Predicting and analyzing variation in cellular interactomes

项目摘要 在过去的二十年里，重大的实验努力已经确定了大量的“参考”。 人类和其他模式生物的相互作用，沿着关于结合的大量知识 蛋白质的特异性，包括大部分人类转录因子（TF）。结果数据 已经被证明是了解细胞功能的一个非常有用的资源;然而，它们并没有 捕获分子相互作用和网络与个体之间的参考有何不同。的确， 虽然健康人群和患病人群的人类基因组正在迅速测序， 相应的个体特异性相互作用网络在很大程度上尚未得到研究;这是一个主要的差距 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，种群间大量的遗传变异使得在短期内不可行， 实验性地确定每个个体的交互网络。因此，我们的长期目标是发展 计算方法，以揭示是否和如何在编码和非编码部分的突变， 基因组扰乱细胞相互作用和网络。我们的具体目标是：（1）我们将开发计算 以结构为基础的方法来识别和编目，在蛋白质组规模，蛋白质内的变化，可能是 以影响它们与DNA、RNA、小分子、肽或离子结合的能力，从而提供 分析蛋白质相互作用变异的综合资源。(2)我们将开发基于新结构的 和概率方法来预测当TF突变时DNA结合特异性如何改变;由于 突变的TF与许多疾病有关，这将有助于了解疾病 网络和病理学。(3)我们将开发新的方法来发现非编码体细胞突变， 癌症中的人类调控网络;这是最终揭示患者特异性的关键一步 癌症网络总的来说，通过追求这些目标，将突变信息与现有的 关于参考相互作用，接口和特性的知识-我们将开发新的计算 这些方法将大大促进我们对疾病中分子相互作用的理解， 健康的环境。

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.