Promoter Prediction Using the Opening Profile of DNA

启动子预测领域拥有几乎无限的潜力，但成功非常有限。我们提出了一种新的基于DNA物理性质的真核启动子预测方法，而不是分析DNA序列的同源性。使用计算机模拟与非线性数学模型，我们预测的DNA区域的本地化开放配置文件。对于几个样品真核启动子，我们已经证明，由模型预测的显性优先开放位置（并通过S1核酸酶消化测定验证）与实验确定的启动子的开放位置相关性良好。 转录起始位点或基因启动子的主要调控位点。我们假设 这些开口分布可以更普遍地应用于寻找新的基因启动子， 基因组DNA中的转录重要位点。在这里，我们建议进一步验证使用非线性数学模型来预测开放配置文件作为真核启动子预测的指标，使用已知的基因核心启动子与实验确定的转录起始位点。我们还将寻求应用计算启动子预测方法在概念验证研究的基因与身份不明的启动子和转录起始位点。最后，我们计划开发数值技术，使我们的启动子预测模型在基因组规模上的应用。基于模拟的DNA开放图谱分析在人类基因启动子的预测中显示出巨大的潜力。这种方法是上级以前的预测模型，因为它检查序列衍生的DNA的物理性质，而不是序列同源性，然而，进一步的调查是必要的，以评估和扩大这种方法的适用性的限制。计算模型的最大优点之一是它可以用于评估任何真核DNA序列的开放轮廓 而且成本很低。

The field of promoter prediction holds almost limitless potential, but has had very limited success. Instead of analyzing DMA sequence homology, we propose a novel method of eucaryotic promoter prediction based on the physical properties of DNA which arise from the local sequence. Using computer simulations with a nonlinear mathematical model, we predict the localized opening profile of a region of DNA. For several sample eucaryotic promoters, we have demonstrated that the dominant preferential opening positions predicted by the model (and verified by S1 nuclease digestion assays) correlate well with the experimentally-determined transcriptional start sites or major regulatory sites of the gene promoters. We hypothesize that these opening profiles can be applied more generally in seeking out novel gene promoters and transcriptionally significant sites in genomic DNA. Here we propose to further validate the use of nonlinear mathematical models to predict opening profiles as indicators for eukaryotic promoter prediction using known gene core promoters with experimentally-determined transcriptional start sites. We will also seek to apply the computational promoter prediction method in proof-of-concept studies on genes with unidentified promoters and transcriptional start sites. Finally, we plan to develop numerical techniques that allow application of our promoter prediction model on a genomic scale. The simulation-based analysis of DNA opening profiles shows great potential in the prediction of human gene promoters. This method is superior to previous prediction models in that it examines sequence-derived physical properties of DNA rather than sequence homology, however, further investigation is necessary to evaluate and expand the limits of applicability of this method. One of the strongest advantages of the computational model is that it can be used to evaluate opening profiles for any sequence of eukaryotic DNA with very little cost.