Drug discovery by integrating chemical genomics and structural systems biology

通过整合化学基因组学和结构系统生物学来发现药物

基本信息

批准号：
9119046
负责人：
STEPHEN K BURLEY
金额：
$ 29.4万
依托单位：
HUNTER COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-01 至 2018-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9119046
关键词：
3-Dimensional Address Adoption Algorithms Anti-Infective Agents Antibiotic Resistance Antibiotics Area Big Data Binding Sites Biochemical Bioinformatics Biological Assay Biophysics Caenorhabditis elegans Cardiovascular Diseases Cells Chemical Structure Chemicals Clinical Collaborations Communities Computational Technique Computer Simulation Computer software Coupled Data Disease Docking Drug Design Drug Industry Drug Targeting Drug resistance Failure Generations Genes Genome Genomics Glean Goals Graph Health Health Services Research Human In Vitro Infection Interdisciplinary Study Laboratories Lead Ligands Malignant Neoplasms Maps Marketing Methodology Methods Microbe Mining Modality Modeling Molecular Molecular Models Molecular Target Multi-Drug Resistance Outcome Performance Pharmaceutical Preparations Pharmacologic Substance Process Protein Analysis Proteins Resolution Structure-Activity Relationship Systems Biology Techniques Testing Therapeutic Treatment Failure United States National Institutes of Health Validation Work base biological systems combat computerized tools cost design drug discovery functional genomics functional/structural genomics genome-wide genomic data global health improved in vivo in vivo Bioassay innovation molecular dynamics molecular modeling multi-scale modeling novel novel strategies novel therapeutics pathogen pathogen genome pre-clinical screening statistics structural genomics success tool

项目摘要

DESCRIPTION (provided by applicant): The cost of bringing a drug to market is astounding and the failure rate is daunting. The limited success of conventional drug discovery is in a large part attributed to the wide adoption of a reductionist model of "one- drug-one-gene-one-disease". New methodologies are very much called for: Polypharmacology focuses on defining multiple targets to a single drug and studying the effect of these drugs on perturbing disease-causing networks. Drug repurposing reuses existing drugs for new clinical indications. These two modalities have emerged as new drug discovery paradigms, and are strongly prompted by the NIH. However, rational and effective polypharmacology and drug repurposing is currently hindered by our limited understanding of structural and energetic origins of genome-wide drug-target interactions. To address this challenge, this proposal seeks to develop and experimentally validate an innovative methodology to determine high-resolution drug-target interactions on a genome scale. Building on our successful proof-of-concept studies, and close multidisciplinary collaborations between experimental and computational laboratories, we will integrate big data from chemical, structural, and functional genomics, and synthesize techniques derived from large-scale graph mining, global set statistics, chemoinformatics, bioinformatics, molecular modeling, and biophysics. Specifically, we will develop a new chemical similarity search method, ligand Enrichment of Network Topological Similarity (ligENTS), to map the continuous chemical universe to its global pharmacological space. We will integrate ligENTS with our already successful structural systems biology platform to construct high- resolution drug-target interaction models across species and across fold space. To demonstrate the feasibility and innovation of our proposed integrative approach, we will apply it to a test case: anti-infective drug repurposing. The emergence of drug resistant microbes to antibiotics poses a great threat to human health. Repurposing safe drugs to target pathogen-associated proteins has emerged as a novel concept to combat drug resistant pathogens. However, significant technical barriers exist in applying existing drug repurposing strategies across species in the context of pathogen-host interactions. Our innovative approach consolidates chemical, structural and network views of molecular components and their interactions in a biological system, thereby providing a new solution to discovering safe and efficient anti-infective agents by determining molecular targets of bioactive compounds in both humans and pathogens. We will experimentally validate novel pathogen-associated proteins and anti-infective drugs, generated from our in silico predictions, both in vitro and in vivo. If successful, this work will provide the scientific community and pharmaceutical industry with: (a) fundamentally new algorithms and associated software for identifying three-dimensional drug-target interaction models on a genome scale, and (b) experimentally validated novel anti-infective compounds and targets, with the potential to combat antibiotic resistance.

描述（由申请人提供）：将药物推向市场的成本令人震惊，失败率令人生畏。常规药物发现的成功有限，这在很大程度上归因于广泛采用了“单毒剂中疾病”模型。新的方法论很大程度上被称为：多形药理学侧重于将多个靶标定义为一种药物，并研究这些药物对扰动疾病的网络的影响。将重用现有药物的药物重新用于新的临床适应症。这两种方式已成为新药发现范式，并由NIH强烈提示。但是，目前，我们对全基因组药物目标相互作用的结构和能量起源有限的理解有限，理性有效的多种药理学和药物重新使用。为了应对这一挑战，该提案旨在开发和实验验证一种创新方法，以确定基因组量表上的高分辨率药物目标相互作用。在我们成功的概念验证研究和实验实验室之间的跨学科合作的基础上，我们将整合来自化学，结构和功能基因组学的大数据，并合成源自大型图形挖掘，全球集合统计量，化学性信息，化学构成，生物信息构成，生物象征学，分子模型和生物学模型和生物学。具体而言，我们将开发一种新的化学相似性搜索方法，即配体的富含网络拓扑相似性（配体），以将连续的化学宇宙映射到其全球药理空间。我们将与已经成功的结构系统生物学平台相结合，以构建跨物种和折叠空间的高分辨率药物 - 目标相互作用模型。为了证明我们提出的综合方法的可行性和创新，我们将其应用于测试案例：反感染药物重新利用。抗药性微生物对抗生素的出现对人类健康构成了巨大威胁。将安全药物重新利用以靶向病原体相关蛋白已成为对抗耐药病原体的一种新颖概念。但是，在病原体宿主相互作用的背景下，在应用物种的现有药物重新利用策略中存在重大的技术障碍。我们的创新方法巩固了分子成分的化学，结构和网络视图及其在生物系统中的相互作用，从而通过确定人类和病原体的生物活性化合物的分子靶标，为发现安全有效的抗感染剂提供了新的解决方案。我们将在体外和体内实验验证新型病原体相关的蛋白质和抗感染药物，这些蛋白质和抗感染性药物在体外和体内产生。如果成功的话，这项工作将为科学界和制药行业提供：（a）从根本上是新的算法和相关软件，可在基因组量表上识别三维药物 - 目标互动模型，以及（b）实验验证的新型抗感性化合物和靶标，具有抗击抗生素抵抗的潜力。