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.

项目总结/摘要 虚拟筛选是利用配体和蛋白质结构寻找铅的最实用的方法 的发现不幸的是，大多数研究人员都无法使用基于配体的技术和对接技术。一 第一阶段的一个关键成果是ZINC数据库，它降低了对接的进入门槛， 公共访问3D筛选库。相似性包围法（SEA），也是在第一个 期间，已经显示出基于配体的靶向鉴定的早期前景。尽管如此，虚拟筛选仍然很困难 供大多数调查人员使用。为了进一步降低这些障碍，我们将开发数据库， 供一般社区使用的工具，并调查它们在概念验证研究中的有用性。的 具体目标是： 1.开发从虚拟筛选衍生并使之成为可能的数据库。A.我们将开发一个 一个预先计算的对接命中数据库，可以简单地查找和购买约1,000个蛋白质 目标的这将依赖于对接、命中评估和目标间比较的自动化工具（aim 2）。我们还将改进第一阶段开发的虚拟筛查数据库。其中包括：B。 扩展ZINC，添加更多商业可用的化合物并改进所代表的结构 C.提高DUD的鲁棒性，这是虚拟筛选的一般基准。D.扩大 第一阶段开发的用于蛋白质功能预测的高能中间体数据库。 2.创建简单的基于网络的工具，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，使非专业人员能够发现其目标的配体。A. 对于基于结构的对接，一个简单的Web界面引导用户对接，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。第二种虚拟筛选工具是基于配体的， 当目标的结构未知但有许多配体可用时，或者当人们想要 探索已知药物或试剂的替代靶点。我们进一步发展了一种新的化学信息学方法， 在上一个时期引入了SEA，以预测目标关系和脱靶效应。这种方法已经 在识别有趣的多药理学方面取得了早熟的成功，我们也将自己使用它来预测- 并测试50至100种FDA药物的脱靶、临床相关作用，并确定约10%的 靶点未知的FDA药物。