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.

摘要 全基因组关联研究（GWAS）、全基因组测序和高通量技术已经成为 产生了大量不同的组学和表型数据。然而，这些数据尚未被 充分探索，以提高药物发现的有效性和效率，这将继续沿着一个- 药物一基因模式因此，将药物推向市场的成本是惊人的，失败率也是惊人的。 令人生畏我们的长期目标是通过确定可靠的方法来恢复落后的制药管道 来实现精准医疗。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，它将基于结构的药物设计与异构化药物设计相结合。 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。 来自我们小组和其他人的越来越多的证据表明，大多数药物通常会相互作用， 多个目标（Target）。强的和弱的多药物-靶点相互作用可以共同地 通过生物分子的构象动力学介导药物功效、毒性和抗性。为了 为了合理设计有效、安全和精确的药物，我们面临着一个尚未解决的主要挑战， 基于结构的药物设计：所有可能的蛋白质及其构象状态与什么相互作用？ 一种药物在生物体内该提案试图通过开发、传播和 实验测试新的计算工具。基于我们成功的初步结果，我们将开发 一个集成的计算管道，以确定三维（3D）蛋白质化学相互作用模型， 细胞环境和结构蛋白质组规模。具体来说，我们将开发一种四级结构- 通过整合来自基因组学、蛋白质组学和 表型组学我们将开发一种新的协同单类协同过滤算法来推断缺失 多层网络中的关系。我们将联合收割机工具从结构生物信息学，生物物理学， 和机器学习来获得对药物作用的生物学见解。为了便于使用， 建议的算法的可重复性，我们将开发基于社区的网络资源建立我们的 蛋白质数据库（Protein Data Bank，PDB）更重要的是，我们将与 实验实验室，以测试拟议的计算工具，使用靶向激酶多药理学， 一个真实世界的例子，并迭代地提高算法，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 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)一个方便用户的网络服务，以支持社区为基础的药物发现;以及（3）潜在的新型抗癌药物 靶向治疗。总之，这些工具将通过提供 结构系统药理学工具包。