Energy parameters and novel algorithms for an extended nearest neighbor energy model of RNA

RNA扩展最近邻能量模型的能量参数和新算法

• 批准号: 1016618
• 负责人: Peter Clote
• 金额: $ 40万
• 依托单位: Boston College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016618&HistoricalAwards=false
• 关键词: Energy; parameters; novel; algorithms; extended

Thermodynamics-based ab initio RNA secondary structure algorithms are used to detect microRNAs, targets of microRNAs, non-coding RNA genes, temperature-dependent riboregulators, selenoproteins, ribosomal frameshift locations, RNA-protein binding sites, etc. The importance and ubiquity of applications of RNA thermodynamics-based algorithms cannot be overemphasized; indeed, such applications include RNA design for novel cancer therapies and for synthetic biology. Free energy parameters of the nearest neighbor (NN) model, also called the Turner model, form the foundation for essentially all current thermodynamics-based RNA algorithms. Dynamic programming minimum free energy structure computation using Zuker?s algorithm yields an accuracy in base pair prediction of around 70%. In this grant, we intend to improve base pair prediction accuracy by mining databases of experimentally measured entropy and enthalpy values for various kinds of loops, fitting novel nearest neighbor parameters by applying Brown?s algorithm to compute the joint probability distribution from inferred marginals, and by implementing extensions of theZuker algorithm to compute minimum free energy structure and partition function for the extended nearest neighbor model. We will then validate the extended nearest neighbor energy model and our algorithms by benchmarking predictions with the Rfam database and with secondary structures inferred from X-ray structures by using RNAview. RNA is now understood to be a biomolecule of fundamental importance to molecular biology, having potential clinical applications in cancer diagnosis, etc. In addition to its role in gene regulation (micro RNAs and riboswitches), noncoding RNA can direct which regions of the genome are transcribed (placement of epigenetic markers) and which variants of a protein will be produced in the cell of a particular organ (alternative splice variants). In medicine, the pattern of dysregulated micro RNAs forms a biomarker for certain types of cancer. Regulation by RNA depends on its structure, in fact, primarily its secondary structure, defined as the (planar) collection of hydrogen bonds formed between different RNA nucleotides of a given sequence. The prediction of RNA secondary structure, given only its nucleotide sequence, is roughly 70% accurate. By improving this accuracy, it will be possible to better predict the messenger RNA targets of micro RNAs, and more generally to better understand gene regulation by RNA. The goal of this grant proposal to improve prediction accuracy by developing a better energy model, called the extended nearest neighbor energy model, in which the formation of hydrogen bonds between two given nucleotides depends on whether additional hydrogen bonds can form as well between neighbors of the nucleotides. We will develop energy parameters for the extended nearest neighbor model by data mining existent UV absorption experimental data, using statistical fitting algorithms, and we will develop computer programs to predict RNA secondary structure using this new energy model. Prediction accuracy of our new approach will be benchmarked on databases of RNA secondary structure.

基于热力学的从头RNA二级结构算法被用于检测microRNA，microRNA的目标，非编码RNA基因，温度依赖性核糖核酸调节因子，硒蛋白，核糖体移码位置，RNA蛋白结合位点等的重要性和普遍性的应用程序的基于RNA的算法不能过分强调，事实上，这样的应用包括RNA设计的新的癌症治疗和合成生物学。最近邻（NN）模型（也称为特纳模型）的自由能参数基本上构成了当前所有基于遗传算法的RNA算法的基础。动态规划最小自由能结构计算用祖克？的算法产生的碱基对预测的准确率约为70%。在这项授权中，我们打算提高碱基对预测精度挖掘数据库的实验测得的熵和焓值的各种环，拟合新的最近邻参数应用布朗？的算法来计算联合概率分布的推断边缘，并通过实现扩展的Zuker算法来计算最小自由能结构和分区函数的扩展最近邻模型。然后，我们将通过Rfam数据库和使用RNAview从X射线结构推断的二级结构进行基准预测来验证扩展最近邻能量模型和我们的算法。 RNA现在被认为是对分子生物学具有根本重要性的生物分子，除了其在基因调控中的作用之外，在癌症诊断等方面具有潜在的临床应用非编码RNA可以指导基因组的哪些区域被转录（表观遗传标记的放置）以及蛋白质的哪些变体将在特定器官的细胞中产生（选择性剪接变体）。在医学上，失调的微小RNA的模式形成了某些类型癌症的生物标志物。RNA的调控取决于其结构，事实上，主要是其二级结构，定义为给定序列的不同RNA核苷酸之间形成的氢键的（平面）集合。如果只考虑RNA的核苷酸序列，那么对RNA二级结构的预测准确率大约为70%。通过提高这种准确性，将有可能更好地预测microRNA的信使RNA靶点，更普遍地说，可以更好地理解RNA对基因的调控。这项资助提案的目标是通过开发一种更好的能量模型来提高预测准确性，称为扩展最近邻能量模型，其中两个给定核苷酸之间氢键的形成取决于是否可以在核苷酸的邻居之间形成额外的氢键。我们将通过数据挖掘现有的紫外吸收实验数据，使用统计拟合算法，开发扩展最近邻模型的能量参数，并将开发计算机程序来预测RNA二级结构使用这个新的能量模型。我们的新方法的预测精度将在RNA二级结构数据库上进行基准测试。