Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

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.

Discovery of NewS‐Glycosides andN‐Glycosides of Pyridine‐biphenyl System with Antiviral Activity and Induction of Apoptosis inMCF7 Cells

“…The final compounds behave in special manners, as the epimeric isomers are not near each other in activity (compound 4g versus 4c , compound 4f versus 4b ; and compound 5h versus 4d , compound 5f versus 4b ) and also variations of substituent on pyridine ring play an important role in activity. To gain insight about structure–activity relationship and to understand the compounds' activity, rapid overlay of chemical structures ( ROCS ) was employed . ROCS are a shape‐based superposition method and is used to perceive similarity between molecules based on their three‐dimensional shape.…”

The Acid/Base Profile of a Large Food Chemical Database

“…A next step of this work that was focused on discussing the acid/base profile of food chemicals, is to explore systematically the mechanism of action of food chemicals or protein targets affected by food chemicals. These can be approached using computational tools for reverse screening also known as in silico target fishing …”

“…These can be approached using computational tools for reverse screening also known as in silico target fishing. [26,27] 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56…”

Improving inverse docking target identification with Z‐score selection

“…Application of molecular docking methods in protein target identification (frequently also referred to as inverse virtual screening; Chen & Zhi, ) seems straightforward but is plagued by shortcomings. Most notably, molecular docking methods have been developed to identify a number of potential ligands for a given target by screening thousands to millions of ligands against a single protein (Huang et al., ; Xu, Huang, & Zou, ). It has become apparent, however, that molecular docking methods are not particularly well equipped to identify a small number of potential targets (from a large set of possible targets) for a given ligand.…”

“…The challenge of identifying true positives from a pool of potential targets has encouraged the development of various analysis methods and webserver, predominantly with a focus on drug side‐effects detection. Among those protocols, invdock (Li et al., ), tarfisdock (Yang, Luo, Chen, Xing, & He, ), sepresa (Wang, Chu, Chen, & Lin, ), and idtarget (Gao et al., ) are widely known molecular docking target identification servers (Huang et al., ; Xu et al., ), where each server selects potential interactive targets of the users’ query compound from its own protein library. invdock is the earliest version of a target identification server, and currently, the database contains 9,000 proteins and nucleic acids for screening.…”