Pharmacophore based virtual screening, synthesis and SAR of novel inhibitors of Mycobacterium sulfotransferase

Mining of potential dipeptidyl peptidase-IV inhibitors as anti-diabetic agents using integrated in silico approaches

The dipeptidyl peptidase-IV (DPP-IV) family of receptors possesses a large binding cavity that imparts promiscuity for number of ligand binding which is not common to other receptors. This feature increases the challenge of using computational methods to identify DPP-IV inhibitors, therefore using both pharmacophore and structure-based screening seems to be a reliable approach. Mining of novel DPP-IV inhibitors by integrating both of these in silico techniques was reported. Pharmacophore model (Model_008) obtained from structurally diverse reported compounds was used as a template for screening of MolMall database followed by structure-based screening against PDB ID: 5T4E. After absorption, distribution, metabolism and excretion (ADME) analysis of shortlisted compounds, consensus docking and molecular mechanics/generalized born surface area studies were carried out. The results of the docking studies obtained were comparable to that of the reference ligand. Out of nine hits identified, only one hit (ID MolMall-20062) was available which was procured through exchange program. Molecular dynamic simulation studies of the procured hit revealed its good selectivity and stability in DPP-IV binding pocket and interactions observed with important amino acids viz., Trp629, Lys544 and Arg125. Biological testing of the compound MolMall-20062 showed promising DPP-IV inhibition activity with IC₅₀: 6.2 µM. Compound MolMall-20062 could be taken as a good lead for the development of DPP-IV inhibitors.AbbreviationsADMEabsorption, distribution, metabolism and excretionChEBIchemical entities of biological interestDPP-IVdipeptidyl peptidase IVDISCOtechdistance comparisonsHTVShigh throughput virtual screeningMDmolecular dynamicsMM-GBSAmolecular mechanics-generalized born surface areaOGTToral glucose tolerance testPBVSpharmacophore-based virtual screeningPDBprotein data bankRMSDroot mean square deviationROCreceiver operating characteristicsSPstandard precisionSBVSstructure-based virtual screeningVSvirtual screeningXPextra precisionCommunicated by Ramaswamy H. Sarma.

Stereoselective synthesis of E-3-(arylmethylidene)-5-(alkyl/aryl)-2(3H)-furanones by sequential hydroacyloxylation-Mizoroki–Heck reactions of iodoalkynes

Stereoselective hydroacyloxylation of iodoalkynes with β-aryl cinnamic acids and subsequent Mizoroki–Heck reaction provides an efficient route to substituted 2(3H)-furanones.

A simple mathematical approach to the analysis of polypharmacology and polyspecificity data

There many possible types of drug-target interactions, because there are a surprising number of ways in which drugs and their targets can associate with one another.  These relationships are expressed as polypharmacology and polyspecificity.  Polypharmacology is the capability of a given drug to exhibit activity with respect to multiple drug targets, which are not necessarily in the same activity class. Adverse drug reactions ('side effects') are its principal manifestation, but polypharmacology is also playing a role in the repositioning of existing drugs for new therapeutic indications.  Polyspecificity, on the other hand, is the capability of a given target to exhibit activity with respect to multiple, structurally dissimilar drugs.  That these concepts are closely related to one another is, surprisingly, not well known.  It will be shown in this work that they are, in fact, mathematically related to one another and are in essence 'two sides of the same coin'.  Hence, information on polypharmacology provides equivalent information on polyspecificity, and vice versa. Networks are playing an increasingly important role in biological research. Drug-target networks, in particular, are made up of drug nodes that are linked to specific target nodes if a given drug is active with respect to that target.  Such networks provide a graphic depiction of polypharmacology and polyspecificity.  However, by their very nature they can obscure information that may be useful in their interpretation and analysis.  This work will show how such latent information can be used to determine bounds for the degrees of polypharmacology and polyspecificity, and how to estimate other useful features associated with the lack of completeness of most drug-target datasets.