A Web-Based Automatic Virtual Screening System

项目摘要/摘要 虚拟筛选是利用铅的配体和蛋白质结构的最实用的方法 发现号。不幸的是，基于配体的技术和对接技术对大多数研究人员来说都是不可用的。一个 第一阶段的主要成果是锌数据库，降低了进入对接的门槛 公共访问3D放映库。相似集合方法(SEA)，也是在第一个 期间，已经显示出基于配体的目标识别的早期前景。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，我们将开发数据库和自动化 供一般社区使用的工具，并调查其在概念验证研究中的用处。这个 具体目标是： 1.开发派生自虚拟筛选并使其能够进行虚拟筛选的数据库。答：我们将制定一项 预先计算的对接命中数据库，只需约1,000个蛋白质即可轻松查找和购买 目标。这将依赖于自动工具进行对接、命中评估和目标(AIM)之间的比较 2)。我们还将完善第一阶段开发的虚拟筛查数据库。这些措施包括： 扩大锌，添加更多商业上可用的化合物，并改善所代表的结构 在里面。C.提高DUD的稳健性，DUD是一个通用的虚拟筛选基准集。D.扩容 高能中间体(HEI)数据库是在第一阶段建立的，用于蛋白质功能预测。 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 将开发和改进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。一个。 对于基于结构的对接，一个外观简单的Web界面用于指导用户对接，选择 参数、校准模型并在我们的集群上管理计算。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 当目标的结构未知但有许多配基可用时，或当一个人想要的时候使用 为已知的药物或试剂寻找替代靶点。我们进一步发展了一种新的化学信息学方法 SEA在最后一期引入，用于预测目标关系和脱靶效应。这种方法已经有了 在发现有趣的复合药理学方面取得了早熟的成功，我们自己也将用它来预测- 以及-测试50到100种FDA药物的非目标、临床相关效果，并确定~10%的目标 FDA目标不明的药物。

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.