A combined molecular modeling study on gelatinases and their potent inhibitors

Abstract

Zinc-dependent matrix metalloproteinase (MMP) family is considered to be an attractive target because of its important role in many physiological and pathological processes. In the present work, a molecular modeling study combining protein-, ligand- and complex-based computational methods was performed to analyze a new series of beta-N-biaryl ether sulfonamide hydroxamates as potent inhibitors of gelatinase A (MMP-2) and gelatinase B (MMP-9). Firstly, the similarities and differences between the binding sites of MMP-2 and MMP-9 were analyzed through sequence alignment and structural superimposition. Secondly, in order to extract structural features influencing the activities of these inhibitors, quantitative structure-activity relationship (QSAR) models using genetic algorithm-multiple linear regression (GA-MLR), comparative molecular field (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed. The proposed QSAR models could give good predictive ability for the studied inhibitors. Thirdly, docking study was employed to further explore the binding mode between the ligand and protein. The results from all the above analyses could provide the information about the similarities and differences of the binding mode between the MMP-2, MMP-9 and their potent inhibitors. The obtained results can provide very useful information for the design of new potential inhibitors.

MMP-2 Selectivity in Hydroxamate-Type Inhibitors

“…In one hand, information can be obtained about the binding mode and the key interactions involved in molecular recognition process, responsible for binding affinity and selectivity. A recent study describes in detail the binding mode of a representative compound to both MMP-2, and MMP-9 enzymes based on docking studies [132]. Significant differences in MMP-2/MMP-9 inhibitory activity may be explained by the different binding mode towards both proteins, even though subtle differences can be found in their sequence alignment and structure superimposition of the ligand binding site.…”

Global and local prediction of protein folding rates based on sequence autocorrelation information

“…It can deal with large search space efficiently and has less chance to get local optimal solution than other algorithm (Hou et al, 1999). Many applications have proved GA to be a very effective tool in solving feature selection problems Hemmateenejad and Mohajeri, 2008;Li et al, 2008;Xi et al, 2010;Liu et al, 2006;Rogers and Hopfinger, 1994). Therefore, GA is employed to select the significant features here, then the simple multiple linear regression (MLR) method is used to build model.…”

Prediction of infinite-dilution activity coefficients of organic solutes in ionic liquids using temperature-dependent quantitative structure–property relationship method

“…Many applications have proved genetic algorithm (GA) to be a very effective tool in solving feature selection problems [31][32][33][34]. Therefore, GA was employed to select the significant descriptors in this work.…”