Binding and Splicing mRNA

描述（由申请人提供）：基因表达通常通过小RNA或蛋白质与信使RNA的结合来调节；例子包括mRNA剪接、microRNA和降解信号。做出更准确的预测将有助于揭示结合和剪接mRNA的功能和细胞活性。我们建议：(1)建立小rna和蛋白质结合的物理化学模型，通过mRNA结合调节基因表达。我们最近开发的寡结合算法binigo可以有效地计算结合自由能。我们的目标是提高结合位点识别的准确性。(2)改进mRNA剪接模型，以了解热力学在可变剪接和内含子/外显子分离中的作用，以及内含子起始密码子框架中的相关性。我们的初步结果显示出意想不到的显著相关性；它们也表现出能量偏差，这或许可以解释细胞是如何找到剪接的。(3)发现预先存在的二级结构如何影响绑定事件，以及绑定如何修改剩余的二级结构。(4)扩展RNA折叠算法以包含结合事件。据估计，至少15%的基因点突变导致错误拼接成熟mRNA。通过阐明机制和改进剪接的预测，有可能设计治疗方法。而且，由于确定剪接位点是寻找基因的瓶颈，这一领域的改进有助于揭示基因组信息。所提出的算法具有更广泛的应用潜力：预测反义基因治疗、RNA干扰、逆转录转座子识别、基因表达的RNA调控以及基因芯片微阵列中的系统错误。我们建议建模和计算结合小rna和蛋白质如何调节基因表达，特别是mRNA剪接。

DESCRIPTION (provided by applicant): Gene expression is often regulated by the binding of small RNAs or proteins to messenger RNA; examples include mRNA splicing, microRNA, and degradation signals. Making more accurate predictions will help uncover the function and cellular activity of binding and splicing mRNA. We propose to: (1) Develop physical-chemical models of small RNAs and proteins binding that modulate gene expression through mRNA binding. Our recently developed oligo-binding algorithm BINDIGO efficiently computes binding free energies. We aim to improve the accuracy with which binding sites can be identified. (2) Improve models of mRNA splicing to understand the role of thermodynamics in alternative splicing and intron/exon segregation, and correlations in the codon frames where introns begin. Our preliminary results show unexpected and significant correlations; they also show energetic biases which may explain how cells find splice junctions. (3) Discover how pre-existing secondary structure influences binding events, and how binding modifies remaining secondary structures. (4) Expand RNA folding algorithms to include binding events. It is estimated that at least 15% of genetic point mutations result in incorrectly spliced mature mRNA. By elucidating the mechanisms and improving the predictions of splicing, it may be possible to design therapeutics. And, since identifying splice sites is a bottleneck in finding genes, improvements in this area can contribute to revealing genomic information. The proposed algorithms have the potential for wider application: predicting anti-sense gene therapies, RNA interference, retro-transposon recognition, RNA regulation of gene expression, and systematic errors in gene chip microarrays. We propose to model and compute how binding small RNAs and proteins modulates gene expression, in particular mRNA splicing.