Omics data integration and analysis for structure-based multi-target drug design

基于结构的多靶点药物设计的组学数据集成和分析

基本信息

批准号：
9285997
负责人：
STEPHEN K BURLEY
金额：
$ 35.42万
依托单位：
HUNTER COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2021-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9285997
关键词：
Address Adopted Adverse effects Algorithmic Software Algorithms Animal Model Big Data Binding Bioinformatics Biological Biomedical Research Biophysics Chemicals Clinical Clinical Treatment Clinical Trials Collaborations Communities Complex Computer Simulation Computer software Computing Methodologies Data Data Analyses Data Analytics Data Set Databases Dimensions Discipline Docking Drug Costs Drug Design Drug Industry Drug Interactions Drug Targeting Drug effect disorder Effectiveness Extracellular Protein Face Failure Fostering Genes Genetic Genomics Genotype Goals Human Genome In Vitro Knowledge Laboratories Ligands Link Machine Learning Malignant Neoplasms Mediating Methodology Methods Modeling Modernization Molecular Conformation Noise Organism Outcome Performance Pharmaceutical Preparations Pharmacologic Substance Pharmacology Phase III Clinical Trials Phenotype Phosphotransferases Physiological Process Protein Dynamics Proteins Proteome Proteomics Reproducibility Research Research Personnel Resistance Structure System Systems Biology Techniques Testing Toxic effect United States National Institutes of Health Update Vision Work anti-cancer therapeutic base biological systems cancer therapy cellular targeting computer infrastructure computerized tools cost data integration design drug candidate drug development drug discovery drug efficacy experience functional genomics genome sequencing genome wide association study genomic data high throughput screening human subject improved in vivo industry partner insight kinase inhibitor molecular dynamics mortality network models novel novel therapeutics online resource open source pathogen genome phenome phenomics phenotypic data precision medicine receptor small molecule structural genomics targeted treatment tool transcriptomics usability user-friendly web services whole genome

项目摘要

Abstract Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput techniques have generated vast amounts of diverse omics and phenotypic data. However, these sets of data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along the one- drug-one-gene paradigm. Consequently, the cost of bringing a drug to market is staggering, and the failure rate is daunting. Our long-term goal is to revive the lagging pharmaceutical pipeline by identifying robust methods for achieving precision medicine. We will achieve this goal by developing a novel structural systems pharmacology approach to drug discovery, which integrates structure-based drug design with heterogeneous omics data integration and analysis in the context of the whole human and pathogen genome and interactome. An increasing body of evidence from both our group and others suggests that most drugs commonly interact with multiple receptors (targets). Both strong and weak multiple drug-target interactions can collectively mediate drug efficacy, toxicity, and resistance through the conformational dynamics of biomolecules. In order to rationally design potent, safe, and precision medicine, we face one of the major unsolved challenges in structure-based drug design: what are all the possible proteins and their conformational states interacting with a drug in an organism? This proposal attempts to address this challenge by developing, disseminating, and experimentally testing novel computational tools. Based on our successful preliminary results, we will develop an integrated computational pipeline to identify three-dimensional (3D) protein-chemical interaction models in the cellular context and on a structural proteome scale. Specifically, we will develop a quaternary structure- centric multi-layered network model by integrating heterogeneous data from genomics, proteomics, and phenomics. We will develop a novel collaborative one-class collaborative filtering algorithm to infer missing relations in the multi-layered network. We will combine tools derived from structural bioinformatics, biophysics, and machine learning to gain biological insights into the drug action. To facilitate the usability and reproducibility of the proposed algorithms, we will develop community-based web resources established by our previous experiences in developing the Protein Data Bank (PDB). More importantly, we will work closely with experimental laboratories to test the proposed computational tools using targeted kinase polypharmacology as a real-world example, and iteratively improve the performance and usability of algorithm, software, and web services. The successful completion of this project will provide the scientific community with: (1) new methods to enhance the scope and capability of high-throughput screening for structure-based multi-target drug design; (2) a user-friendly web service to support community-based drug discovery; and (3) potential novel anti-cancer targeted therapeutics. Together, these tools will advance drug discovery and precision medicine by providing a structural systems pharmacology toolkit.

抽象的全基因组关联研究 (GWAS)、全基因组测序和高通量技术已产生了大量不同的组学和表型数据。不过，这组数据尚未得到证实充分探索以提高药物发现的有效性和效率，并沿着这一方向继续进行药物单基因范式。因此，将药物推向市场的成本是惊人的，而且失败率很高是令人畏惧的。我们的长期目标是通过确定稳健的方法来重振落后的制药渠道以实现精准医疗。我们将通过开发新颖的结构系统来实现这一目标药物发现的药理学方法，它将基于结构的药物设计与异质性相结合在整个人类和病原体基因组和相互作用组的背景下进行组学数据整合和分析。我们的团队和其他人提供的越来越多的证据表明，大多数药物通常会相互作用具有多个受体（目标）。强和弱的多药物靶点相互作用可以共同通过生物分子的构象动力学介导药物功效、毒性和耐药性。为了为了合理地设计有效、安全和精准的药物，我们面临着尚未解决的主要挑战之一基于结构的药物设计：所有可能的蛋白质及其构象状态有哪些相互作用有机体中的药物？该提案试图通过制定、传播和实验测试新颖的计算工具。基于我们成功的初步成果，我们将开发一个集成的计算管道，用于识别三维（3D）蛋白质-化学相互作用模型细胞背景和结构蛋白质组规模。具体来说，我们将开发一种四级结构- 通过集成来自基因组学、蛋白质组学和生物医学的异构数据来构建中心多层网络模型表型组学。我们将开发一种新颖的协作一类协作过滤算法来推断缺失多层网络中的关系。我们将结合来自结构生物信息学、生物物理学、和机器学习以获得药物作用的生物学见解。为了促进可用性和所提出的算法的可重复性，我们将开发由我们建立的基于社区的网络资源以前开发蛋白质数据库（PDB）的经验。更重要的是，我们将与实验实验室使用靶向激酶多药理学来测试所提出的计算工具一个真实的例子，迭代地提高算法、软件和网络的性能和可用性服务。该项目的成功完成将为科学界提供：（1）新方法提高基于结构的多靶点药物设计的高通量筛选的范围和能力； (2) 用户友好的网络服务，支持基于社区的药物发现； (3) 潜在的新型抗癌药物靶向治疗。这些工具将共同推动药物发现和精准医学的发展结构系统药理学工具包。