Structure-Based Prediction of the Interactome

基于结构的相互作用组预测

基本信息

批准号：
7464375
负责人：
BONNIE BERGER
金额：
$ 28.09万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-04-01 至 2012-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7464375
关键词：
Accounting Algorithms Area Arts Avastin Binding Biochemical Biological Biological Process Blast Cell Categories Cell Communication Cell membrane Cells Chromosome Mapping Classification Co-Immunoprecipitations Collaborations Complex Computational algorithm Computer software Computing Methodologies Consultations Data Data Sources Databases Decision Trees Depth Development Disease Disease Management Drug Delivery Systems Drug Design ERBB2 gene Epidermal Growth Factor Receptor Erbitux Etanercept Evolution Extracellular Protein FGFR1 gene Facility Construction Funding Category Family Future Generic Drugs Genes Genetic Transcription Genome Genomics Goals Homology Modeling Human Human Genome Internet Investigation Knowledge Lead Learning Life Ligand Binding Ligands Linear Programming Link Localized Logistic Regressions Machine Learning Maintenance Measures Medical Membrane Proteins Metabolism Methods Molecular Biology Mutation Numbers Oncogenes Ontology Organism Output Performance Play Probability Process Programmed Learning Protein Binding Protein Databases Proteins Proteome Proteomics Public Health Applications Research Research Roche brand of rituximab Roche brand of trastuzumab Role Score Scoring Method Signal Transduction Structure Surface Systems Biology Techniques Testing Therapeutic Tissues Training Translations Validation Yeasts base computerized tools data mining design extracellular fly forest functional genomics genome database genome sequencing human disease improved interfacial novel novel strategies numb protein programs protein protein interaction protein structure receptor receptor binding research study success yeast two hybrid system

项目摘要

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.

描述（由申请人提供）：蛋白质 - 蛋白质相互作用（PPI）在所有生物过程中都起着核心作用。类似于基因组的完整测序，对相互作用组的完整描述是对生物学过程深入了解的基本一步，并且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。尽管发现PPI的高通量生化方法已被证明非常成功，但当前对Interactome的实验覆盖范围仍然不足，并且将从计算工具中受益。该提案的广泛，长期的目标是利用基于结构的计算方法提供的信息，以作为对交互式构造大规模综合方法的潜在高质量，高覆盖的数据源。具体而言，该项目的目的是：1）开发可在基因组量表上应用的新的基于结构的预测方法，以及2）将这些预测与其他功能基因组信息整合在一起，以在基因组量表上预测PPI。该项目还将产生可检验的假设进行实验研究。拟议研究的关键产品是LTHREADER程序，这是一个局部穿线程序，该程序将同时将查询序列对与蛋白质 - 蛋白质接口的模板保持一致。通过利用蛋白质复合物界面中包含的信息，它可以显着改善覆盖范围和预测质量的最新信息。某些核心计算方面是开发用于螺纹查询序列对模板（使用线性编程），学习统计势（SVM）的算法，并结合了PPI预测的多个蛋白质界面得分（促进）。此类基于结构的方法的输出将与用于预测PPI（使用随机森林）的结构2NET框架中的其他功能基因组数据相结合。拟议的研究的最终产物将是一个全面的基因组PPI预测数据库，该数据源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的预测将使细胞外和细胞内信号网络更好地阐明，这在药物靶标识别方面具有直接的医学意义。例如，这项研究的有希望的公共卫生应用是治疗剂的合理设计，这些设计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用于进一步研究。蛋白质相互相互作用以在细胞内和之间进行通信，形成在所有生物医学过程中扮演基本角色的网络（Interactome），包括维持细胞完整性，代谢，转录/翻译和细胞 - 细胞通信。大规模了解这些相互作用网络将赋予理性，有针对性的药物设计和更聪明的疾病管理能力。在该项目中，我们开发了用于基于结构的蛋白质蛋白质相互作用的预测的计算方法，并将这些预测与可用的高吞吐量基因组数据整合在一起，以预测整个物种基因组的相互作用。