Computational Pharmacology

计算药理学

• 批准号: 8720765
• 负责人: Ivet Bahar
• 金额: $ 13.94万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8720765
• 关键词: Allosteric Site; Area; Autophagocytosis; Binding Sites; Chemicals; Computer Assisted; Computer software; Data; Databases; Drug Targeting; Goals; Institutes; Libraries; Ligands; Literature; Liver Fibrosis; Machine Learning; Metric; Mining; Mitogens; Modeling; Molecular Bank; Mouse Cell Line; Pathway interactions; Pharmaceutical Preparations; Pharmacology; Proteins; Quantitative Evaluations; RNA Interference; Research; Resources; Shapes; Structural Protein; Structure; System; Systems Biology; Techniques; Testing; base; computerized tools; design; drug discovery; high throughput screening; inhibitor/antagonist; multicatalytic endopeptidase complex; network models; pharmacophore; research study; scaffold; screening; small molecule; virtual

The Computational Pharmacology Core B (PI: Ivet Bahar, J.K. Vries Chair of Computational and Systems Biology, and Associate Director of U of Pittsburgh Drug Discovery Institute) will provide expertise in developing and applying computational tools for modeling protein-inhibitor interactions, assessing target proteins, pathways and networks, quantitative evaluation of target systems pharmacological features, and analyses of high throughput and high content screening (HTS/HCS) data. The Core resources for high-throughput virtual screening of molecular libraries as well as modeling protein-ligand interactions and mining databases will be extensively used to serve the research goals of the Center. The specific aims will focus on two areas: (i) quantitative and systems pharmacology (OSP) analyses and (ii) ligand- and target-based discovery approaches. QSP will deliver new therapeutical targets with small-molecule modulators and polypharmacology strategies for experimental tests in Project 1. We will identify new potential targets by analyzing RNAi and small-molecule screening data from Project 2 using machine learning techniques. We will also assess the polypharmacological effect of simultaneously targeting more than one protein. In particular, we will investigate the effects of combining modulators of autophagy and proteasome pathways, newly identified targets, and/or other specific targets investigated in Project 1 towards designing polypharmacological strategies. Target- and ligand based approaches, on the other hand, will be used for repurposing existing drugs, computational assessment of identified targets, and identifying new compounds. We will mine drug-target association databases such as DrugBank, and protein structural databases using sequence- and binding site shape similarity metrics. Targets with known structural data will be examined for availability of alternative more selective inhibition mechanisms by identifying potential allosteric sites. To this aim, we will use the structural ensemble analysis software and elastic network models developed in the Bahar lab. Finally, we will identify new chemical scaffolds by pharmacophore-based screening of purchasable compound libraries. Structure-based models of Project 1 targets, experimental HTS hits from Project 2, compounds from literature (e.g. autophagy inducers), and chemical mitogens investigated in Project 3 will be used in pharmacophore model building. Compounds that we identify and prioritize will be tested in cell lines, mouse and worm models of ATD. Results will be used to refine pharmacophore models and to iteratively search for new compounds

计算药理学核心B（PI：IVET Bahar，J.K。Vries计算与系统生物学主席，匹兹堡U型药物发现研究所U副主任以及匹兹堡U的副主任将在开发和应用计算工具方面使用计算工具来建模蛋白质抑制剂相互作用，以评估目标蛋白质和系统较高的目标，并分析目标较高，并分析特征，并分析了较高的系统，并分析特征，并分析特征，并分析的特征和网络。 （HTS/HCS）数据。分子库的高通量虚拟筛选以及对蛋白质 - 配体相互作用和采矿数据库进行建模的核心资源将广泛用于实现该中心的研究目标。 具体目的将重点放在两个领域：（i）定量和系统药理学（OSP）分析以及（ii）基于配体和基于目标的发现方法。 QSP将通过项目1中的实验测试提供新的治疗靶标和多种药理学策略。我们将通过使用机器学习技术分析项目2的RNAi和小分子筛选数据来确定新的潜在目标。我们还将评估同时靶向多种蛋白质的多药理学效应。特别是，我们将研究组合自噬和蛋白酶体途径的调节剂，新鉴定的靶标和/或项目1中研究的其他特定目标对设计多药理学策略的影响。另一方面，基于靶标和配体的方法将用于重新利用现有药物，对已确定靶标的计算评估以及识别新化合物。我们将使用序列和结合位点形状相似性指标来挖掘药物目标关联数据库，例如药品库和蛋白质结构数据库。将检查具有已知结构数据的目标，以通过鉴定潜在的变构位点可用性，以便替代选择性抑制机制。为此，我们将使用Bahar Lab中开发的结构集合分析软件和弹性网络模型。最后，我们将通过基于药效团的可购买化合物库来确定新的化学脚手架。项目1目标的基于结构的模型，项目2的实验性HTS命中，文献（例如自噬诱导剂）的化合物以及项目3中研究的化学有差点的化合物将用于药物学模型构建。我们识别和优先级的化合物将在ATD的细胞系，鼠标和蠕虫模型中进行测试。结果将用于完善药效团模型，并迭代地寻找新化合物