Predicting and analyzing variation in cellular interactomes

预测和分析细胞相互作用组的变化

• 批准号: 9896829
• 负责人: MONA SINGH
• 金额: $ 31.27万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-18 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9896829
• 关键词: Affect; Alleles; Amino Acid Sequence; Animal Model; Binding; Binding Proteins; Binding Sites; Bioinformatics; Biological; Biological Process; Catalogs; Cell physiology; Code; Complex; Computing Methodologies; DNA; DNA Binding; DNA Sequence Alteration; DNA-Protein Interaction; Data; Data Set; Diagnosis; Disease; Gene Expression; Genes; Genetic Variation; Genome; Goals; Human; Human Genome; Individual; Infrastructure; Internet; Ions; Knowledge; Ligand Binding; Link; Malignant Neoplasms; Mediating; Methods; Mutagenesis; Mutate; Mutation; Nucleic Acid Regulatory Sequences; Organism; Pathology; Patients; Pattern; Peptides; Play; Population; Property; Protein Analysis; Proteins; Proteome; RNA; Regulator Genes; Research; Resources; Sampling; Site; Software Tools; Somatic Mutation; Specificity; Structure; Untranslated RNA; Variant; Work; Zinc Fingers; base; cancer genome; disease-causing mutation; experimental study; human disease; improved; interest; knowledge base; machine learning method; novel; predictive tools; preference; small molecule; software development; transcription factor; tumor; virtual

Project Summary Over the last two decades, significant experimental efforts have determined large sets of “reference” interactions for humans and other model organisms, along with substantial knowledge about the binding specificities of proteins, including for a large fraction of human transcription factors (TFs). The resulting data have proven to be an incredibly useful resource for understanding how cells function; nevertheless, they do not capture how molecular interactions and networks are different from the reference across individuals. Indeed, while human genomes in both healthy and disease populations are rapidly being sequenced, the corresponding individual-specific interaction networks remain largely unexamined; this represents a major gap in our knowledge, as mutations that alter molecular interactions underlie a wide range of human diseases. Further, the substantial amount of genetic variation across populations makes it infeasible in the near term to experimentally determine per-individual interaction networks. Thus our long-term goal is to develop computational methods to uncover whether and how mutations within coding and non-coding portions of the genome perturb cellular interactions and networks. Our specific aims are: (1) We will develop computational structure-based approaches to identify and catalog, at proteome-scale, variations within proteins that are likely to impact their ability to bind with DNA, RNA, small molecules, peptides or ions, thereby providing a comprehensive resource for analyzing protein interaction variation. (2) We will develop novel structure-based and probabilistic methods to predict how DNA-binding specificities are altered when a TF is mutated; since mutated TFs have been linked to numerous diseases, this will be a great aid in understanding disease networks and pathology. (3) We will develop new methods to uncover non-coding somatic mutations that alter human regulatory networks in cancer; this is a critical step towards ultimately uncovering patient-specific cancer networks. Overall by pursuing these aims—which integrate mutational information with existing knowledge about reference interactions, interfaces and specificities—we will develop novel computational methods that will significantly advance our understanding of molecular interactions perturbed in disease and healthy contexts.

项目概要 在过去的二十年里，大量的实验工作已经确定了大量的“参考” 人类和其他模型生物的相互作用，以及有关结合的大量知识 蛋白质的特异性，包括大部分人类转录因子 (TF) 的数据。 已被证明是了解细胞功能的极其有用的资源；然而，它们并非如此。 捕捉分子相互作用和网络与个体之间的参考有何不同。 虽然健康人群和疾病人群的人类基因组正在迅速测序， 相应的个人特定互动网络在很大程度上仍未得到审查；这是一个重大差距； 据我们所知，改变分子相互作用的突变是多种人类疾病的基础。 此外，不同人群之间存在大量的遗传变异，使得短期内不可能实现 因此，我们的长期目标是开发每个人的交互网络。 揭示编码和非编码部分是否以及如何突变的计算方法 我们的具体目标是：（1）我们将开发计算能力。 基于结构的方法，在蛋白质组规模上识别和编录蛋白质内可能存在的变异 影响它们与 DNA、RNA、小分子、肽或离子结合的能力，从而提供 （2）我们将开发基于新型结构的 以及预测 TF 突变时 DNA 结合特异性如何的概率方法； 突变的转录因子与多种疾病有关，这对于了解疾病有很大帮助 (3)我们将开发新方法来揭示改变的非编码体细胞突变。 癌症中的人类网络监管；这是最终揭示患者特异性的关键一步 总体而言，通过追求这些目标——将突变信息与现有的相结合。 关于参考交互、接口和特性的知识——我们将开发新颖的计算 这些方法将显着增进我们对疾病和疾病中分子相互作用的理解 健康的环境。