Dhrubajyoti Gogoi

3D pharmacophore-based virtual screening, docking and density functional theory approach towards the discovery of novel human epidermal growth factor receptor-2 (HER2) inhibitors

Human epidermal growth factor receptor 2 (HER2) is one of the four members of the epidermal growth factor receptor (EGFR) family and is expressed to facilitate cellular proliferation across various tissue types. Therapies targeting HER2, which is a transmembrane glycoprotein with tyrosine kinase activity, offer promising prospects especially in breast and gastric/gastroesophageal cancer patients. Persistence of both primary and acquired resistance to various routine drugs/antibodies is a disappointing outcome in the treatment of many HER2 positive cancer patients and is a challenge that requires formulation of new and improved strategies to overcome the same. Identification of novel HER2 inhibitors with improved therapeutics index was performed with a highly correlating (r=0.975) ligand-based pharmacophore model (Hypo1) in this study. Hypo1 was generated from a training set of 22 compounds with HER2 inhibitory activity and this well-validated hypothesis was subsequently used as a 3D query to screen compounds in a total of four databases of which two were natural product databases. Further, these compounds were analyzed for compliance with Vebers drug-likeness rule and optimum ADMET parameters. The selected compounds were then subjected to molecular docking and Density Functional Theory (DFT) analysis to discern their molecular interactions at the active site of HER2. The findings thus presented would be an important starting point towards the development of novel HER2 inhibitors using well-validated computational techniques.

Identification of novel human renin inhibitors through a combined approach of pharmacophore modelling, molecular DFT analysis and in silico screening

Renin is an aspartyl protease of the renin-angiotensin system (RAS) and the first enzyme of the biochemical pathway for the generation of angiotensin II - a potent vasoconstrictor involved in the maintenance of cardiovascular homeostasis and the regulation of blood pressure. High enzymatic specificity of renin and its involvement in the catalysis of the rate-limiting step of the RAS hormone system qualify it as a good target for inhibition of hypertension and other associated diseases. Ligand-based pharmacophore model (Hypo1) was generated from a training set of 24 compounds with renin inhibitory activity. The best hypothesis consisted of one Hydrogen Bond Acceptor (HBA), three Hydrophobic Aliphatic (HY-Al) and one Ring Aromatic (AR) features. This well-validated pharmacophore hypothesis (correlation coefficient 0.95) was further utilized as a 3D query to screen database compounds, which included structures from two natural product repositories. These screened compounds were further analyzed for drug-likeness and ADMET studies. The compounds which satisfied the qualifying criteria were then subjected to molecular docking and Density Functional Theory (DFT) analysis in order to discern their atomic level interactions at the active site of the 3D structure of rennin. The pharmacophore-based modelling that has been used to generate the novel findings of the present study would be an avant-garde approach towards the development of potent inhibitors of renin.

An In Silico Approach for Identification of Potential Anti-Mycobacterial Targets of Vasicine and Related Chemical Compounds

Tuberculosis (TB) is known to mankind as one of the most pervasive and persistent of diseases since the early days of civilization. The growing resistance of the causative pathogen Mycobacterium tuberculosis to the standard drug regimen for TB poses further difficulty in its treatment and control. Screening of novel plant-derived compounds with promising anti-tubercular activity has been cited as a prospective route for new anti-tubercular drug discovery and design. Justicia adhatoda L. is a perennial evergreen shrub which is widely mentioned in scientific literature on account of its potent anti-mycobacterial properties. In the present study, we have employed a series of computational methodologies to reveal the probable molecular interactions of vasicine, the principal alkaloid of Justicia adhatoda L., and two of its close natural derivatives- vasicinone and deoxyvasicine, with certain biological targets in M. tuberculosis. Targets were identified from literature and through a reverse Pharmacophore-based approach. Subsequent comparative molecular docking to identify the best ligand-target interactions revealed Antigen 85C of M. tuberculosis as the most potent biological target of vasicine on the basis of optimum molecular docking values. A chemogenomics approach was also employed to validate the molecular interactions between the same class of chemical compounds as vasicine and Antigen 85C. Further, a library of structural analogs of vasicine was created by bioiosterism-based drug design to identify structural analogs with better inhibitory potential against Antigen 85C.

Identification of potential type 4 cAMP phosphodiesterase inhibitors via 3D pharmacophore modeling, virtual screening, DFT and structural bioisostere design

Cyclic nucleotide phosphodiesterase Type 4 specifically metabolizes Cyclic Adenosine Monophosphate and has widespread distribution across the human body. The aim of this study was to generate a well-validated ligand-based 3D Quantitative Structure Activity Relationship pharmacophore model to identify potential phosphodiesterase Type 4 inhibitors using a set of 18 known chemically diverse phosphodiesterase Type 4 inhibitors. The HypoGen module of Discovery Studio v4.1 was used to generate the aforementioned pharmacophore model which was then employed as 3D query for virtual screening of four chemical and two natural product databases. The top hits were evaluated for their drug-like properties. The binding orientations of the best fits were predicted by molecular docking. Orbital energies were predicted for top hits and the density functional theory based minimum energy gap (Highest Occupied Molecular Orbital–Lowest Unoccupied Molecular Orbital gap) was used to further cull the selection and identify the most potential phosphodiesterase Type 4 inhibitors. Chemical similarity search was performed and structural analogs of the best hits were designed to discover novel potential phosphodiesterase Type 4 inhibitors. Use of Hypo1 as 3D query for virtual screening yielded 1243 compounds and subsequent molecular docking studies narrowed it down to 19 potential phosphodiesterase Type 4 inhibitors while a density functional theory-based study further culled this selection down to six most potential inhibitors. Six structurally diverse chemical structures with novel scaffolds and six analogs of the best hits were identified using pharmacophore modeling to be potential phosphodiesterase Type 4 inhibitors.

Novel butyrylcholinesterase inhibitors through pharmacophore modeling, virtual screening and DFT-based approaches along-with design of bioisosterism-based analogues

Ligand and structure-based pharmacophore models were used to identify the important chemical features of butyrylcholinesterase (BChE) inhibitors. A training set of 16 known structurally diverse compounds with a wide range of inhibitory activity against BChE was used to develop a quantitative ligand-based pharmacophore (Hypo1) model to identify novel BChE inhibitors in virtual screening databases. A structure-based pharmacophore hypothesis (Phar1) was also developed with the ligand-binding site of BChE in consideration. Further, the models were validated using test set, Fishers Randomization and Leave-one-out validation methods. Well-validated pharmacophore hypotheses were further used as 3D queries in virtual screening and 430 compounds were finally selected for molecular docking analysis. Subsequently, ADMET, DFT and chemical similarity search were employed to narrow down on seven compounds as potential drug candidates. Analogues of the best hit were further developed through a bioisosterism-guided approach to further generate a library of potential BChE inhibitors.