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

• 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
• 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

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。传统药物发现的有限成功在很大程度上归因于广泛采用“一种药物一种基因一种疾病”的简化模型。非常需要新的方法：多药理学专注于定义单一药物的多个靶点，并研究这些药物对扰乱致病网络的影响。药物再利用将现有药物重新用于新的临床适应症。这两种模式已经成为新的药物发现范式，并受到NIH的强烈推动。然而，合理和有效的多药理学和药物再利用目前阻碍了我们有限的理解的结构和能量来源的全基因组药物靶点相互作用。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，以确定基因组规模上的高分辨率药物-靶标相互作用。在我们成功的概念验证研究以及实验和计算实验室之间密切的多学科合作的基础上，我们将整合来自化学，结构和功能基因组学的大数据，并合成来自大规模图形挖掘，全局集统计，化学信息学，生物信息学，分子建模和生物物理学的技术。具体来说，我们将开发一种新的化学相似性搜索方法，网络拓扑相似性的配体富集（ligENTS），将连续的化学宇宙映射到其全球药理学空间。我们将把ligENTS与我们已经成功的结构系统生物学平台整合，以构建跨物种和跨折叠空间的高分辨率药物-靶标相互作用模型。为了证明我们提出的综合方法的可行性和创新性，我们将把它应用到一个测试案例：抗感染药物再利用。对抗生素产生耐药性的微生物的出现对人类健康构成了极大的威胁。将安全药物重新用于靶向病原体相关蛋白已成为对抗耐药病原体的新概念。然而，在病原体-宿主相互作用的背景下，在跨物种应用现有的药物再利用策略方面存在重大的技术障碍。我们的创新方法巩固了分子组分及其在生物系统中相互作用的化学，结构和网络观点，从而通过确定人体和病原体中生物活性化合物的分子靶标，为发现安全有效的抗感染药物提供了新的解决方案。我们将通过实验验证新的病原体相关蛋白和抗感染药物，这些药物是从我们的计算机预测中产生的，包括体外和体内。如果成功，这项工作将为科学界和制药业提供：（a）用于在基因组规模上识别三维药物-靶标相互作用模型的全新算法和相关软件，以及（B）实验验证的新型抗感染化合物和靶标，具有对抗抗生素耐药性的潜力。