Abstract

Inherent biological viability and diversity of natural products make them a potentially rich source for new therapeutics. However, identification of bioactive compounds with desired therapeutic effects and identification of their protein targets is a laborious, expensive process. Extracts from organism samples may show desired activity in phenotypic assays but specific bioactive compounds must be isolated through further separation methods and protein targets must be identified by more specific phenotypic and <em>in vitro</em> experimental assays. Still, questions remain as to whether all relevant protein targets for a compound have been identified. The desire is to understand breadth of purposing for the compound to maximize its use and intellectual property, and to avoid further development of compounds with insurmountable adverse effects. Previously we developed a Virtual Target Screening system that computationally screens one or more compounds against a collection of virtual protein structures. By scoring each compound-protein interaction, we can compare against averaged scores of synthetic drug-like compounds to determine if a particular protein would be a potential target of a compound of interest. Here we provide examples of natural products screened through our system as we assess advantages and shortcomings of our current system in regards to natural product drug discovery.

Highlights

  • Natural products are receiving renewed interest as potential sources for therapeutic agents since the number of new drugs discovered has dropped in recent years in spite of the growth in available synthetic compound libraries for drug discovery campaigns

  • Advanced drug discovery techniques developed in recent years to support high-throughput screening (HTS) that primarily used synthetic compounds can benefit new drug discovery efforts that focus on natural products

  • Virtual Target Screening (VTS) was originally developed and run on a Dell Precision 490 workstation running Fedora 8 Linux with dual Xeon 3.06 GHz processors, 4 GB RAM and a 250 GB hard drive. This was sufficient when running one or a few molecule of interest (MOI) but, for the larger sets of MOIs for the 67 National Cancer Institute (NCI) natural products and the 87 Center for Drug Discovery and Innovation (CDDI) natural products, we incorporated execution on a local cluster we have developed in the Virtual Screening & Molecular Modeling Core at H

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Summary

Introduction

Natural products are receiving renewed interest as potential sources for therapeutic agents since the number of new drugs discovered has dropped in recent years in spite of the growth in available synthetic compound libraries for drug discovery campaigns. Advanced drug discovery techniques developed in recent years to support high-throughput screening (HTS) that primarily used synthetic compounds can benefit new drug discovery efforts that focus on natural products. The Center for Drug Discovery and Innovation (CDDI) provides shared resources to USF researchers [1]. Among these resources the CDDI provides natural product libraries of extracts, sub-fractions and individual compounds for screening in drug discovery campaigns. This includes primary therapeutic targets and potential alternate targets for repurposing, or unintended targets with potential detrimental interactions that could lead to adverse reactions

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