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Binding and Splicing mRNA

mRNA 的结合和剪接

基本信息

批准号：
7924463
负责人：
DANIEL PAUL AALBERTS
金额：
$ 7.92万
依托单位：
WILLIAMS COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-30 至 2011-08-31
项目状态：
已结题

项目摘要

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.

描述(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；例如，信使RNA剪接、微小RNA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结合来调节基因表达。我们最近开发的寡聚结合算法BINDIGO有效地计算了结合自由能。我们的目标是提高识别结合位点的准确性。(2)改进mRNA剪接模型，以了解热力学在选择性剪接和内含子/外显子分离中的作用，以及内含子开始处密码子框架中的相关性。我们的初步结果显示了意想不到的和显著的相关性；它们还显示了能量偏向，这可能解释了细胞如何找到剪接连接。(3)发现预先存在的二级结构如何影响结合事件，以及结合如何修改剩余的二级结构。(4)扩展RNA折叠算法，将结合事件包括在内。据估计，至少有15%的基因点突变会导致成熟mRNA的错误拼接。通过阐明剪接的机制和改进剪接的预测，有可能设计出治疗方法。而且，由于识别剪接位点是寻找基因的瓶颈，这一领域的改进可以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误差。我们建议对结合的小RNA和蛋白质如何调节基因表达，特别是mRNA剪接进行建模和计算。