Structure-Based Prediction of the Interactome

描述(由申请人提供)：蛋白质-蛋白质相互作用(PPI)在所有生物过程中发挥核心作用。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。尽管发现PPI的高通量生化方法已被证明非常成功，但目前对交互作用组的实验覆盖仍然不足，并将受益于计算工具。这项提议的广泛、长期目标是利用基于结构的计算方法提供的信息，作为大规模综合方法相互作用组构建的潜在高质量、高覆盖率的数据源。具体地说，本项目的目标是：1)开发可应用于基因组规模的基于结构的新预测方法；2)将这些预测与其他功能基因组信息相结合，以预测基因组规模的PPI。该项目还将为实验研究产生可检验的假设。这项拟议研究的一个关键产品是LTHREADER程序，这是一个本地化的线程程序，它将同时将查询序列对与蛋白质-蛋白质界面的模板进行比对。通过利用蛋白质复合体界面中包含的信息，它可以在覆盖和预测质量方面显著提高最先进的水平。一些核心的计算方面是开发用于将查询序列对线性化到模板的算法(使用线性规划)、学习统计势能(SVM)以及组合用于PPI预测的多个蛋白质接口分数(Boosting)。这种基于结构的方法的输出将与Struct2Net框架中的其他功能基因组数据相结合，以预测PPI(使用随机森林)。这项拟议研究的最终产品将是一个全基因组PPI预测的综合数据库，该数据库来自纯粹基于结构的方法以及综合方法。该数据库还将包括细胞外配体-受体相互作用。 PPI的预测将有助于更好地阐明细胞外和细胞内的信号网络，这在药物靶标识别方面具有直接的医学意义。例如，这项研究的一个有前途的公共卫生应用是合理设计治疗药物，抑制或干扰细胞外配体与受体的结合。所有生产的计算算法、软件和数据库都将公开供进一步研究使用。 蛋白质相互作用，在细胞内和细胞之间进行交流，形成网络(相互作用组)，在所有生物医学过程中发挥基础作用，包括维持细胞完整性、新陈代谢、转录/翻译和细胞间通信。大规模了解这些相互作用网络将使合理的、有针对性的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来预测整个物种基因组的相互作用。

DESCRIPTION (provided by applicant): Protein-protein interactions (PPIs) 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. Although high-throughput biochemical approaches for discovering PPIs have proven very successful, the current experimental coverage of the interactome remains inadequate and would benefit from computational tools. The broad, long term goal of this proposal is to harness the information provided by structure-based computational approaches as a potentially high-quality, high-coverage data source for large-scale integrative approaches to interactome construction. Specifically, this project aims to: 1) develop new structure-based prediction methods that can be applied on a genome scale, and 2) integrate these predictions with other functional genomic information to predict PPIs at a genome scale. This project will also generate testable hypotheses for experimental investigations. A key product of the proposed research is the LTHREADER program, a localized threading program that will simultaneously align query sequence-pairs to templates of protein-protein interfaces. By exploiting information contained in the protein complex interfaces, it may significantly improve upon the state-of-the-art in coverage and prediction quality. Some of the core computational aspects are the development of algorithms for threading query sequence-pairs to templates (using linear programming), learning statistical potentials (SVMs), and combining multiple protein interface scores for PPI prediction (boosting). The output from such structure-based approaches will be combined with other functional genomic data in the Struct2Net framework for predicting PPIs (using random forests). A final product of the proposed research will be a comprehensive database of genome-wide PPI predictions derived from purely structure-based as well as integrative approaches. The database will also include extracellular ligand-receptor interactions. The prediction of PPIs will enable better elucidation of extracellular and intracellular signaling networks, which has direct medical implications in terms of drug target identification. For example, a promising public-health application of this research is the rational design of therapeutics which inhibit or interfere with the binding of extracellular ligands to receptors. All the produced computational algorithms, software, and databases will be made publicly available for further studies.Relevance Proteins interact with each other to communicate within and between cells, forming networks (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 interactions, and integrate these predictions with available high- throughput genomic data to predict the Interactomes of entire species' genomes.