PREDICTING FUNCTIONAL EFFECTS OF NSSNPS USING MULTIPLE INFORMATION SOURCES

使用多种信息源预测 NSSNPS 的功能效果

• 批准号: 8170517
• 负责人: ANDREJ SALI
• 金额: $ 0.71万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8170517
• 关键词: Algorithms; Amino Acid Sequence; Biological Assay; Clinical Research; Collaborations; Collection; Computer Retrieval of Information on Scientific Projects Database; Computer software; Computing Methodologies; Data; Data Set; Databases; Disease; Funding; Gene Family; Goals; Grant; Homology Modeling; Imagery; Informatics; Institution; Internet; Laboratories; Measures; Membrane Transport Proteins; Methods; Mutagenesis; Output; Peptide Sequence Determination; Probability; Proteins; Relative (related person); Research; Research Personnel; Resources; Sequence Alignment; Set protein; Source; Structure; United States National Institutes of Health; base; biocomputing; crosslink; protein protein interaction; research study

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.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 广泛的目标是通过集成和交联的各种信息源并通过公共Web服务器提供蛋白质的功能表征。 我们正在开发一种计算方法，以结合物理和统计潜力，蛋白质序列比对，了解和预测结构，蛋白质 - 蛋白质相互作用以及临床研究的结合，以预测非同义SNP的功能效应。 具体目的是： AIM 1：定量确定最有用的序列和基于结构的特征，这些特征可用于预测NSSNP是否具有功能效应。 AIM 2：开发和验证一种用于组合功能的算法，以最好地对NSSNP进行分类。 该算法的输入将是蛋白质序列，也是更多候选NSSNP的蛋白质序列。 输出将是NSSNP是否具有功能效应（量化为概率和统计显着性度量）的预测和预测的解释，该预测使用了哪些功能及其相对重要性。 该方法将通过几个数据集在计算上进行验证：已经进行了全面诱变实验和功能测定的蛋白质，并且NSSNP被确定为在SNP数据库中与中性或疾病相关的蛋白质。 AIM 3：在软件包中实现该方法，并使其可以作为Web服务器访问。 AIM 4：与膜转运蛋白（PMT）项目（Leabman等，2003）的UCSF药物遗传学合作应用该方法，包括Kathy Giacomini和Deanna Kroetz的实验室。 AIM 5：使用分类器的成功预测来了解为什么某些特征组合有效。 目标6：为所有可用的NSSNP创建功能预测的数据库，并将其保持最新。 AIM 7：与两个较大的Web数据库交叉链接Web资源：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。 我们将资源用于生物计算，可视化和信息学（RBVI）来访问PMT SNP数据的机器可读格式。 该数据用于开发用于表征非同义SNP的预测功能。