The Privileged Chemical Space Predictor (PCSP): A computer program that identifies privileged chemical space from screens of modularly assembled chemical libraries
The Privileged Chemical Space Predictor (PCSP): A computer program that identifies privileged chemical space from screens of modularly assembled chemical libraries
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DOI：
10.1016/j.bmcl.2010.01.017
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发表时间：
2010-02-15
影响因子：
2.7
通讯作者：
Disney, Matthew D.
中科院分区：
文献类型：
--
作者：
Seedhouse, Steven J.;Labuda, Lucas P.;Disney, Matthew D.
模块化组装的组合文库通常用于识别与蛋白质或核酸结合并调节其功能的配体。然而，筛选这些化合物的大部分数据并未有效地用于研究。精细的结构-活性关系（SAR）。如果准确构建 SAR 数据，就可以设计出更有效的粘合剂。在此，我们描述了一种称为特权化学空间预测器（PCSP）的计算机程序，该程序从高通量筛选（HTS）数据中统计确定SAR，然后识别小分子中易于与靶标结合的特征。对特征进行统计显着性评分，并可用于设计改进的第二代化合物或更针对目标的库。该程序的实用性是通过对模块化组装的类肽库的分析来证明的，该类肽库先前已筛选出与来自真菌病原体白色念珠菌的 I 组内含子 RNA 的结合和抑制。 (C) 2010 Elsevier Ltd. 保留所有权利。
Modularly assembled combinatorial libraries are often used to identify ligands that bind to and modulate the function of a protein or a nucleic acid. Much of the data from screening these compounds, however, is not efficiently utilized to de. ne structure-activity relationships (SAR). If SAR data are accurately constructed, it can enable the design of more potent binders. Herein, we describe a computer program called Privileged Chemical Space Predictor (PCSP) that statistically determines SAR from high-throughput screening (HTS) data and then identifies features in small molecules that predispose them for binding a target. Features are scored for statistical significance and can be utilized to design improved second generation compounds or more target-focused libraries. The program's utility is demonstrated through analysis of a modularly assembled peptoid library that previously was screened for binding to and inhibiting a group I intron RNA from the fungal pathogen Candida albicans. (C) 2010 Elsevier Ltd. All rights reserved.