A Web-Based Automatic Virtual Screening System

描述(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潜在目标。在过去的十年里，这些目标一直是凭经验实现的。我们认为，在这两个企业中都有强烈的计算机发现呼声。该项目的长期目标是将化学带入一个庞大的生物学家社区，方法是针对所有结构上可寻址的靶标进行对接筛选，并开发识别介导表型生物学活动的靶标的工具。第一个目标是通过开发复合库、基准测试集和基于Web的工具来实现的，这些工具可以从根本上降低进入门槛。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并进行实验测试来实现的。1.利用化学信息学和对接技术，详细阐述锌的活性预测，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，使生物学家能够为生物学询问化学。1.锌平台中的工具将把商业上可用的化合物与其已知和可能的目标联系起来，并相应地将目标与其已知或可能的配体联系起来。2.一种名为DxTRx的新工具，将靶标与它们调节的表型和疾病联系起来。3.我们将使用对接为存在结构的10,000个相关目标预计算高分配体列表。这些暗杀名单将提供给社会，并将成为我们自己的目标-目标联系研究的基础。简而言之，我们将开发一套集成的工具，使研究人员能够从许多生物学活跃领域的目标化合物表型目标出发。2.从配体预测目标(SEA)。我们将进一步开发SEA来审问药理，并改进核心方法。我们将使用SEA根据配基而不是序列相似性来重组靶家族树，例如激酶、GPCRs和离子通道。早期的工作预示着一种戏剧性的重新划分，导致关于新的目标关联的可检验的假说。B.研究蛋白质结构背景下的配基相似性。SEA现在通过拓扑比较配体，并使用统计引擎来确定重要性。对于许多靶点来说，结构是存在的，有可能在这些计算中加入受体的背景。C.找回受体还可能解决SEA的一个弱点，即它对已知配体的依赖。利用AIM 1中的工作，我们将比较蛋白质组范围的对接命中列表，寻找新的靶标-靶标关联。一个新的应用是D。我们将使用SEA来预测全生物表型筛选中活性化合物的靶标，扩大现有的合作。

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.