Promoter Prediction Using the Opening Profile of DNA

启动子预测领域拥有几乎无限的潜力，但取得的成功非常有限。我们没有分析DNA序列的同源性，而是提出了一种新的基于DNA物理性质的真核启动子预测方法，该方法源于局部序列。利用计算机模拟和非线性数学模型，我们预测了DNA区域的局域开放轮廓。对于几个样本的真核启动子，我们已经证明了由模型预测的主要优先开放位置(并由s1核酸酶消化实验验证)与实验确定的很好地相关。 基因启动子的转录起始点或主要调控位点。我们假设 这些开放图谱可以更广泛地应用于寻找新的基因启动子和 基因组DNA中转录上有意义的位置。在这里，我们建议进一步验证使用非线性数学模型来预测作为真核生物启动子预测指标的非线性数学模型，使用具有实验确定的转录起始点的已知基因核心启动子。我们还将寻求将计算启动子预测方法应用于具有未知启动子和转录起始点的基因的概念验证研究。最后，我们计划开发数值技术，使我们的启动子预测模型能够在基因组规模上应用。基于模拟的DNA开放图谱分析在预测人类基因启动子方面显示出巨大的潜力。这种方法优于以前的预测模型，因为它检查的是DNA的序列衍生的物理性质，而不是序列同源性，但需要进一步的研究来评估和扩大这种方法的适用范围。计算模型的最大优点之一是它可以用来评估任何真核DNA序列的开放轮廓 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.