Promoter Prediction Using the Opening Profile of DNA

启动子预测领域几乎具有无限的潜力，但取得的成功非常有限。我们提出了一种基于DNA的物理性质来预测真核生物启动子的新方法，而不是分析DMA序列的同源性。利用非线性数学模型的计算机模拟，我们预测了DNA区域的局部开放轮廓。对于几个真核生物启动子样本，我们已经证明了由模型预测的优势优先开放位置（并通过S1核酸酶消化试验验证）与实验确定的位置密切相关

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.