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.

通过与Shuo Gu合作，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的处理以及它们与正常细胞活性以及癌症等疾病的重要性。在这里，我们研究了Drosha靶向的茎环结构以及这些Drosha/RNA相互作用产生的microRNA同工型的类型。应用实验和计算方法来确定产生的同工型如何随RNA序列和结构的函数而变化。结果表明，靶向Drosha茎中弯曲，扭曲和/或柔性结构似乎有助于产生替代形式的microRNA。比较结构预测和实验结果并相关。具体而言，PRI-MIR-9-1的裂解，而不是PRI-MIR-9-2或PRI-MIR-9-3，产生了一种具有移动的种子序列的替代miR-9，该miR-9超出了其靶RNA的范围。有趣的是，对低度神经胶质瘤患者样品的分析表明，替代Pri-MiR-9具有肿瘤进展的潜力。其他PRI-MIRS还进行了研究，它们还产生了同工型，这是靶向RNA的形状和柔韧性的函数---在细胞形状中，预测为确定细胞内的RNA结构提供了新的细节。这些结果可能与更标准的形状技术有所不同，这些技术在产生潜在的结构预测时不会考虑细胞环境。我们根据从大肠杆菌数据库中获得的细胞形状测量中已知的神经网络（通过超级促进技术优化的神经网络（通过超帕纳体化技术）进行了计算预测的方法。鉴于序列，预测产生了相当准确的结果，具有皮尔逊系数，实验形状得分比热力学折叠更好。例如，我们预测了mRNA中翻译启动位点的形状得分。该方法表明核苷酸立即在翻译起始位点的上游是相对非结构化的。发现这些结果具有统计学意义，而相反，基于热力学折叠的结果却没有。这是第一次将计算方法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主题。这些图案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，这是一种大规模平行的遗传算法，包括预测转录过程中形成的RNA二级结构的功能，即作为RNA链延长的共转录折叠。就像它形成本地结构一样。这些结构又具有形成三级相互作用的能力，可以影响终止基序的形成。这些顺序二级结构基序也在3D中建模，进一步验证其潜在的形成和三级影响。这些结果可以指示用于终止控制的新范式。--与Stuart Le Grice合作的另一个项目涉及开发一种计算方法，以确定针对各种RNA结构基序的小分子的结合位点和亲和力。该项目的目的是帮助筛选小分子，以便在治疗上有益于靶向病毒RNA或引起基因的癌症。小分子最初是从通过使用小分子微阵列与实验筛选方法结合的集合得出的。目前所处的管道能够确定合理的准确性配体姿势以及结合口袋的构象。它似乎也能够区分不同配体的不同级别的结合亲和力。该管道目前正在应用于肝炎三角洲病毒的埃普西隆区域和三链PAN。我们能够分别与NMR和X射线结构数据达成很好的一致性，分别对这两个显着不同的站点。这种方法为潜在的治疗目的打开了小分子结合的计算预测之门，这是一个尚未广泛探索的研究领域。开发，RNA2Drawer。 RNA结构预测程序仍然不完美，许多子结构仍然通过手动勘探确定，这是在RNA结构图计划中最有效地进行的。开发了RNA2Drawer，以允许在结构更改和手动调整用户对布局的过程中，同时保持图形的几何形状（例如，椭圆形环，具有均匀堆叠的碱基对的茎）。此外，该程序允许注释，例如底座的着色和圆圈以及第三级相互作用的绘制（例如，伪单元）。 RNA2DRAWER还可以绘制常见的元素，例如可选扁平的最外面的循环和通过自动突出互补的子序列来帮助结构编辑，互补的子序列有助于发现潜在的新和替代配对，尤其是长期距离的三级配对，这在基因组中在生物学上至关重要许多RNA病毒。 RNA2DRAWER输出图纸作为png文件，或以pptx和svg文件的形式输出，使得绘图的每个对象（例如，基本，债券）都是单个pptx或svg对象，允许在Microsoft Powerpoint或Vector Powerpoint或vector图形编辑器中进行进一步操作。作为Adobe Illustrator。 - 同样，在与马里兰大学的安妮·西蒙（Anne Simon）合作，我们还在探索与真核系统中替代翻译模式有关的RNA图案。具体而言，我们一直专注于不包含5'帽位点并且缺乏多个尾巴，帽独立翻译元素（cite或pte）的那些，这不是正常的翻译模式，而是在几种RNA中发现的一种机制病毒。我们通过计算3D建模和实验验证（例如位点的诱变和形状）发现了元素，例如，在稳定伪病毒的carmovires中，稳定了超出伪型基序的结构，这些基序有助于翻译因子结合，从而模拟了5'盖盖位点。