A computational study on role of 6-(hydroxymethyl)-3-[3,4,5-trihydroxy-6-[(3,4,5-trihydroxyoxan-2-yl)oxymethyl]oxan-2-yl]oxyoxane-2,4,5-triol in the regulation of blood glucose level

Anil Kumar N, Sharopov F, Ramírez-Alarcón K, Ruiz-Ortega A, Abdulmajid Ayatollahi S, Valere Tsouh Fokou P, Kobarfard F, Amiruddin Zakaria Z, Iriti M, Taheri Y, Martorell M, Sureda A, N. Setzer W, Durazzo A, Lucarini M, Santini A, Capasso R, Adrian Ostrander E, -ur-Rahman A, Iqbal Choudhary M, C. Cho W, Sharifi-Rad J. Antidiabetic Potential of Medicinal Plants and Their Active Components

Diabetes mellitus is one of the major health problems in the world, the incidence and associated mortality are increasing. Inadequate regulation of the blood sugar imposes serious consequences for health. Conventional antidiabetic drugs are effective, however, also with unavoidable side effects. On the other hand, medicinal plants may act as an alternative source of antidiabetic agents. Examples of medicinal plants with antidiabetic potential are described, with focuses on preclinical and clinical studies. The beneficial potential of each plant matrix is given by the combined and concerted action of their profile of biologically active compounds.

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

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.

In silico identification and screening of CYP24A1 inhibitors: 3D QSAR pharmacophore mapping and molecular dynamics analysis

Vitamin D is a key signalling molecule that plays a vital role in the regulation of calcium phosphate homeostasis and bone remodelling. The circulating biologically active form of vitamin D is regulated by the catabolic mechanism of cytochrome P450 24-hydroxylase (CYP24A1) enzyme. The over-expression of CYP24A1 negatively regulates the vitamin D level, which is the causative agent of chronic kidney disease, osteoporosis and several types of cancers. In this study, we found three potential lead molecules adverse to CYP24A1 through structure-based, atom-based pharmacophore and e-pharmacophore-based screening methods. Analysis was done by bioinformatics methods and tools like binding affinity (binding free energy), chemical reactivity (DFT studies) and molecular dynamics simulation (protein-ligand stability). Combined computational investigation showed that the compounds NCI_95001, NCI_382818 and UNPD_141613 may have inhibitory effects against the CYP24A1 protein.

Insights into unbound-bound states of GPR142 receptor in a membrane-aqueous system using molecular dynamics simulations

G protein coupled receptors (GPCRs) are source machinery in signal transduction pathways and being one of the major therapeutic targets play a significant in drug discovery. GPR142, an orphan GPCR, has been implicated in the regulation of insulin, thereby having a crucial role in Type II diabetes management. Deciphering of the structures of orphan, GPCRs (O-GPCRs) offer better prospects for advancements in research in ion translocation and transduction of extracellular signals. As the crystallographic structure of GPR142 is not available in PDB, therefore, threading and ab initio-based approaches were used for 3D modeling of GPR142. Molecular dynamic simulations (900 ns) were performed on the 3D model of GPR142 and complexes of GPR142 with top five hits, obtained through virtual screening, embedded in lipid bilayer with aqueous system using OPLS force field. Compound 1, 3, and 4 may act as scaffolds for designing potential lead agonists for GPR142. The finding of GPR142 MD simulation study provides more comprehensive representation of the functional properties. The concern for Type II diabetes is increasing worldwide and successful treatment of this disease demands novel drugs with better efficacy.

In silico identification of potential drug compound against Peroxisome proliferator-activated receptor-gamma by virtual screening and toxicity studies for the treatment of Diabetic Nephropathy

Diabetic Nephropathy is a serious complication of diabetes mellitus. Current therapeutic strategies of Diabetic Nephropathy are based on control of modifiable risks like hypertension, glucose levels, and dyslipidemia. Peroxisome proliferator activated receptor-gamma (PPAR-γ) is implicated in several metabolic syndromes including Diabetic Nephropathy, besides obesity, insulin insensitivity, dislipidemia, inflammation, and hypertension. In the present study, virtual screening of 617 compounds from two different public databases was done against PPAR-γ with an objective to find a possible lead compound. Two softwares, PyRx and iGEMDOCK, were used to achieve the docking accuracy in order to avoid loss of candidate compounds. Rosiglitazone (used to treat Diabetic Nephropathy) was taken as the standard compound. A total of 30 compounds with good binding affinity with PPAR-γ were selected for further filtering, on the basis of absorption, distribution, metabolism, excretion, and toxicity (ADMET). The interaction profiling of these 30 compounds, showed a minimum of one and maximum of three interactions with reference to rosiglitazone (SER-289, HIS-449, HIS-323, TYR-473). The fulfilling of ADMET analysis criteria of 30 compounds led to the selection of four compounds (ZINC ID 00181552, 00276456, 00298314, 00448009). Molecular dynamics simulation of these lead compounds in complex with PPAR-γ revealed that three of the four compounds formed a stable complex in the ligand-binding pocket of PPAR-γ during 20-ns simulation. Hence, these three (ZINC ID 00181552, 00276456, 00298314) of the four compounds are potential candidates for experimental validation of biological activity against PPAR-γ in future drug discovery studies.

In silico modelling and molecular dynamics simulation studies of thiazolidine based PTP1B inhibitors

Protein tyrosine phosphatase 1B (PTP1B) has been identified as a negative regulator of insulin and leptin signalling pathway; hence, it can be considered as a new therapeutic target of intervention for the treatment of type 2 diabetes. Inhibition of this molecular target takes care of both diabetes and obesity, i.e. diabestiy. In order to get more information on identification and optimization of lead, pharmacophore modelling, atom-based 3D QSAR, docking and molecular dynamics studies were carried out on a set of ligands containing thiazolidine scaffold. A six-point pharmacophore model consisting of three hydrogen bond acceptor (A), one negative ionic (N) and two aromatic rings (R) with discrete geometries as pharmacophoric features were developed for a predictive 3D QSAR model. The probable binding conformation of the ligands within the active site was studied through molecular docking. The molecular interactions and the structural features responsible for PTP1B inhibition and selectivity were further supplemented by molecular dynamics simulation study for a time scale of 30 ns. The present investigation has identified some of the indispensible structural features of thiazolidine analogues which can further be explored to optimize PTP1B inhibitors.