Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:3.4.24.17 (MMP-3)
 3,419 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The inhibitory activity (IC50) toward matrix metalloproteinases (MMP-1, MMP-2, MMP-3, MMP-9, and MMP-13) of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives (HPSAAs) has been successfully modeled using 2D autocorrelation descriptors. The relevant molecular descriptors were selected by linear and nonlinear genetic algorithm (GA) feature selection using multiple linear regression (MLR) and Bayesian-regularized neural network (BRANN) approaches, respectively. The quality of the models was evaluated by means of cross-validation experiments and the best results correspond to nonlinear ones (Q2>0.7 for all models). Despite the high correlation between the studied compound IC50 values, the 2D autocorrelation space brings different descriptors for each MMP inhibition. On the basis of these results, these models contain useful molecular information about the ligand specificity for MMP S'1, S1, and S'2 pockets.
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PMID:Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives as matrix metalloproteinase inhibitors. 1650 15

A target-ligand QSAR approach using autocorrelation formalism was developed for modeling the inhibitory potency (pIC(50)) toward matrix metalloproteinases (MMP-1, MMP-2, MMP-3, MMP-9, and MMP-13) of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives. Target and ligand structural information was encoded in the Topological Autocorrelation Interaction matrix calculated from 2D topological representation of inhibitors and protein sequences. The relevant Topological Autocorrelation Interaction descriptors were selected by genetic algorithm-based multilinear regression analysis and Bayesian-regularized genetic neural network approaches. A model ensemble strategy was employed for achieving robust and reliable linear and non-linear predictors having nine topological autocorrelation interaction descriptors with square correlation coefficients of ensemble test-set fitting (R(2)(test)) about 0.80 and 0.87, respectively. Electrostatic and hydrophobicity/hydrophilicity properties were the most relevant on the optimum models. In addition, the distribution of the inhibition complexes on a self-organized map depicted target dependence rather than an inhibitor similarity pattern.
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PMID:Proteochemometric modeling of the inhibition complexes of matrix metalloproteinases with N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives using topological autocorrelation interaction matrix and model ensemble averaging. 1855 54