Prediction of nearest neighbor parameters for folding RNAs with modified nucleotides

预测具有修饰核苷酸的折叠 RNA 的最近邻参数

• 批准号: 10576175
• 负责人: Sharon Aviran
• 金额: $ 20.41万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10576175
• 关键词: Academy; Acceleration; Accounting; Adoption; Agreement; Algorithms; Area; Biological Assay; Biology; Biotechnology; Cell physiology; Cells; Chemicals; Collaborations; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Dedications; Development; Disease; Engineering; Free Energy; Future; Gene Expression Regulation; Genes; Goals; Half-Life; Infrastructure; Innate Immune Response; Investments; Learning; Life; Link; Measurement; Medicine; Messenger RNA; Methods; Modeling; Modification; Nucleotides; Optics; Parameter Estimation; Performance; Pharmaceutical Preparations; Play; Polishes; Probability; Publishing; RNA; RNA Folding; RNA Splicing; RNA Stability; RNA library; RNA vaccine; Reporting; Role; Science; Scientist; Specificity; Structure; Techniques; Therapeutic; Thermodynamics; Time; Transcript; Translations; Untranslated RNA; Uridine; Validation; Work; base; cost; data mining; data structure; design; experimental study; genome-wide analysis; improved; insight; interest; melting; method development; next generation sequencing; novel; posttranscriptional; rapid technique; rational design; tool; transcriptome

Natural and synthetic RNAs play key roles in cellular function, biotechnology, and medicine. RNAs fold into intricate structures, which often drive their functions, thus determining RNA structure is fundamental to biology and biotechnology. Computational thermodynamics-based secondary structure modeling (TSSM) is a popular, low-cost, and rapid approach to structure prediction, which has enabled transcriptome-wide structure-function studies and massive structure-based screens of synthetic RNA libraries. However, recent evidence suggests that a diversity of post-transcriptional chemical nucleotide modifications additionally exert profound impact on local and/or global structure, to ultimately modulate the RNA’s stability, expression, or regulatory function. Such modifications are widespread in all life domains and represent a new and poorly understood layer of gene regulation, which has been implicated in disease. Moreover, they are routinely introduced into RNA medicines as a means of evading the innate immune response. Taken together, the wealth of natural modifications and development of novel artificial ones, the growing interest in their mechanism, and their centrality to RNA medicine underscore a pressing need to determine structures of RNAs with modified nucleotides rapidly and accurately. However, TSSM methods cannot account for the effects of modifications due to a lack of parameters to estimate their folding stabilities. They rely on the feature-rich Turner nearest-neighbor (NN) thermodynamic model, which is parameterized by 294 free-energy change values derived for canonical bases from 802 costly and laborious UV melting experiments. Given the diverse and rapidly expanding pool of modifications, it is impractical to repeat such experiments for each type. The premise of this proposal is that NN parameters can be learned more efficiently from alternative experiments, which are affordable, widely accessible, and high throughput. Specifically, next-generation sequencing has transformed RNA Structure Probing (SP) into a routine massively parallel experiment, which reports structural information about local nucleotide dynamics. SP is widely used to gain insights into RNA structure and function from genome-wide studies and to constrain TSSM algorithms to improve their predictions. However, unlike melting assays, the relationship between RNA folding stability and SP measurements is highly nontrivial, and thus the problem of recovering the parameters from SP data is difficult. The goal of this proposal is to develop novel algorithms and software to estimate NN parameters from high-throughput SP data. We will design statistical inference methods that reconcile information from folding algorithms and SP experiments and apply them to data for unmodified and modified RNAs to estimate new parameters for modified nucleotides. As the link between SP data and folding thermodynamics is complex, and furthermore, the ability to fit the Turner parameters from SP data has not been explored, we will assess the feasibility, accuracy, performance, and computational efficiency of the developed methods. Validation efforts will include comparing to experimentally derived values and evaluating predictions over held-out data.

天然和合成的 RNA 在细胞功能、生物技术和医学中发挥着关键作用。 复杂的结构通常驱动其功能，因此确定 RNA 结构是生物学的基础 基于计算热力学的二级结构建模（TSSM）是一种流行的、 低成本、快速的结构预测方法，使得转录组范围内的结构功能成为可能 然而，最近的证据表明。 转录后化学核苷酸修饰的多样性也对 局部和/或全局结构，最终调节 RNA 的稳定性、表达或调节功能。 修饰广泛存在于所有生命领域，代表了一个新的、人们知之甚少的基因层 此外，它们通常被引入 RNA 药物中。 作为逃避先天免疫反应的手段，丰富的自然修饰和 新型人工RNA的开发、对其机制的日益浓厚的兴趣以及它们在RNA医学中的中心地位 强调了快速准确地确定具有修饰核苷酸的 RNA 结构的迫切需要。 然而，由于缺乏参数来估计，TSSM 方法无法解释修改的影响 它们的折叠稳定性依赖于特征丰富的特纳最近邻 (NN) 热力学模型，该模型 由 294 个自由能变化值参数化，这些自由能变化值源自 802 个昂贵且费力的规范基 考虑到修饰的多样性和快速扩展，重复进行是不切实际的。 对每个类型进行这样的实验，这个提案的前提是可以学到更多的NN参数。 有效地利用替代实验，这些实验价格低廉、可广泛使用且通量高。 具体来说，新一代测序已将 RNA 结构探测 (SP) 大规模转变为常规 平行实验报告了局部核苷酸动力学的结构信息，被广泛用于。 从全基因组研究中深入了解 RNA 结构和功能，并限制 TSSM 算法 然而，各种熔解测定，RNA 折叠稳定性和 SP 之间的关系得到了改善。 测量非常重要，因此从 SP 数据恢复参数的问题很困难。 该提案的目标是开发新颖的算法和软件来估计神经网络参数 我们将设计统计推理方法来协调折叠信息。 算法和 SP 实验，并将其应用于未修饰和修饰 RNA 的数据以估计新的 由于 SP 数据和折叠热力学之间的联系很复杂，并且 此外，尚未探索从 SP 数据拟合特纳参数的能力，我们将评估 所开发方法的可行性、准确性、性能和计算效率。 将包括与实验得出的努力值进行比较以及评估对保留数据的预测。