Structure based prediction of the interactome

描述（由申请人提供）：分子相互作用在所有生物学过程中发挥核心作用。类似于基因组的完整测序，相互作用组的完整描述是更深入理解生物过程的基本步骤，并具有影响系统生物学，基因组学，分子生物学和治疗学的巨大潜力。蛋白质-蛋白质相互作用（PPI）和蛋白质-RNA相互作用（PRI）是特别感兴趣的，因为它们在维持细胞完整性、代谢、转录/翻译和细胞-细胞通讯中至关重要。尽管高通量实验PPI和PRI数据正在迅速积累，但构建完整和可靠的数据集需要多次重复昂贵的筛选。该提案旨在开发新的方法，这些方法将显着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，并提高对新兴高通量（HTP）数据的信心，目标是以更低的成本进行全面的相互作用组作图。总之，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，并分支到蛋白质-RNA相互作用预测方法;这代表了大多数生物信息学方法用于预测的纯粹基于序列的方法的重大转变。这些框架将提供对内部和公共HTP数据的全面评估，具有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，MAPK 6在癌症相关信号网络中的作用，以及（长非编码）RNA-蛋白在神经退行性疾病中的结合作用。最后，我们将在基因组规模上计算筛选PPI和PRI，并扩展我们的Struct 2Net网络服务器，以向社区传播基于我们的方法和结果的工具。越来越多的HTP相互作用数据集正在确定，从而提供了新的机会，利用这些数据结合结构的见解，以映射结合位点，并揭示细胞功能的潜在分子机制。分子相互作用，并将提高完整的相互作用组的覆盖率和准确性。这些目标的成功完成将导致计算方法，这将大大增加我们对蛋白质-蛋白质和蛋白质-RNA相互作用的高通量数据的信心，并将揭示其功能的基本方面，以及实验研究的可检验假设。所有开发的软件都将公开提供。 公共卫生相关性：生物过程是通过各种类型的分子（相互作用组）之间的数千种相互作用进行的，这些分子在所有生物医学过程中发挥着重要作用，包括维持细胞完整性，代谢，转录/翻译和细胞-细胞通讯。大规模了解这些相互作用网络将使合理的靶向药物设计和更智能的疾病管理成为可能。在这个项目中，我们开发了基于结构的蛋白质-蛋白质和蛋白质- RNA相互作用预测的计算方法，并将这些预测与现有的高通量基因组数据相结合，以预测整个物种基因组的相互作用组。

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. PUBLIC HEALTH RELEVANCE: Biological processes are carried out through thousands of interactions between various types of molecules (the Interactome) that play fundamental roles in all biomedical processes including the maintenance of cellular integrity, metabolism, transcription/translation, and cell-cell communication. Understanding these interaction networks on a large scale will empower both rational, targeted drug design and more intelligent disease management. In this project, we develop computational methods for structure-based prediction of protein-protein and protein- RNA interactions, and integrate these predictions with available high-throughput genomic data to predict the Interactomes of entire species' genomes.