Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach. (June 2015)

• Record Type: Journal Article
• Authors: Babu, Sathya; Sohn, Honglae; Madhavan, Thirumurthy
• Is Part Of: Computational biology and chemistry. Volume 56(2015)
• Page Start: 109
• Page End: 121
• Publication Date: 2015-06
• Subjects: CRTh2 -- 3D-QSAR -- CoMFA -- CoMSIA; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.04.007
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Ligand based CoMFA and CoMSIA were performed on CRTh2 antagonist. Implemented two different alignments based on systematic search and simulated annealing. Steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor parameters were desirable for potent inhibitory activity. Abstract: CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q 2 > 0.4, r 2 > 0.5 and r 2 pred > 0.5. Based on better q 2 and r 2 pred values, the best predictions were obtained for the CoMFA (model 5 q 2 = 0.488, r 2 pred = 0.732), and CoMSIA (model 45 q 2 = 0.525, r 2 pred = 0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.