Abstract
This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.
Highlights
New drugs can be designed via traditional receptor structurebased virtual screening, which enables the discovery of bioactive compounds that bind the target protein, but they can originate from reverse virtual screening, which finds the unknown protein targets of active compounds or additional targets of existing drugs
In this review article, based on previous studies, we selected the three most commonly used types of reverse screening methods, i.e., methods based on shape similarity, pharmacophore modeling and molecular docking, and provided a detailed and comprehensive introduction, including a description of the principles underlying each method and a systematic classification of software, online services, and databases
We collected most the articles related to the application of computer-aided target reverse screening prediction published since 2000 and analyzed the possible relationships or correlations between compound structures and screening methods by using cluster analysis
Summary
New drugs can be designed via traditional receptor structurebased virtual screening, which enables the discovery of bioactive compounds that bind the target protein, but they can originate from reverse virtual screening, which finds the unknown protein targets of active compounds or additional targets of existing drugs (drug repositioning; Hurle et al, 2013). Among the 84 drug products introduced to the market in 2013, new indications of existing drugs accounted for 20%, implying that drug repositioning plays a key role in drug discovery (Graul et al, 2014; Li J. et al, 2016). With the continuous development of Big Data and computational techniques, computer-aided reverse screening methods are playing an increasingly important role in the prediction of the off-target effects and side effects of drugs as well as in drug repositioning (Rognan, 2010; Liu et al, 2014; Schomburg and Rarey, 2014)
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