A Web-Based Automatic Virtual Screening System

基于网络的自动虚拟筛选系统

• 批准号: 8249884
• 负责人: John J. Irwin
• 金额: $ 30.33万
• 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 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8249884
• 关键词: Benchmarking; Biological; Biological Process; Biology; 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; Structure; System; Techniques; Testing; abstracting; base; cheminformatics; clinically relevant; drug discovery; improved; interest; novel; protein function; protein structure; success; tool; tool development; virtual; web interface

Project Summary / 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.

项目摘要 /摘要 虚拟筛查是利用配体和蛋白质结构的最实用方法 发现。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。一个 第一阶段的关键结果是锌数据库，已降低了进入的进入的障碍 公共访问3D筛选库。相似性合奏方法（SEA）也在第一个 时期，已经显示出基于配体的靶标识别的早期希望。尽管如此，虚拟筛选仍然很困难 用于大多数调查员。为了进一步降低这些障碍，我们将开发数据库并自动化 一般社区使用的工具，并研究了它们在概念验证研究中的有用性。这 具体目的是： 1。开发来自源自虚拟筛选的数据库。答：我们将发展一个 可以简单地查找并购买约1,000个蛋白质的预计对接命中的数据库 目标。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。这些包括：B。 扩展锌，添加更多市售的化合物并改善代表的结构 在其中。 C.提高DUD的鲁棒性，这是虚拟筛选的一般基准测试。 D.扩展 高能中间体（HEI）的数据库在蛋白质功能预测的第一阶段开发。 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，以使非专家能够发现目标的配体。一个。 对于基于结构的码头，一个看上去很简单的网络接口，用于导致用户的对接，请选择 参数，校准模型并管理我们集群上的计算。我们将开发自动化 评估对接结果可靠性的工具。 B.第二个虚拟筛选工具是基于配体的，用于 当目标结构未知但有许多配体时使用，或者当一个人想要时使用 探索已知药物或试剂的替代靶标。我们进一步开发了一种新颖的化学信息法 海上在最后一个时期引入，以预测目标关系和脱靶效应。这种方法已经 早熟在识别有趣的多药理学方面的成功，我们还将自己使用它来预测 - 和测试脱离目标，临床上相关的50至100次FDA药物的影响，并确定约10％的靶标 靶标未知的FDA药物。