3D-QSAR based pharmacophore modeling and virtual screening for identification of novel pteridine reductase inhibitors

Abstract

Pteridine reductase is a promising target for development of novel therapeutic agents against Trypanosomatid parasites. A 3D-QSAR pharmacophore hypothesis has been generated for a series of L. major pteridine reductase inhibitors using Catalyst/HypoGen algorithm for identification of the chemical features that are responsible for the inhibitory activity. Four pharmacophore features, namely: two H-bond donors (D), one Hydrophobic aromatic (H) and one Ring aromatic (R) have been identified as key features involved in inhibitor-PTR1 interaction. These features are able to predict the activity of external test set of pteridine reductase inhibitors with a correlation coefficient (r) of 0.80. Based on the analysis of the best hypotheses, some potent Pteridine reductase inhibitors were screened out and predicted with anti-PTR1 activity. It turned out that the newly identified inhibitory molecules are at least 300 fold more potent than the current crop of existing inhibitors. Overall the current SAR study is an effort for elucidating quantitative structure-activity relationship for the PTR1 inhibitors. The results from the combined 3D-QSAR modeling and molecular docking approach have led to the prediction of new potent inhibitory scaffolds.

Pharmacophore-Based Virtual Screening and Biological Evaluation of Small Molecule Inhibitors for Protein Arginine Methylation

“…Pharmacophore-based virtual screening has been successfully carried out in the search of novel leads for several targets. 26–28 …”

Pharmacophore Mapping, In Silico Screening and Molecular Docking to Identify Selective Trypanosoma brucei Pteridine Reductase Inhibitors

“…The presence of two hydrophobic features in the QSAR hypothesis emphasizes the already established importance of these interactions in PTR1 molecular recognition and catalysis 20. Our group has earlier reported 3D‐QSAR pharmocophore hypothesis derived from the LmPTR1 20.…”

“…The presence of two hydrophobic features in the QSAR hypothesis emphasizes the already established importance of these interactions in PTR1 molecular recognition and catalysis 20. Our group has earlier reported 3D‐QSAR pharmocophore hypothesis derived from the LmPTR1 20. Four pharmacophore features, namely: two H‐bond donors (D), one Hydrophobic aromatic (H) and one Ring aromatic (R) have been shown to be responsible for biological activity against LmPTR1.…”

“…The 3D QSAR Pharmacophore Generation protocol with the Catalyst HypoGen16 algorithm was used to derive SAR pharmacophore models from a given set of inhibitors with known biological activity. The pharmacophore model and conformers were developed by using the same procedure adopted for L. major inhibitors 20. A maximum of 250 diverse conformational models were generated for each compound using an energy constraint of 20 kcal/mol.…”

Effective virtual screening strategy focusing on the identification of novel Bruton's tyrosine kinase inhibitors

“…The 3D QSAR Pharmacophore Generation module available in DS2.5 is a productive technique to generate quantitative pharmacophore models, and has been proved successful in discovering lead compounds with new scaffolds [24,38]. Herein, the prepared training set was submitted to the protocol, and the features including hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), ring aromatic (RA), and hydrophobic (HY) were selected to develop pharmacophore hypotheses according to the result of feature mapping.…”