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Pharmacophore fingerprint analysis of small molecules and proteins for virtual high throughput screening.

机译：用于虚拟高通量筛选的小分子和蛋白质的药理学指纹分析。

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摘要

We explored the application of 3-point pharmacophore fingerprints for characterizing small molecules and proteins for protein-based virtual high-throughput screening. Virtual high-throughput screening is a new approach attracting increasing levels of interest in pharmaceutical industry as a productive and cost-effective technology in the search for novel lead molecules.; We characterized the chemical and spatial properties of small molecules and proteins by the flexible small molecule fingerprints and active site sitepoint fingerprints, respectively. We developed the conformation generator CONGEN/MACROMODEL for fast and automatic conformational search for the flexible small molecule fingerprint generation. For proteins, Matt Evans developed the active site sitepoint fingerprints to represent active sites in “ligand space.”; Using the MySQL database management system, we built a drug-targeted protein and ligand database based on the 3D structures from the Protein Data Bank, and the MDDR drug molecule database from the MDL Drug Data Report. The database is used to build virtual screening models for all activity classes, and also for cluster analysis of proteins and small molecules.; We found our 3D fingerprints are useful in clustering small molecules and proteins into biological classes using agglomerative hierarchical clustering methods. In terms of adjusted Rand index, the average linkage, complete linkage, and Ward's methods are better than single linkage and centroid methods. Detailed inspection of results show that the cluster analysis based on our fingerprints is an effective way to partition small molecules and proteins into activity classes.; In the virtual screening study, we compared protein class sitepoint fingerprints generated from the proteins in the database against flexible small molecule fingerprints in the MDDR database. The protein class consensus sitepoint fingerprints and consensus sitepoint-small molecule fingerprints are much better than random selection, and the enrichment rates are higher than those from protein class individual, intersection, and union sitepoint fingerprints.
机译：我们探索了三点药效团指纹图谱在表征小分子和蛋白质以基于蛋白质的虚拟高通量筛选中的应用。虚拟高通量筛选是一种新方法，在生产新颖的前导分子时作为一种生产性和成本效益高的技术吸引了制药行业越来越多的关注。我们分别通过灵活的小分子指纹和活性位点指纹来表征小分子和蛋白质的化学和空间特性。我们开发了构象生成器 CONGEN / MACROMODEL ，用于快速和自动构象搜索，以生成灵活的小分子指纹。对于蛋白质，Matt Evans开发了活性位点指纹，以表示“配体空间”中的活性位。使用MySQL数据库管理系统，我们基于Protein Data Bank的3D结构和MDL Drug Data Report的MDDR药物分子数据库，构建了以药物为靶标的蛋白质和配体数据库。该数据库用于为所有活动类别建立虚拟筛选模型，还用于蛋白质和小分子的聚类分析。我们发现我们的3D指纹可用于使用凝聚层次聚类方法将小分子和蛋白质聚类为生物类别。在调整兰德指数方面，平均联动，完全联动和沃德方法优于单联动和质心方法。详细的结果检查表明，基于我们的指纹进行的聚类分析是将小分子和蛋白质划分为活性类别的有效方法。在虚拟筛选研究中，我们将数据库中蛋白质产生的蛋白质 class 定位点指纹与MDDR数据库中的柔性小分子指纹进行了比较。蛋白质类共有点指纹和共有点-小分子指纹比随机选择要好得多，并且富集率要高于蛋白质类单个，交叉和结合点指纹。

著录项

  • 作者

    Yang, Zheng.;

  • 作者单位

    University of California, Santa Cruz.;

  • 授予单位 University of California, Santa Cruz.;
  • 学科 Chemistry Pharmaceutical.; Chemistry Organic.; Computer Science.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 277 p.
  • 总页数 277
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 药物化学;有机化学;自动化技术、计算机技术;
  • 关键词

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