A Web-Based Automatic Virtual Screening System

描述(由申请人提供)：摘要虚拟筛选是利用配体和蛋白质结构进行铅发现的最实用的方法。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。第一阶段的一个关键成果，锌数据库，降低了通过公共访问3D筛选库对接的门槛。相似集成方法(SEA)也是在第一阶段发展起来的，在基于配体的目标识别方面显示出了早期的前景。尽管如此，对于大多数调查人员来说，虚拟筛查仍然很难使用。为了进一步降低这些障碍，我们将开发供一般社区使用的数据库和自动化工具，并调查它们在概念验证研究中的有用性。其具体目标是：1.开发由虚拟筛查派生并支持虚拟筛查的数据库。答：我们将开发一个预先计算的对接命中数据库，只需查找和购买大约1000个蛋白质靶标即可。这将依赖于自动工具进行对接、命中评估和目标之间的比较(目标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：B.扩大锌，添加更多商业上可用的化合物，并改善其中所代表的结构。C.提高DUD的稳健性，DUD是一个通用的虚拟筛选基准集。D.扩展第一阶段开发的高能中间体(HEI)数据库，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他们目标的配体。答：对于基于结构的对接，是一个简单的Web界面，用于对接，指导用户，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基的，用于靶标结构未知但有许多配基可用时，或当一个人想要探索已知药物或试剂的替代靶点时使用。我们进一步发展了一种新的化学信息学方法SEA，用于预测目标关系和脱靶效应。这种方法在识别有趣的多元药理方面取得了早熟的成功，我们自己也将使用它来预测和测试50到100种FDA药物的非靶点、临床相关的效果，并确定~10%的FDA药物的靶点未知。 公共卫生相关性：虚拟筛查被广泛用于发现新的分子线索，用于药物发现和试剂了解生物过程。不幸的是，这项技术仍然很难使用，因此仅限于几个专家实验室，限制了其用途。在这项提案中，我们创建了数据库和工具，将虚拟筛查带给广泛的生物受众，大大扩大了其影响和用途。

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.