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3D-QSAR-aided design of potent c-Met inhibitors using molecular dynamics simulation and binding free energy calculation

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Version 2 2018-11-01, 10:36
Version 1 2018-07-25, 14:14
journal contribution
posted on 2018-11-01, 10:36 authored by Pavithra K. Balasubramanian, Anand Balupuri, Swapnil P. Bhujbal, Seung Joo Cho

Abstract

Mesenchymal-epithelial transition factor (c-Met) is a member of receptor tyrosine kinase. It involves in various cellular signaling pathways which includes proliferation, motility, migration, and invasion. Over-expression of c-Met has been reported in various cancers. Hence, it is an ideal therapeutic target for cancer. The main objective of the study is to identify crucial residues involved in the inhibition of c-Met kinase and to design a series of potent imidazo [4,5-b] pyrazine derivatives as c-Met inhibitors. Docking was used to identify important active site residues involved in the inhibition of c-Met kinase which was further validated by 100 ns of molecular dynamics simulation and free energy calculation using molecular mechanics generalized born surface area. Furthermore, binding energy decomposition identified that residues Tyr1230, Met1211, Asp1222, Tyr1159, Met1160, Val1092, Ala1108, and Leu1157 contributed favorably to the binding stability of compound 32. Receptor-guided Comparative Molecular Field Analysis (CoMFA) (q2 = 0.751, NOC = 6, r2 = 0.933) and Comparative Molecular Similarity Indices Analysis (COMSIA) (q2 = 0.744, NOC = 6, r2 = 0.950) models were generated based on the docked conformation of the most active compound 32. The robustness of these models was tested using various validation techniques and found to be predictive. The results of CoMFA and CoMSIA contour maps exposed the regions favorable to enhance the activity. Based on this information, 27 novel c-Met inhibitors were designed. These designed compounds exhibited potent activity than the most active compound of the existing dataset.

Communicated by Ramaswamy H. Sarma

Funding

This work is supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (NRF-2015R1A5A2009070).

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