In silico ligand-based pharmacophore model generation for the identification of novel Pneumocystis carinii DHFR inhibitors

Abstract

The ubiquitous enzyme, dihydrofolate reductase (DHFR), is a well-studied metabolic enzyme of significant pharmacological relevance. The past research studies have shown that DHFR has emerged as an important therapeutic target for the treatment of various diseases including cancers, bacterial and protozoal infections, and the opportunistic infections that are associated with AIDS. DHFR has been successfully used and selected as a target for the purpose of devising a cure for various diseases by discovering the antimicrobial drugs against a range of pathogenic microorganisms including the opportunistic microorganisms Pneumocystis carinii (pc), Toxoplasma gondii and Mycobacterium avium complex. However, it has been reported that blockage of the enzymatic activity of DHFR is a crucial and most significant element in terms of the treatment of a wide range of diseases. DHFR is also a key enzyme in the treatment of Pneumocystosis. The discovery and development of type-specific pcDHFR inhibitors is of both research and clinical interests. Ligand-based pharmacophore modeling is playing a vital role for the identification of ligand features for the particular targets. In this study, we present a model for the design of ligand-based pharmacophore onto the set of 10 compounds of eight different classes along with the standard drug trimethoprim. The ligand-based pharmacophore model has been identified to facilitate the discovery of type-specific pcDHFR inhibitors. A pharmacophore model was generated using Ligand Scout 3.02 with diverse classes of pcDHFR inhibitors. The proposed pharmacophore model generated in this study revealed that the model contains two HBAs, two HBDs, and one HY/AR volume. Ligand Scout 3.02 has been used to predict the pharmacophore features for pcDHFR inhibitors, and the distances between pharmacophore features have been computed by the effective use of the software VMD. The results indicate that the in silico methods are valuable for the prediction of the biological activity of the compound or compound library by screening it against a predicted pharmacophore. Thus, the results obtained in this study can be considered to be useful and reliable tools for the identification of structurally diverse compounds with the desired biological activity. The model has also been validated, firstly by mapping of five test compounds on to our model and secondly by docking these test compounds into the active site of pcDHFR.