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.

我们与 Shuo Gu 合作，研究了 Drosha 在细胞系统中的细微功能，因为它对于理解 microRNA 的加工以及它们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 靶向茎环结构以及由这些 Drosha/RNA 相互作用产生的 microRNA 亚型的类型。应用实验和计算方法来确定产生的亚型如何随着 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/或柔性结构似乎有利于替代形式的 microRNA 的产生。对结构预测和实验结果进行了比较和关联。具体来说，裂解 pri-miR-9-1（而非 pri-miR-9-2 或 pri-miR-9-3）产生了另一种 miR-9，其种子序列发生了改变，从而扩大了其靶 RNA 的范围。有趣的是，对低级别神经胶质瘤患者样本的分析表明，替代的 pri-miR-9 具有促进肿瘤进展的潜力。还研究了其他 pri-miR，它们还产生了作为目标 RNA 形状和灵活性函数的亚型——细胞内 SHAPE 预测为确定细胞内 RNA 结构提供了新的细节水平。这些结果可能与更标准的 SHAPE 技术不同，这些技术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从大肠杆菌数据库获得的已知细胞内形状测量值训练神经网络（通过超参数化技术优化）来计算预测细胞内形状的方法。给定序列的预测可以使用皮尔逊系数产生相当准确的结果，其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。该方法表明紧邻翻译起始位点上游的核苷酸相对非结构化。这些结果被发现具有统计显着性，而相比之下，基于热力学折叠的结果则不然。这是计算方法首次应用于基于机器学习的细胞内RNA结构的预测。 ---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。这些基序似乎超出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，能够预测转录过程中形成的 RNA 二级结构，即 RNA 链伸长时的共转录折叠。因为它确实形成了局部结构。这些结构反过来能够形成三级相互作用，从而影响终止基序的形成。这些连续的二级结构基序也在 3D 中建模，进一步验证它们的潜在形成和三级影响。这些结果可能表明了终止控制的新范例。---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。该项目的目标是帮助筛选小分子，以了解它们在靶向病毒 RNA 或致癌基因方面具有治疗益处的潜力。小分子最初源自通过结合使用小分子微阵列的实验筛选方法发现的组。目前的流程能够以合理的精度水平确定配体姿势以及结合袋的构象。它似乎还能够区分不同配体的不同水平的结合亲和力。该管道目前正应用于丁型肝炎病毒的ε区域和三链PAN。我们能够分别与这两个显着不同位点的 NMR 和 X 射线结构数据获得良好的一致性。这种方法为计算预测结合 RNA 基序的小分子以用于潜在的治疗目的打开了大门，这是一个尚未得到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，许多子结构仍然通过手动探索来识别，而手动探索在 RNA 结构绘图程序中进行的效率最高。 RNA2Drawer 的开发是为了允许图形结构编辑，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，椭圆体环、具有均匀堆叠碱基对的茎）。此外，该程序还允许进行注释，例如碱基的着色和圆圈以及三级相互作用的绘制（例如，假结）。 RNA2Drawer 还可以绘制常用的所需元素，例如可选的扁平最外层环，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的和替代的配对，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重要的生物学意义。 RNA2Drawer 将绘图输出为 PNG 文件或 PPTX 和 SVG 文件，这样绘图的每个对象（例如碱基、键）都是单独的 PPTX 或 SVG 对象，从而允许在 Microsoft PowerPoint 或矢量图形编辑器（如 Adob​​e Illustrator）中进行进一步操作。 ——我们还与马里兰大学的 Anne Simon 合作，一直在探索真核系统中替代翻译模式所涉及的 RNA 基序。具体来说，我们一直专注于那些不包含 5'帽位点且缺乏聚 A 尾、帽独立翻译元件（CITE 或 PTE）的病毒，这不是正常的翻译模式，而是在几种 RNA 病毒中发现的机制。我们通过计算 3D 建模和实验验证（例如定点诱变和 SHAPE）发现了一些元素，这些元素似乎在 Carmoviruses 中很常见，可以稳定假结基序之外的结构，有利于翻译因子结合，从而模拟 5'帽位点。