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

• 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
• 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

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），整个基因组测序和高通量技术具有 产生了大量不同的OMIC和表型数据。但是，这些数据集尚未 全面探索以提高药物发现的有效性和效率，这继续沿 药物范式范式。因此，将药物带入市场的成本令人震惊，失败率 令人生畏。我们的长期目标是通过识别强大的方法来恢复滞后药品管道 以实现精确医学。我们将通过开发一种新颖的结构系统来实现这一目标 药物发现的药理学方法，该方法将基于结构的药物设计与异质相结合 在整个人类和病原体基因组和相互作用组的背景下，法律数据的整合和分析。 我们小组和其他人的越来越多的证据表明，大多数药物通常相互作用 具有多个受体（目标）。强大和弱的多重药物目标相互作用都可以集体 通过生物分子的构象动力学介导药物疗效，毒性和抗性。为了 为了合理设计有力，安全和精确的医学，我们面临着主要的尚未解决的挑战之一 基于结构的药物设计：所有可能的蛋白质及其构象状态与哪些与之相互作用 有机体中的药物？该建议试图通过开发，传播和 实验测试新型计算工具。根据我们成功的初步结果，我们将发展 一项集成的计算管道，以识别三维（3D）蛋白质化学相互作用模型 细胞环境和结构蛋白质组量表。具体而言，我们将开发一个第四纪结构 - 以基因组学，蛋白质组学和 现象学。我们将开发一种新颖的协作单级协作过滤算法，以推断失踪 多层网络中的关系。我们将结合源自结构生物信息学，生物物理学的工具， 和机器学习以获得对药物作用的生物学见解。促进可用性和 拟议算法的可重复性，我们将开发由我们的基于社区的Web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测试提出的计算工具作为 一个现实世界的示例，并迭代地改善算法，软件和Web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 为了增强基于结构的多目标药物设计的高通量筛选的范围和能力； （2）用户友好的Web服务，以支持基于社区的药物发现； （3）潜在的新型反癌 有针对性的治疗学。这些工具一起将通过提供药物发现和精确医学 结构系统药理学工具包。