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为每种蛋白质找到配体，并确定表型活性化合物的目标。在过去的十年里，这些目标一直是经验主义的追求。我们相信，在这两个企业中都有对计算发现的强烈需求。这个项目的长期目标是通过对所有结构上可寻址的靶标进行对接筛选，并通过开发识别介导表型生物活性的靶标的工具，将化学带到大型生物学家社区。第一个目标是通过开发复合库、基准测试集和基于web的工具来实现，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. 利用化学信息学和对接技术详细阐述锌的活性预测，并将靶点与疾病联系起来。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. 锌平台上的工具将把商业上可用的化合物与它们已知的和可能的靶标连接起来，相应地，将靶标与它们已知的或可能的配体连接起来。2. 一种新的工具DxTRx将靶标与它们所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一个结构存在的10,000个相关目标的高分配体列表。这些目标清单将提供给社区，并将成为我们自己的目标-目标联系研究的基础。简而言之，我们将开发一个集成的工具集，允许研究者从靶标-化合物-表型-靶标在许多活跃的生物学领域进行研究。2. 从配体预测目标（SEA）。我们将进一步利用SEA来询问药理学，并改进核心方法。我们将A.使用SEA通过配体而不是序列相似性来重组靶标家族树，例如激酶、gpcr和离子通道。早期的工作预示着一个戏剧性的再树化，导致新的目标关联的可测试的假设。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比较。对于许多目标，存在结构，并且可能在这些计算中添加受体上下文。C.恢复受体也可以解决SEA的弱点，即它对已知配体的依赖。利用目标1的工作，我们将比较蛋白质组范围内的对接列表，寻找新的靶标关联。一个新的应用是D.我们将使用SEA来预测整个生物体表型筛选中活性化合物的靶标，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.