A Web-Based Automatic Virtual Screening System

项目总结/摘要 一个长期的目标是将化学试剂引入生物学，扩展工具的发展， 社区越来越依赖：ZINC（http：zinc.docking.org），DUDE（http：dude.docking.org），DOCK 其中包括Blaster（http：blaster.docking.org）和SEA（http：sea.docking.org）。第二个目标是探索 药理学的配体为基础的组织的基本基础和影响，利用它来预测 生物相关的多药理学，我们认为在该领域的第一次。 1.新的公共工具将化学带入生物学。A.我们开发的ZINC工具，使人们能够输入一个 靶向并找到已知的所有可用试剂，或输入分子以找到其已知和预测的靶点， 测试其中的几个。B。目标在路径和网络中运作。我们介绍的工具，让一个 在由化学和生物信息学相似性组织“可点击”网络中从靶到靶的岛跳跃。 C.通过将化学信息学直接引入DOCK Blaster，研究人员可以搜索他们的类似物列表， 支架、功能基团及其对接的相互作用。D.我们开发了一个库， 表型筛选问题，靶ID，通过将所有文库化合物预先注释到靶，每个靶 具有两个或更多个注释的正交分子（Novartis & Sigma-Aldrich）。当这种正交 分子在筛选中共享一个表型，它表明了潜在的目标。 2.比较和结合基于配体，基于结构和生物信息学的相似性。联系目标 通过配体相似性揭示的关联与生物信息学所建议的非常不同。这是 令人欣慰但又令人困惑在这里，我们A。将生物信息学目标网络与预测的目标网络进行全面比较 通过配体相似性初步结果表明，这些网络大多是正交的，但有有趣的， 重叠领域（下文C部分探讨）。B。理解相同的约束力的结构基础 配体的“无关”蛋白质，我们比较了数百个复合物的x射线结构，其中两个 使用广泛使用的基于结构的位点比较，不同的蛋白质与完全相同的配体结合 程序.这些程序能否识别出不相关蛋白质中的结合位点实际上是相似的？如何 蛋白质识别相同的配体官能团的方式有很多种？C.在那些生物和 化学信息学邻域重叠，生物信息学暗示疾病中的靶对，而 化学信息学表明，该对可以被试剂共调节。对于这些对，共享 多药理学在功能上是有意义的。我们预测和测试50。 虽然这些目标雄心勃勃，但其可行性得到了广泛的初步成果的支持。

Project Summary / Abstract A long-term goal is to bring chemical reagents to biology, extending the development of tools on which the community increasingly depends: ZINC (http://zinc.docking.org), DUDE (http://dude.docking.org), DOCK Blaster (http://blaster.docking.org) and SEA (http://sea.docking.org) among them. A second goal explores the fundamental bases and implications of a ligand-based organization of pharmacology, leveraging it to predict biologically-relevant polypharmacology, we argue for the first time in the field. 1. New public tools to bring chemistry to biology. A. We develop ZINC tools that enable one to input a target and find all available reagents known for it, or to input a molecule to find its known and predicted targets, testing several of these. B. Targets operate in pathways and networks. We introduce tools that allow one to island hop from target-to-target in “clickable” networks organized by both chemo- and bio-informatic similarity. C. By bringing chemoinformatics directly into DOCK Blaster, investigators can search their hit lists for analogs, scaffolds, functional groups, and their docked interactions. D. We develop a library that addresses the key problem of phenotypic screening, target ID, by pre-annotating all library compounds to targets, with each target having two or more orthogonal molecules annotated (with Novartis & Sigma-Aldrich). When such orthogonal molecules share a phenotype in a screen, it suggests the underlying target. 2. Comparing and combining ligand-based, structure-based & bioinformatic similarity. Linking targets by ligand similarity reveals associations very different from what bioinformatics would suggest. This is gratifying but puzzling. Here we A. Comprehensively compare bioinformatic target networks to those predicted by ligand similarity. Preliminary results suggest that these networks are mostly orthogonal but have intriguing areas of overlap (explored in C, below). B. To understand the structural basis for the binding of identical ligands by “unrelated” proteins, we compare the x-ray structures of hundreds of complexes where two dissimilar proteins bind exactly the same ligands, using widely-used, structure-based site comparison programs. Can these programs recognize the binding sites in the unrelated proteins as, in fact, similar? How many ways can proteins recognize the same ligand functional groups? C. In the areas where the bio- and chemoinformatics neighborhoods overlap, the bioinformatics implicates pairs of targets in a disease, while chemoinformatics suggest that that the pair can be co-modulated by a reagent. For these pairs, shared polypharmacology is functionally meaningful. We predict and test 50. Whereas these goals are ambitious, their plausibility is supported by extensive preliminary results.