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RNA在细胞功能，生物技术和医学中起关键作用。 RNA折叠成 错综复杂的结构通常可以推动其功能，因此确定RNA结构是生物学的基础 和生物技术。基于计算热力学的二级结构建模（TSSM）是一个流行的， 低成本和快速的结构预测方法，这已启用了整个转录组结构功能 合成RNA文库的研究和基于结构的大规模筛选。但是，最近的证据表明 转录后化学核苷酸修饰的多样性以及对 局部和/或全球结构，最终调节RNA的稳定性，表达或调节功能。这样的 修改在所有生命领域中都广泛，代表了一个新的且知之甚少的基因层 调节，这已在疾病中暗示。此外，它们通常会引入RNA药物 作为逃避先天免疫反应的一种手段。综上 开发新颖的人造，对其机制的兴趣日益增强以及对RNA医学的中心地位 强调需要快速准确地确定RNA结构的压力。 但是，由于缺乏参数无法估算，TSSM方法无法说明修改的影响 他们的折叠稳定性。他们依靠功能丰富的特纳最近邻居（NN）热力学模型，该模型 由294个自由化变更值参数化，来自802的规范基础，昂贵且费力 紫外线熔化实验。鉴于修改的多样化和快速扩展，重复是不切实际的 每种类型的实验。该提议的前提是可以学习NN参数 有效地从负担得起的，广泛访问且吞吐量高的替代实验中有效。 具体而言，下一代测序已将RNA结构探测（SP）转化为常规 平行实验，报告有关局部核苷酸动力学的结构信息。 SP广泛习惯 从全基因组研究中获得对RNA结构和功能的见解，并将TSSM算法限制为 改善他们的预测。但是，与熔化测定不同，RNA折叠稳定性与SP之间的关系 测量值高度不足，因此很难从SP数据中恢复参数的问题。 该建议的目的是开发新颖的算法和软件，以估算NN参数 高通量SP数据。我们将设计统计推理方法，以调和折叠信息 算法和SP实验，并将其应用于数据，以估算未经修改和修改的RNA 修饰核苷酸的参数。由于SP数据与折叠热力学之间的联系很复杂，并且 此外，尚未探索从SP数据中拟合Turner参数的能力，我们将评估 开发方法的可行性，准确性，性能和计算效率。验证 努力将包括与实验得出的值进行比较，并评估预测数据的预测。