Mining Natural-Products Screening Data for Target-Class Chemical Motifs

Abstract

In this article, we describe two complementary data-mining approaches used to characterize the GlaxoSmithKline (GSK) natural-products set (NPS) based on information from the high-throughput screening (HTS) databases. Both methods rely on the aggregation and analysis of a large set of single-shot screening data for a number of biological assays, with the goal to reveal natural-product chemical motifs. One of them is an established method based on the data-driven clustering of compounds using a wide range of descriptors, 1 whereas the other method partitions and hierarchically clusters the data to identify chemical cores. 2,3 Both methods successfully find structural scaffolds that significantly hit different groups of discrete drug targets, compared with their relative frequency of demonstrating inhibitory activity in a large number of screens.We describe how these methods can be applied to unveil hidden information in large single-shot HTS data sets. Applied prospectively, this type of information could contribute to the design of new chemical templates for drug-target classes and guide synthetic efforts for lead optimization of tractable hits that are based on natural-product chemical motifs.Relevant findings for 7TM receptors (7TMRs), ion channels, class-7 transferases (protein kinases), hydrolases, and oxidoreductases will be discussed.

Systematic Analysis and Prediction of the Target Space of Bioactive Food Compounds: Filling the Chemobiological Gaps

“…The presence of large adjusted residuals in some combinations could be related to the presence in the compound classes of some selective "privileged scaffolds", as those described from the analysis of ChEMBL data, 53 and for natural products from proprietary screening data. 54 The compound classes used here, however, are not restricted to single scaffolds or substructures, and therefore, other factors could be of importance in this regard. As a matter of fact, the scaffolds identified in the current work (Figure 10 and Table S5) are the most significant ones for each target class versus chemical class combination and thus provide an initial glimpse on the chemical diversity of the involved chemotypes and their relation to the activity.…”

A Systematic Analysis and Prediction of the Target Space of Bioactive Food Compounds: Filling the Chemobiological Gaps

“…Besides providing target hypothesis , the predicted interactions were further analyzed to identify putative "favored" vs "disfavored" target vs compound class combinations, as displayed in Fig 9B . The presence of large adjusted residuals in some combinations could be related to the presence in the compound classes of some selective "privileged scaffolds", as those described from the analysis of ChEMBL data, 54 and for natural products from proprietary screening data. 55 The compound classes used here, however, are not restricted to single scaffolds or substructures, and therefore other factors could be of importance in this regard. As a matter of fact, the scaffolds identified in the current work (Figure 10 and Table S5) are the most significant ones for each target class vs chemical class combination, and thus provide an initial glimpse on the chemical diversity of the involved chemotypes and their relation to the activity.…”

Creating and screening natural product libraries

“…122 An additional body of literature assessing which classes of both biochemical targets and chemical scaffolds have been most and least successful for HTS development has also recently emerged. 121,[123][124][125] These "target-class" assessments highlight potential limitations of a target or scaffold; but may not predict the behavior of either a novel target or a wellannotated target in a novel assay system. The emergent nature of both ligandability and target-class assessments suggests that due diligence prior to undertaking assay development for a biochemical screen is critical.…”