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

基于热力学的从头计算RNA二级结构算法用于检测microrna、microrna靶点、非编码RNA基因、温度依赖性核糖体调节因子、硒蛋白、核糖体移码位置、RNA-蛋白结合位点等。RNA热力学算法应用的重要性和普遍性怎么强调都不过分；事实上，这类应用包括用于新型癌症疗法和合成生物学的RNA设计。最近邻（NN）模型的自由能参数，也称为特纳模型，构成了基本上所有当前基于热力学的RNA算法的基础。动态规划最小自由能结构的计算S算法的碱基对预测准确率约为70%。在这项资助中，我们打算通过挖掘各种回路的实验测量熵和焓值的数据库来提高碱基对预测的准确性，并通过应用Brown？通过实现zuker算法的扩展来计算扩展的最近邻模型的最小自由能结构和配分函数。然后，我们将通过Rfam数据库和RNAview从x射线结构推断的二级结构对预测进行基准测试，验证扩展的最近邻能量模型和我们的算法。RNA是分子生物学中一种具有重要基础意义的生物分子，在癌症诊断等方面具有潜在的临床应用价值。除了在基因调控（微RNA和核糖开关）中的作用外，非编码RNA还可以指导基因组的哪些区域被转录（表观遗传标记的放置），以及在特定器官的细胞中产生蛋白质的哪些变体（替代剪接变体）。在医学上，微rna失调的模式形成了某些类型癌症的生物标志物。RNA的调控取决于它的结构，事实上，主要是它的二级结构，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。仅根据核苷酸序列对RNA二级结构的预测准确率约为70%。通过提高这种准确性，将有可能更好地预测微RNA的信使RNA靶点，并更广泛地了解RNA对基因的调控作用。这项拨款提案的目标是通过开发一种更好的能量模型来提高预测的准确性，这种模型被称为扩展的最近邻能量模型，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。我们将通过对现有紫外吸收实验数据的数据挖掘，利用统计拟合算法，开发扩展最近邻模型的能量参数，并利用该新能量模型开发计算机程序来预测RNA二级结构。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。

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.