PREDICTING FUNCTIONAL EFFECTS OF NSSNPS USING MULTIPLE INFORMATION SOURCES

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 广泛的目标是通过整合和交联各种信息源并通过公共网络服务器提供这些信息，为蛋白质的功能表征做出贡献。 我们正在开发一种计算方法来预测非同义SNP的功能效应，使用物理和统计潜力，蛋白质序列比对，已知和预测的结构，蛋白质-蛋白质相互作用和临床研究的组合。 具体目标是： 目标1：定量分析确定了信息量最大的基于序列和结构的特征，这些特征可用于预测nsSNP是否具有功能效应。 目标2：开发和验证一种算法，用于组合特征，以便最好地分类nsSNP。 该算法的输入将是蛋白质序列和多个候选nsSNP的一个。 输出将是nsSNP是否具有功能效应的预测（量化为概率和统计显著性度量）和预测的解释，使用了哪些特征及其相对重要性。 该方法将通过几个数据集进行计算验证：已进行全面诱变实验和功能测定的蛋白质，以及在SNP数据库中鉴定为中性或疾病相关的nsSNP。 目标3：在软件包中实现该方法，并使其作为Web服务器访问。 目的4：与UCSF膜转运蛋白（PMT）项目的药物遗传学合作应用该方法（Leabman等人，2003），包括Kathy Giacomini和Deanna Kroetz的实验室。 目标5：使用分类器做出的成功预测来理解为什么某些特征组合有效。 目标6：为所有可用的nsSNP创建功能预测数据库，并保持更新。 目标7：将网络资源与两个更大的网络数据库交叉链接：蛋白质同源性模型的Modbase集合和U.C.圣克鲁斯基因家族浏览器。 我们使用生物计算，可视化和信息学资源（RBVI）来访问PMT SNP数据的机器可读格式。 该数据用于开发可用于表征非同义SNP的预测特征。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The broad goal is to contribute to the functional characterization of proteins by integrating and cross-linking a variety of information sources and making them available via a public web server. We are developing a computational method for predicting the functional effects of non-synonymous SNPs, using a combination of physical and statistical potentials, protein sequence alignments, know and predicted structures, protein-protein interactions, and clinical studies. The specific aims are: Aim 1: Quantitatively identify the most informative sequence- and structure-based features that can be used to predict whether an nsSNP has a functional effect. Aim 2: Develop and validate an algorithm for combining the features so as to best classify nsSNPs. The input to the algorithm will be a protein sequence and one for more candidate nsSNPs. The output will be a prediction of whether the nsSNPs have a functional effect (quantified as a probability and a statistical significance measure) and an explanation of the prediction, which features were used and their relative importance. The method will be validated computationally with several data sets: proteins for which comprehensive mutagenesis experiments and functional assays have been performed, and nsSNPs identified as being neutral or disease-associated in SNP databases. Aim 3: Implement the method in a software package and make it accessible as a web server. Aim 4: Apply the method in collaboration with the UCSF Pharmocogenetics of Membrane Transporters (PMT) project (Leabman et al., 2003) including the laboratories of Kathy Giacomini and Deanna Kroetz. Aim 5: Use the successful predictions made by the classifier to understand why certain feature combinations are effective. Aim 6: Create a database of functional predictions for all available nsSNPs and keep it up to date. Aim 7: Cross-link the web resource with two larger web databases: the Modbase collection of protein homology models and the U.C. Santa Cruz Gene Family Browser. We use the Resource for Biocomputing, Visualization, and Informatics (RBVI) to access machine-readable formats of PMT SNP data. This data is used to develop predictive features useful in characterizing non-synonymous SNPs.