Computational Approaches for RNA Structure and Function Determination

RNA 结构和功能测定的计算方法

• 批准号: 10262024
• 负责人: Bruce Shapiro
• 金额: $ 46.04万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10262024
• 关键词: 3-Dimensional; Affinity; Agreement; Algorithms; Base Pairing; Binding; Binding Sites; Biological; Biological Assay; Biology; Cancer Etiology; Carmovirus; Carrier Proteins; Catalysis; Categories; Cell Shape; Cell physiology; Cells; Characteristics; Collaborations; Computer software; Computing Methodologies; Databases; Development; Disease; Elements; Environment; Escherichia coli; Gene Silencing; Genes; Genetic Programming; Genetic Transcription; Genome; Geometry; Glioma; Goals; Hepatitis Delta Virus; Higher Order Chromatin Structure; Individual; Knowledge; Laboratories; Ligands; Machine Learning; Malignant Neoplasms; Manuals; Maryland; Measurement; Membrane; Messenger RNA; Methodology; Methods; MicroRNAs; Modeling; Molecular Conformation; Nucleotides; Output; Pathologic; Pathway interactions; Patients; Poly(A) Tail; Production; Protein Isoforms; RNA; RNA Binding; RNA Sequences; RNA Viruses; RNA analysis; Research; Roentgen Rays; Sampling; Seeds; Shapes; Site; Site-Directed Mutagenesis; Structure; Structure-Activity Relationship; System; Techniques; Therapeutic; Thermodynamics; Time; Training; Transcriptional Regulation; Translations; Universities; Viral; Viral Cancer; Virus; base; cancer cell; drug development; flexibility; neural network; programs; screening; small molecule; stem; structured data; three dimensional structure; three-dimensional modeling; transcription termination; translation factor; tumor progression; vector; viral RNA; web site

In collaboration with Shuo Gu we studied the nuanced functionalities of Drosha in cellular systems due to its importance for understanding the processing of microRNAs and how they relate to normal cellular activity as well as diseases such as cancer. Here we studied Drosha targeted stem-loop structures and the types of microRNA isoforms that were produced by these Drosha/RNA interactions. Experimental and computational approaches were applied to determine how the produced isoforms varied as a function of the RNA sequence and structure. Results indicate that bent, distorted and/or flexible structures in the targeted Drosha stem seem to facilitate the production of alternate forms of microRNAs. Structural predictions and experimental results were compared and correlated. Specifically, cleavage of pri-miR-9-1, but not pri-miR-9-2 or pri-miR-9-3, generated an alternative miR-9 with a shifted seed sequence that exapands the scope of its target RNAs. Interestingly, analysis of low-grade glioma patient samples indicate that alternative pri-miR-9 has a potential for tumor progression.Other pri-miRs were also studied and they also produced isoforms as a function of the targeted RNA's shape and flexibility --- In cell SHAPE prediction provides a new level of detail for determining RNA structure within cells. These results may vary from the more standard SHAPE techniques that do not take the cellular environment into account when producing potential structural predictions. We developed a method for the computational prediction of in cell SHAPE by training a neural network (which was optimized by hyper-paramaterization techniques) based on known in cell SHAPE measurements obtained from an E. coli database. Predictions, given a sequence, produce reasonably accurate results with a Pearson coefficient with experimental shape scores better than thermodynamic folding. As an example, we predicted the SHAPE scores around translation start sites in mRNAs. The method indicates that nucleotides immediately upstream of the translation start sites to be relatively unstructured. These results were found to be statistically significant, while in contrast, results based on thermodynamic folding were not. This is the first time that computational methods have been applied to the prediction of RNA structure within cells based on machine learning. ---In another project in collaboration with Mikhail Kashlev we determining motifs that during transcription are responsible for transcriptional termination. These motifs appear to go beyond the standard RNA hairpin that is normally involved in termination. The approach involves the use of MPGAFOLD, a massively parallel genetic algorithm the includes capabilities to predict RNA secondary structures that form during transcription, i.e. co-transcriptional folding as the RNA strand elongates. As it does local structures form. These structures in turn have the ability to form tertiary interactions which can influence the formation of termination motifs. These sequential secondary structure motifs are also modeled in 3D further verifying their potential formation and tertiary influence. A new paradigm for termination control may be indicated by these results.---Another project in collaboration with Stuart Le Grice involves the development of a computational approach to determined binding sites and affinities of small molecules targeting various RNA structural motifs. The goal of this project is to aid in the screening of small molecules for their potential to be therapeutically beneficial in targeting viral RNAs or cancer causing genes. The small molecules are initially derived from sets found by binding to experimental screening methods using small molecule microarrays. The pipeline as it currently stands is able to determine to a reasonable level of accuracy ligand poses as well as the conformation of the binding pockets. It also seem able to discriminate between different levels of binding affinities for different ligands. The pipe-line is currently being applied to the epsilon region of the hepatitis delta virus and to the triple stranded PAN. We are able to get good agreement with NMR and X-ray structure data respectively to these two significantly different sites. This methodology is opening the door to computational prediction of small molecule binding the RNA motifs for potential therapeutics purposes, a domain of research that has not been extensively explored.---In collaboration with Anne Simon, University of Maryland a new RNA structure drawing algorithm was developed, RNA2Drawer. RNA structure prediction programs remain imperfect and many substructures are still identified by manual exploration, which is most efficiently conducted within an RNA structure drawing program. RNA2Drawer was developed to allow for graphical structure editing while maintaining the geometry of a drawing (e.g., ellipsoid loops, stems with evenly stacked base pairs) throughout structural changes and manual adjustments to the layout by the user. In addition, the program allows for annotations such as colouring and circling of bases and drawing of tertiary interactions (e.g., pseudoknots). RNA2Drawer can also draw commonly desired elements such as an optionally flattened outermost loop and assists structure editing by automatically highlighting complementary subsequences, which facilitates the discovery of potentially new and alternative pairings, particularly tertiary pairings over long-distances, which are biologically critical in the genomes of many RNA viruses. RNA2Drawer outputs drawings either as PNG files, or as PPTX and SVG files, such that every object of a drawing (e.g., bases, bonds) is an individual PPTX or SVG object, allowing for further manipulation in Microsoft PowerPoint or a vector graphics editor such as Adobe Illustrator. --Also in collaboration with Anne Simon, University of Maryland, we have been exploring the RNA motifs that are involved in alternative modes of translation in eukaryotic systems. Specifically we have been concentrating on those that do not contain 5' cap sites and lack a poly A tail, cap Independent translation elements (CITE, or PTE), which is not the normal mode of translation, but is a mechanism found in several RNA viruses. We have found elements, via computational 3D modeling and experimental verification such as site directed mutagenesis and SHAPE, that seem to be common for example, in Carmoviruses that stabilize structures beyond pseudoknot motifs that are conducive for translation factor binding and thus mimic 5' cap sites.

在与Shuo Gu的合作中，我们研究了Drosha在细胞系统中的细微功能，因为它对于理解microrna的加工及其与正常细胞活动以及癌症等疾病的关系非常重要。在这里，我们研究了Drosha靶向茎环结构和这些Drosha/RNA相互作用产生的microRNA亚型的类型。采用实验和计算方法来确定产生的异构体如何随RNA序列和结构的变化而变化。结果表明，目标Drosha茎中的弯曲、扭曲和/或柔性结构似乎有助于产生替代形式的microrna。对结构预测结果与实验结果进行了比较和关联。具体来说，对pri-miR-9-1的切割，而不是对pri-miR-9-2或pri-miR-9-3的切割，产生了一个具有移位种子序列的替代miR-9，扩大了其靶rna的范围。有趣的是，对低级别胶质瘤患者样本的分析表明，替代的pri-miR-9具有肿瘤进展的潜力。其他的pri- mir也被研究过，它们也产生了同工异构体，作为目标RNA的形状和灵活性的功能——细胞内形状预测为确定细胞内RNA结构提供了一个新的细节水平。这些结果可能与更标准的SHAPE技术不同，后者在产生潜在的结构预测时不考虑细胞环境。我们开发了一种基于大肠杆菌数据库中已知的细胞形状测量数据，通过训练神经网络（通过超参数化技术进行优化）来计算预测细胞内形状的方法。在给定序列的情况下，预测可以产生相当准确的结果，皮尔逊系数的实验形状得分优于热力学折叠。例如，我们预测了mrna翻译起始位点周围的SHAPE分数。该方法表明，翻译起始位点上游的核苷酸相对非结构化。这些结果具有统计学意义，而基于热力学折叠的结果则没有统计学意义。这是首次将计算方法应用于基于机器学习的细胞内RNA结构预测。在与Mikhail Kashlev合作的另一个项目中，我们确定了转录过程中负责转录终止的基序。这些基序似乎超越了通常参与终止的标准RNA发夹。该方法涉及使用MPGAFOLD，这是一种大规模并行遗传算法，包括预测转录过程中形成的RNA二级结构的能力，即RNA链延长时的共转录折叠。就像局部结构形成一样。这些结构反过来又具有形成三级相互作用的能力，这可以影响终止基序的形成。这些顺序的二级结构基序也在三维中建模，进一步验证了它们的潜在形成和三级影响。这些结果可能表明一种新的终止控制范例。-与Stuart Le Grice合作的另一个项目涉及开发一种计算方法来确定针对各种RNA结构基序的小分子的结合位点和亲和力。这个项目的目标是帮助筛选小分子，因为它们有可能在治疗上对靶向病毒rna或致癌基因有益。这些小分子最初是通过结合小分子微阵列的实验筛选方法得到的。管道，因为它目前的立场是能够确定一个合理的精度水平配体的姿势，以及结合口袋的构象。它似乎还能区分不同配体的不同结合亲和力。该管线目前正在应用于丁型肝炎病毒的epsilon区和三链PAN。我们能够分别与这两个显著不同的位点的核磁共振和x射线结构数据得到很好的一致性。这种方法为潜在治疗目的的小分子结合RNA基序的计算预测打开了大门，这是一个尚未被广泛探索的研究领域。——与马里兰大学Anne Simon合作开发了一种新的RNA结构绘制算法RNA2Drawer。RNA结构预测程序仍然不完善，许多亚结构仍然通过人工探索来识别，这在RNA结构绘制程序中是最有效的。RNA2Drawer的开发允许图形结构编辑，同时在结构变化和用户手动调整布局的过程中保持绘图的几何形状（例如，椭球环，具有均匀堆叠碱基对的茎）。此外，该程序允许注释，如着色和基地的圆圈和绘制三级相互作用（例如，伪结）。RNA2Drawer还可以绘制通常需要的元素，如可选的最外层环平坦化，并通过自动突出显示互补子序列来辅助结构编辑，这有助于发现潜在的新配对和替代配对，特别是长距离的三级配对，这在许多RNA病毒的基因组中具有生物学关键作用。RNA2Drawer以PNG文件或PPTX和SVG文件输出绘图，这样绘图的每个对象（例如，碱基，键）都是一个单独的PPTX或SVG对象，允许在Microsoft PowerPoint或矢量图形编辑器（如Adobe Illustrator）中进一步操作。——我们还与马里兰大学的Anne Simon合作，一直在探索真核生物系统中参与替代翻译模式的RNA基序。具体来说，我们一直专注于那些不包含5'帽位点和缺乏poly - a尾的病毒，帽独立翻译元件（CITE或PTE），这不是正常的翻译模式，但在几种RNA病毒中发现了一种机制。通过计算3D建模和实验验证，我们发现了一些元素，如位点定向诱变和SHAPE，这些元素似乎在carmovirus中很常见，例如，它们稳定了假结基序以外的结构，有利于翻译因子结合，从而模拟了5'帽位点。