A Web-Based Automatic Virtual Screening System

基于网络的自动虚拟筛选系统

• 批准号: 7652801
• 负责人: John J. Irwin
• 金额: $ 28万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7652801
• 关键词: Benchmarking; Biological; Biological Process; Biology; Blast Cell; Chemicals; Code; Communities; Community Services; Databases; Docking; Evaluation; Expert Systems; Goals; Laboratories; Lead; Libraries; Ligands; Location; Methods; Modeling; Molecular; Online Systems; Pharmaceutical Preparations; Proteins; Reagent; Research Personnel; Screening procedure; Site; Specialist; Structure; System; Techniques; Testing; Zinc; abstracting; base; cheminformatics; clinically relevant; drug discovery; improved; interest; novel; protein function; protein structure; public health relevance; success; tool; tool development; virtual; web interface

DESCRIPTION (provided by applicant): Abstract Virtual screening is the most practical method to leverage ligand and protein structures for lead discovery. Unfortunately, both ligand-based and docking techniques are inaccessible to most investigators. A key result from the first period, the ZINC database, has lowered the barrier to entry for docking through public access 3D screening libraries. The Similarity Ensemble Approach (SEA), also developed in the first period, has shown early promise for ligand-based target identification. Still, virtual screening remains difficult to use for most investigators. To lower these barriers still further we will develop databases and automated tools for use by the general community, and investigate their usefulness in proof-of-concept studies. The specific aims are: 1. To develop databases that derive from and enable virtual screening. A. We will develop a database of pre-calculated docking hits that can simply be looked up and purchased for about 1,000 protein targets. This will rely on automated tools for docking, hit evaluation, and comparisons among targets (aim 2). We will also improve databases for virtual screening developed in the first period. These include: B. Expanding ZINC, adding more commercially available compounds and improving the structures represented in it. C. Improving the robustness of DUD, a general benchmarking set for virtual screening. D. Expanding the database of high energy intermediates (HEI) developed in the first period for protein function prediction. 2. To create simple web-based tools for ligand-based and protein-based virtual screening. We will develop and refine two web-based tools to enable non-specialists to discover ligands for their targets. A. For structure-based docking, a simple-looking web-interface to docking that guides the user, selects parameters, calibrates the model, and manages the calculation on our cluster. We will develop automated tools to evaluate the reliability of docking results. B. The second virtual screening tool is ligand based, for use when the structure of the target is unknown but many ligands are available, or when one wants to explore alternate targets for a known drug or reagent. We further develop a novel cheminformatic method SEA introduced in the last period to predict target relationships and off-target effects. This approach has had precocious success in identifying interesting polypharmacology, and we will also use it ourselves to predict- and-test off-target, clinically relevant effects of 50 to 100 FDA drugs, and identify the targets of the ~10% of FDA drugs for which a target is unknown. PUBLIC HEALTH RELEVANCE: Virtual screening is widely used to discover new molecular leads for drug discovery and reagents to understand biological processes. Unfortunately, the technique remains difficult to use, and has thus been restricted to a few expert laboratories, limiting its usefulness. In this proposal, we create databases and tools to bring virtual screening to a wide biological audience, much expanding its impact and usefulness.

描述（由申请人提供）：抽象虚拟筛选是利用配体和蛋白质结构进行铅发现的最实用方法。不幸的是，对于大多数研究人员来说，基于配体的技术和对接技术都是无法访问的。第一阶段的关键结果是锌数据库，它降低了通过公共访问3D筛选库对接进入的障碍。同样在第一阶段开发的相似性合奏方法（SEA）已显示出基于配体的靶标识别的早期希望。尽管如此，对于大多数研究人员来说，虚拟筛查仍然很难使用。为了进一步降低这些障碍，我们将开发数据库和自动化工具，以供一般社区使用，并研究它们在概念证明研究中的有用性。具体目的是：1。开发来自并启用虚拟筛选的数据库。答：我们将开发一个数据库的预计对接命中率的数据库，该数据库可以简单地以大约1,000个蛋白质靶标购买并购买。这将依靠自动化工具进行对接，命中评估和目标之间的比较（AI​​M 2）。我们还将改善在第一阶段开发的虚拟筛选的数据库。其中包括：B。扩展锌，添加更多的市售化合物并改善其代表的结构。 C.提高DUD的鲁棒性，这是虚拟筛选的一般基准测试。 D.扩展在蛋白质功能预测的第一阶段开发的高能中间体（HEI）数据库。 2。创建简单的基于Web的工具，用于基于配体和基于蛋白质的虚拟筛选。我们将开发和完善两个基于网络的工具，以使非专家可以发现其目标的配体。答：对于基于结构的扩展坞，是一个简单的Web接口，用于导向用户，选择参数，校准模型并管理我们群集上的计算。我们将开发自动化工具来评估对接结果的可靠性。 B.第二个虚拟筛选工具是基于配体的，供靶标的结构未知，但有许多配体可用，或者当一个人想探索已知药物或试剂的替代靶标时。我们进一步开发了一种新型的化学方法，在上一个时期引入了用于预测目标关系和靶向效果的新型SEA。这种方法在识别有趣的多种药理学方面取得了早熟的成功，我们还将自己使用它来预测50至100 FDA药物的临床和临床相关效果，并识别出约10％的FDA药物的靶标的靶标的靶标的目标。 公共卫生相关性：虚拟筛查被广泛用于发现药物发现的新分子铅和试剂以了解生物学过程。不幸的是，该技术仍然很难使用，因此仅限于一些专家实验室，从而限制了其实用性。在此提案中，我们创建了数据库和工具，将虚拟筛查带给广泛的生物学受众，从而大大扩展了其影响和实用性。