In- silico Design of Novel Inhibitors for Benzimidazole Receptor against Cryptosporidium parvum

Abstract

Cryptosporidium parvum is an obligate, intracellular, protozoan parasite belonging to the phylum Apicomplexa. Cryptosporidium parvum is increasingly being recognized as an important pathogen causing diarrhea in children, with the highest associated morbidity and mortality, especially among children in developing countries. The highest prevalence of cryptosporidiosis has been documented in children aged 6–12 months. 3D QSAR studies were carried out on benzimidazole derivatives to identify new improved possible inhibitors. kNN-MFA methodology was used to conduct 3D QSAR studies which are to found to have better predictivity as compared to other regression methods. In this study, a good statistical model was generated on thirty two benzimidazole derivatives, which was used to design newer molecule with high binding affinity against IMPDH. These derivatives are active and can be used to generate 3D-QSAR model. A good statistical model developed by the stepwise kNN-MFA method having the cross validated correlation coefficient 68% (q2= 0.6852) and predictive correlation coefficient 93% (pred r2= 0.9325), showed the best prediction for IMPDH inhibition in test set of compounds. QSAR model indicate steric descriptor, at the grid points that play an important role to evaluate activity of new molecules. Thus, kNN-MFA stepwise contour plots and docking studies provide beneficial clues related to structural modification of substituted benzimidazole derivatives and their activities which should be applicable to design as well as predict activities of newly designed molecules. Further, to check the drug likeness of compounds, we also carried out ADME/Toxicity prediction.

Keywords: IMPDH, Benzimidazole, Cryptosporidium parvum, kNN-MFA

Cite this Article

Chandra Isha, Mishra BN, Srivastava   Vivek et al.  In- silico Design of Novel Inhibitors for Benzimidazole Receptor Against Cryptosporidium parvum. Research & Reviews: A Journal of Bioinformatics. 2015; 2(2): 1–10p.