Abstract
Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual screening campaigns with a view to repurposing known drugs as potential allosteric (and/or orthosteric) ligands for the hM2 muscarinic subtype which was indeed resolved in complex with an allosteric modulator thus allowing a precise identification of this binding cavity. First, a docking protocol was developed and optimized based on binding space concept and enrichment factor optimization algorithm (EFO) consensus approach by using a purposely collected database including known allosteric modulators. The so-developed consensus models were then utilized to virtually screen the DrugBank database. Based on the computational results, six promising molecules were selected and experimentally tested and four of them revealed interesting affinity data; in particular, dequalinium showed a very impressive allosteric modulation for hM2. Based on these results, a second campaign was focused on bis-cationic derivatives and allowed the identification of other two relevant hM2 ligands. Overall, the study enhances the understanding of the factors governing the hM2 allosteric modulation emphasizing the key role of ligand flexibility as well as of arrangement and delocalization of the positively charged moieties.
Highlights
Drug repositioning represents an efficient strategy to find novel therapeutic applications for old molecules [1,2,3,4]
The muscarinic receptors represent a clear example since they are implicated in several truly debilitating diseases, but the use of drugs variously acting on muscarinic receptors is woefully limited by the fact that even those molecules which reached the market are associated with several significant central and peripheral side effects
The unsuitable profile of these ligands is due to their poor selectivity by which they bind all muscarinic receptors subtypes, a problem that can be ascribed to the very high degree of conservation among the key residues lying in the orthosteric cavity of the five muscarinic subtypes
Summary
Drug repositioning represents an efficient strategy to find novel therapeutic applications for old molecules [1,2,3,4]. Previous studies suggested that average and range values are suitable descriptors to explore such a binding space and can find fruitful applications for developing predictive models Notwithstanding these encouraging results, the application of the binding space descriptors to optimize the performances of virtual screening campaigns was never investigated because docking simulations for virtual screening usually generate only one pose per ligand, a choice clearly explainable for minimizing computational costs. Both equations including average and range values comprise two score averages and one range value which may encode for the entropic factors as discussed above To summarize these preliminary docking simulations, one may conclude that: (1) the utilized docking strategies should prove successful in recognizing potential allosteric modulators in the future VS campaigns; (2) averaging docking scores from multiple binding poses appears useful when using non-optimal protein structures; (3) the range values induce limited, but constant enhancements presumably since they encode for the often disregarded entropic factors. FFiigguurree22..PPuutatatitviveeccoommpplelexxeessaassccoommppuutteeddbbyyddoocckkiinnggssiimmuullaattiioonnssffoorr ((yyeellllooww ccaarrbboonn aattoommss,, A(A))WW8844, , ((aazzuurree ccaarrbboonn aattoommss,, B(B))ddeeqquuaalilniniuiumm, ,((ppuurrppleleccaarrbboonn aattoommss,, C(C) )kkeetotoccoonnaazzooleleaanndd((ggrreeeenn ccaarrbboonn aattoommss,,D(D) )chchlolorhrhexexididinineewwitihthininththeehhMM22bbininddininggsistietessininitistsaactcitviveestsatatete(P(PDDBBIdId: :4M4MQQTT).).InInaallllfifigguureress, , tthheellooooppbbeettwweeeennrreessiidduueess441111aanndd441188wwaass nnoott ddiissppllaayyeedd ffoorr ssaakkee ooff ccllaarriittyy
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
