A Web-Based Automatic Virtual Screening System

描述（由申请人提供）：化学生物学的两个首要目标是找到每种蛋白质的配体，并确定表型活性化合物的靶标。在过去十年中，这些目标一直在以经验为基础进行追求。我们相信，这两个企业都强烈呼吁进行计算发现。该项目的长期目标是通过针对所有结构上可寻址的靶点进行对接筛选，并通过开发鉴定介导表型生物活性的靶点的工具，将化学引入大型生物学家社区。第一个目标是通过开发复合库、基准测试集和基于网络的工具来实现的，这些工具从根本上降低了进入门槛。第二个目标，配体的目标识别，是通过开发新的化学信息学方法和实验测试来实现的。 1.使用化学信息学和对接来阐述ZINC的活性预测，并将靶标与疾病联系起来。我们将开发和部署公共访问工具，使生物学家能够询问化学生物学。1. ZINC平台中的工具将商业上可获得的化合物与其已知和可能的目标相关联，并且相应地将目标与其已知或可能的配体相关联。2.一种新的工具DxTRx将靶点与它们调节的表型和疾病联系起来。3.我们将使用对接来预先计算存在结构的10，000个相关靶标的高分配体列表。这些命中列表将提供给社区，并将成为我们自己的目标-目标连锁研究的基础。简而言之，我们将开发一个集成的工具集，使研究人员能够从目标化合物的表型目标出发，在许多生物学领域的积极兴趣。 2.从配体预测靶点（SEA）。我们将进一步利用SEA进行药理学研究，并对核心方法进行改进。我们将A。使用SEA重组目标家族树，如激酶，GPCR和离子通道，通过配体而不是序列相似性。早期的工作预示着一个戏剧性的重新分支，导致可测试的假设，新的目标协会。B。研究配体相似性的蛋白质结构背景。SEA现在通过拓扑结构比较配体，并使用统计引擎进行显著性分析。对于许多目标，结构存在，并且可以将受体上下文添加到这些计算中。C.恢复受体也可能解决SEA的弱点，即对已知配体的依赖。利用目标1中的工作，我们将比较蛋白质组范围内的对接命中列表，寻找新的靶标-靶标关联。一个新的应用是D.我们将使用SEA来预测在整个生物体表型筛选中具有活性的化合物的靶标，扩展现有的合作。

DESCRIPTION (provided by applicant): Two overarching goals in chemical biology are finding ligands for every protein, and identifying the targets underlying phenotypically active compounds. For the last decade, these goals have been pursued empirically. We believe that there is a strong call for computational discovery in both enterprises. It is the long- term goal o this project to bring chemistry to a large community of biologists, by enabling docking screens against all structurally addressable targets, and by developing tools that identify the targets mediating phenotypic biological activity. The first aim is met by developing compound libraries, benchmarking sets, and web-based tools that radically reduce barriers to entry. The second aim, target identification for ligands, is met by developing new chemoinformatic methods and testing them experimentally. 1. To elaborate ZINC with activity predictions using cheminformatics and docking, and link targets to disease. We will develop and deploy public access tools that enable biologists to interrogate chemistry for biology. 1. Tools in the ZINC platform will link commercially available compounds to their known and likely targets and, correspondingly, link targets to their known or likely ligands. 2. A new tool, DxTRx, connects targets to the phenotypes and diseases that they modulate. 3. We will use docking to precalculate high-scoring ligand lists for 10,000 relevant targets for which a structure exists. These hit-lists will be made available to the community, and will be substrates for our own target-target linkage studies. In short, we will develop an integrated tool set to allow an investigator to proceed from target ¿¿ compound ¿¿ phenotype¿¿target in many areas of biology of active interest. 2. Predicting targets from ligands (SEA). We will further exploit SEA to interrogate pharmacology, and to improve the core method. We will A. Use SEA to reorganize target-family trees, such as for kinases, GPCRs, and ion channels, by ligand rather than sequence similarity. Early work portends a dramatic re- arborization, leading to testable hypotheses about new target-associations. B. Investigate a protein structure context for the ligand similarities. SEA now compares ligands by topology, with a statistical engine for significance. For many targets, structures exist, and it may be possible to add a receptor context to these calculations. C. Bringing back the receptor may also address a weakness of SEA, its dependence on known ligands. Exploiting work in aim 1, we will compare the proteome-wide docking hit lists, seeking new target- target associations. A new application is to D. We will use SEA to predict the targets of compounds active in whole organism phenotypic screens, expanding on existing collaborations.