiently in gene silencing to fight these diseases. Gene silencing is based on a class of ribonucleic acids called microRNAs. One of the aims of this research is to study the molecular basis of these microRNAs, and in particular the mechanism by which they bind the messenger RNAs of targeted genes. Predicting accurately the targeted genes of microRNAs is among the most important unresolved questions of gene silencing at this moment. Many in the field are hindered by their over-reliance on the secondary structures of ribonucleotide chains, which severely limits the usefulness and predictive value of the current computational models. Ribonucleotide chains' function is rather due to their tertiary structure. This is why it is crucial for all laboratories currently engaged in research on RNAs and microRNAs that modeling programs capable of rapidly and accurately producing RNA tertiary structures with atomic precision become available. In this research, I propose the development and application of an RNA computer-modeling framework that builds upon the one I developed over the last 20 years to obtain high-resolution tertiary structures of microRNAs and targeted messenger RNAs. These tertiary structures will help us get insights into how and where microRNAs bind their targets, and prepare biological assays to confirm these insights. We aim to put these new powerful tools in the hands of the experimentalists, since rapidly and reliably producing RNA structures will yield immense benefits to the biomedical field's ability to understand and experiment with these recently discovered gene-silencing pathways. PUBLIC HEALTH RELEVANCE: Accumulating evidence shows that RNA molecules are crucial players in the mechanisms that protect us against external intruders such as viruses, or internal dysfunction that leads to diseases. Determining RNA three-dimensional (3D) structures is key to study RNA function, but is costly and laborious using physical methods. In many ways, this situation hinders the RNA biomedical field, and the research here aims at providing to all computational tools to rapidly and reliably producing RNA 3D structures, yielding immense benefits to the researchers' ability to understand and manipulate RNAs including those in our own protection mechanisms.