Abstract
Background: Mitotic kinesin Eg5, a member of the kinesin superfamily, plays an essential role in cell proliferation while the regulated cell proliferation is essential for survival, but uncontrolled cell proliferation increases the risk of cancer. Therefore development of new efficient Eg5 inhibitors is very important in cancer chemotherapy. S-trityl-L-cysteine (STLC) and their analogues are known as potent allosteric inhibitors for Eg5. In the present work, we try to develop some 3D-QSAR techniques including CoMFA and COMSIA methods to modeling and prediction of the Eg5 inhibitory activities for new STLC analogues. The result of this study not only can use to predict the Eg5 inhibitory activities of untested chemicals but also to design more active chemicals.
Method: The 3D-QSAR methods including CoMFA, CoMSIA, and H-QSAR methods were performed to study the structure anti-cancer activity relationship of STLC analogues. The analysis of the contour maps from CoMFA was used for design more active Eg5 inhibitors.
Results: The constructed CoMFA and CoMSIA models produced statistically significant results with the cross-validation correlation coefficients Q2 of 0.608 and 0.5, noncross-validation correlation coefficients R2 of 0.988 and 0.943, standard errors SE of 0.0937 and 0.2, and predicted correlation coefficients R2pred of 0.925 and 0.761, respectively. The analysis of the steric contour maps from CoMFA showed sterically bulky groups (R1) in para position have a favorable effect on inhibitory activity. Also, the electrostatic contour map of CoMFA model suggests that the presence electron negative substituent in the meta and para position can increase the inhibitory activity.
Conclusion: Based on the result of COMFA model newly compounds with good predictive activity have been designed and predicted. Our finding in design represents both factors affinity to Eg5 and physicochemical properties of inhibitor should be considered in design and synthesis.
Keywords: 3D-QSAR, kinesin Eg5, STLC derivatives, CoMFA, CoMSIA, H-QSAR.
Graphical Abstract