by Michael Fernández, Leyden Fernández, Julio Caballero, José Abreu, Grethel Reyes
Abstract:
A target-ligand QSAR approach using autocorrelation formalism was developed for modeling the inhibitory potency (pIC50) 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 (R2test) 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. © 2008 The Authors.
Reference:
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 (Michael Fernández, Leyden Fernández, Julio Caballero, José Abreu, Grethel Reyes), In Chemical Biology and Drug Design, volume 72, 2008. (http://www.scopus.com/inward/record.url?eid=2-s2.0-45449097699&partnerID=40&md5=ed90b738325ea77d70beae342ad49836 http://onlinelibrary.wiley.com/doi/10.1111/j.1747-0285.2008.00675.x/abstract) (cited By (since 1996) 2)
Bibtex Entry:
Title = {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},
Author = {Michael Fernández and Leyden Fernández and Julio Caballero and José Abreu and Grethel Reyes},
Journal = {Chemical Biology and Drug Design},
Year = {2008},
Note = {cited By (since 1996) 2},
Number = {1},
Pages = {65-78},
Volume = {72},
Abstract = {A target-ligand QSAR approach using autocorrelation formalism was developed for modeling the inhibitory potency (pIC50) 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 (R2test) 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. © 2008 The Authors.},
Affiliation = {Center for Biotechnological Studies, Faculty of Agronomy, University of Matanzas, Matanzas 44740, Cuba; Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Centro de Bioinformática Y Simulación Molecular, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile; Artificial Intelligence Lab., Faculty of Informatics, University of Matanzas, Matanzas 44740, Cuba},
Author_keywords = {Bayesian-regularized genetic neural networks; Genetic algorithm; MMP inhibitors; QSAR analysis},
Comment = {http://www.scopus.com/inward/record.url?eid=2-s2.0-45449097699&partnerID=40&md5=ed90b738325ea77d70beae342ad49836 http://onlinelibrary.wiley.com/doi/10.1111/j.1747-0285.2008.00675.x/abstract},
Document_type = {Article},
Doi = {http://dx.doi.org/10.1111/j.1747-0285.2008.00675.x},
Owner = {2008_Chem_Biol_Drug_Des_72_65},
Source = {Scopus},
Url = {http://www.ncbi.nlm.nih.gov/pubmed/18554254}
}