MULTIPROCESSOR SEQUEST FOR LARGE SCALE ANALYSIS OF PROTEINS

资源的此组件的主要重点是应用 发现监管要素的模式识别方法，例如 转录因子结合位点，在一组共同调节的基因中。我们 尚未从DNA阵列组获得用于分析的数据集。 然而，我们一直在评估我们项目的能力 确定酵母启动子中的正确功能结构域 具有已知调控位点的基因集合。这些控件具有 允许我们测试不同的方法，并对 提高他们的检测能力。我们还得到了几个 来自Pat Brown的实验数据集，并对这些AS进行了分析 进一步测试这些方法，以可靠地识别监管地点。 我们还探索了我们的方法来阐明 当多个蛋白质参与其中时的协同作用 监管。我们已经使用来自MetJ抑制子的数据完成了这项工作 大肠杆菌，因为有足够的数据来进行这项分析。一篇论文 已提交对这些结果的描述。我们认为 能够检测多个结合位点并解释其 协作相互作用对一些酵母基因来说将是重要的，并且 我们在一个大肠杆菌例子上采用和改进的方法是有用的 经验和准备。 我们还一直在开发和完善我们的 一种特异鉴定蛋白质-蛋白质中涉及的基序的方法 互动。为此，我们使用了MHC-多肽相互作用的数据 识别被识别的子域，并对能量 单个氨基酸的贡献。此方法似乎是 运行良好，并将通过其他数据集进一步完善。我们 我认为这种方法也适用于其他类型的数据，涉及 蛋白质-蛋白质相互作用，特别是用于识别 允许将蛋白质对接到复合体中的基序。

The primary focus of this component of the Resource is to apply pattern recognition methods to discover regulatory elements, such as transcription factor binding sites, in sets of co-regulated genes. We have not yet gotten datasets from the DNA array group for analysis. However, we have been assessing the ability of our programs to identify the correct functional domains in yeast promoters using collections of genes with known regulatory sites. The controls have allowed us to test different approaches and to make refinements to to improve their detection capability. We have also obtain a couple of experimental datasets from Pat Brown and have run analyses on those as further tests of the methods to reliably identify regulatory sites. We have also explored the ability of our methods to elucidate cooperative interactions when multiple proteins are involved in the regulation. We have done this using data from the MetJ repressor from E. coli because adequate data exist for this analysis. A paper describing these results has been submitted. We think that the ability to detect multiple binding sites and account for their cooperative interactions will be important for some yeast genes, and the approach we've adopted and refined on an E. coli example is useful experience and preparation. We have also been developing and refining an adaptation of our method to specifically identify motifs involved in protein-protein interactions. For this we have used data on MHC-peptide interactions to identify the sub-domain being recognized and to model the energetic contributions of individual amino acids. This method appears to be working well and will be further refined with additional datasets. We think this method will be applicable to other types of data concerning protein-protein interactions, specifically for the identification of motifs that allow for docking of the proteins into complexes.