Structure based prediction of the interactome

描述(申请人提供)：分子相互作用在所有生物过程中起着核心作用。与基因组的完整测序类似，对相互作用的完整描述是加深对生物过程理解的根本步骤，并具有巨大的潜力影响系统生物学、基因组学、分子生物学和治疗学。蛋白质-蛋白质相互作用(PPI)和蛋白质-RNA相互作用(PRI)在维持细胞完整性、新陈代谢、转录/翻译和细胞间通讯方面具有重要意义。尽管高通量实验PPI和PRI数据正在快速积累，但构建完整和可靠的数据集需要多次复制昂贵的屏幕。这项提议旨在开发新的方法，以显著推进我们在基于结构的方法方面的努力，以更好地预测PPI和RPI，并增强对新出现的高通量(HTP)数据的信心，目标是以更低的成本全面绘制相互作用组图。综上所述，这些方法将极大地扩展我们对大分子网络的理解。我们将继续设计基于结构的蛋白质-蛋白质相互作用预测方法，并扩展到蛋白质-RNA相互作用预测方法；这代表着大多数生物信息学方法用于预测的纯基于序列的方法的重大转变。我们还将建立计算框架，使用基于结构的方法提高对HTP蛋白质-蛋白质和蛋白质-RNA相互作用数据集的信心；这些框架将提供内部和公共HTP数据的全面评估，以及潜在的生物医学应用，例如与癌症治疗开发相关的热休克蛋白-激酶相互作用，MAPK6在癌症相关信号网络中的S角色，以及(长非编码)RNA-蛋白质结合在神经退行性疾病中的角色。最后，我们将在基因组范围内计算筛选PPI和PRI，并扩展我们的Struct2Net网络服务器，将基于我们的方法和结果的工具分发给社区。越来越多的HTP相互作用数据集正在确定中，从而提供了新的机会，将这些数据与结构洞察力结合起来，绘制结合位点图，并揭示细胞功能的潜在分子机制。分子相互作用，并将提高完整相互作用组的覆盖率和准确性。成功完成这些目标将导致计算方法，这些方法将显著增加我们对蛋白质-蛋白质和蛋白质-RNA相互作用的高通量数据的信心，并将揭示它们功能的基本方面，以及用于实验研究的可验证的假设。所有开发的软件都将公之于众。

DESCRIPTION (provided by applicant): Molecular interactions play a central role in all biological processes. Akin to the complete sequencing of genomes, complete descriptions of interactomes is a fundamental step towards a deeper understanding of biological processes, and has a vast potential to impact systems biology, genomics, molecular biology and therapeutics. Protein-protein interactions (PPIs) and protein-RNA interactions (PRIs) are of particular interest as they are critical in maintenance of cellular integrity, metabolism, transcription/translation, and cell-cell communication. Although high-throughput experimental PPI and PRI data is rapidly accumulating, building complete and confident datasets requires multiple replicates of expensive screens. This proposal aims to develop new methods that will significantly advance our efforts at structure-based approaches to better predict PPIs and RPIs and boost confidence in emerging high-throughput (HTP) data with the goal of comprehensive interactome mapping at lower cost. Taken together, these methods will vastly expand our understanding of macromolecular networks. We will continue to devise structure-based methods for protein-protein interaction prediction and branch out to methods for protein-RNA interaction prediction; this represents a major shift from the purely sequence-based approaches that most bioinformatics approaches utilize to predict We will also build computational frameworks for boosting confidence in HTP protein-protein and protein-RNA interaction datasets using structure-based approaches; these frameworks will provide a comprehensive assessment of in-house and public HTP data, with potential biomedical applications such as heat shock protein-kinase interactions related to development for cancer therapeutics, MAPK6's role in a cancer-related signaling network, and (long non-coding) RNA-protein binding roles in neurodegenerative disease. Finally, we will computationally screen for PPIs and PRIs at the genome scale and expand our Struct2Net webserver to disseminate tools based on our methods and results to the community. An increasing number of HTP interaction datasets are being determined, thus presenting new opportunities to leverage this data in conjunction with structural insights to map binding sites and to uncover the underlying molecular mechanisms of cellular functions. molecular interactions and will enhance coverage and accuracy of the complete interactome. Successful completion of these aims will result in computational methods that will significantly increase our confidence in high-throughput data on protein-protein and protein-RNA interactions and will reveal fundamental aspects of their functioning, as well as testable hypotheses for experimental investigations. All developed software will be made publicly available.