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、生物技术和医学中起着关键作用。RNA折叠成 复杂的结构，这往往驱动其功能，因此确定RNA结构是生物学的基础 和生物技术。基于计算化学的二级结构建模（TSSM）是一种流行的， 低成本，快速的结构预测方法，使转录组范围的结构-功能 研究和大规模基于结构的合成RNA文库筛选。然而，最近的证据表明， 转录后化学核苷酸修饰的多样性另外对 局部和/或全局结构，以最终调节RNA的稳定性、表达或调节功能。等 修饰广泛存在于所有的生命领域，代表了一个新的和知之甚少的基因层， 调节，这与疾病有关。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总的来说，丰富的自然变化和 新的人工基因的开发，对它们的机制越来越感兴趣，以及它们在RNA医学中的中心地位 强调迫切需要快速准确地确定具有修饰核苷酸的RNA的结构。 然而，由于缺乏参数进行估计，TSSM方法不能解释修改的影响 它们的折叠稳定性。他们依赖于功能丰富的特纳最近邻（NN）热力学模型， 是由294个自由能变化值从802个昂贵和费力的标准基地得出参数化 UV熔融实验。鉴于修改的多样性和迅速扩大的池，重复是不切实际的， 每种类型的实验。这个建议的前提是，神经网络参数可以学习更多 有效地从替代实验，这是负担得起的，广泛访问，和高通量。 具体来说，下一代测序已经将RNA结构探测（SP）大规模地转变为常规技术。 平行实验，其报告关于局部核苷酸动力学的结构信息。SP广泛用于 从全基因组研究中深入了解RNA的结构和功能，并限制TSSM算法， 改善他们的预测。然而，与解链测定不同，RNA折叠稳定性与SP之间的关系 测量是高度非平凡的，因此从SP数据恢复参数的问题是困难的。 该提案的目标是开发新的算法和软件来估计NN参数， 高吞吐量SP数据。我们将设计统计推断方法，从折叠协调信息 算法和SP实验，并将其应用于未修饰和修饰的RNA的数据，以估计新的 修饰的核苷酸的参数。由于SP数据和折叠热力学之间的联系是复杂的， 此外，还没有探索从自然电位数据拟合特纳参数的能力，我们将评估 可行性，准确性，性能和计算效率的开发方法。验证 工作将包括与实验得出的值进行比较，并评估对保留数据的预测。