by Michael Fernández, José Abreu, Julio Caballero, Miguel Garriga, Leyden Fernández
Abstract:
Predicting protein stability changes upon point mutation is important for understanding protein structure and designing new proteins. Autocorrelation vector formalism was extended to amino acid sequences and 3D conformations for encoding protein structural information with modeling purpose. Protein autocorrelation vectors were weighted by 48 amino acid/residue properties selected from the AAindex database. Ensembles of Bayesian-regularized genetic neural networks (BRGNNs) trained with amino acid sequence autocorrelation (AASA) vectors and amino acid 3D autocorrelation (AA3DA) vectors yielded predictive models of the change of unfolding Gibbs free energy change (G) of chymotrypsin Inhibitor 2 protein mutants. The ensemble predictor described about 58 and 72% of the data variances in test sets for AASA and AA3DA models, respectively. Optimum sequence and 3D-based ensembles exhibit high effects on relevant structural (volume, solvent-accessible surface area), physico-chemical (hydrophilicity/hydrophobicity-related) and thermodynamic (hydration parameters) properties.
Reference:
Comparative modeling of the conformational stability of chymotrypsin inhibitor 2 protein mutants using amino acid sequence autocorrelation (AASA) and amino acid 3D autocorrelation (AA3DA) vectors and ensembles of Bayesian-regularized genetic neural networks. (Michael Fernández, José Abreu, Julio Caballero, Miguel Garriga, Leyden Fernández), In Molecular Simulation, volume 33, 2007. (http://www.scopus.com/inward/record.url?eid=2-s2.0-36248942933&partnerID=40&md5=28a982cc9acb862a0d3afbf47cebf96e http://www.tandfonline.com/doi/abs/10.1080/08927020701564479.Ub-EJsob-QI) (cited By (since 1996) 3)
Bibtex Entry:
Title = {Comparative modeling of the conformational stability of chymotrypsin inhibitor 2 protein mutants using amino acid sequence autocorrelation (AASA) and amino acid 3D autocorrelation (AA3DA) vectors and ensembles of Bayesian-regularized genetic neural networks.},
Author = {Michael Fernández and José Abreu and Julio Caballero and Miguel Garriga and Leyden Fernández},
Journal = {Molecular Simulation},
Year = {2007},
Note = {cited By (since 1996) 3},
Number = {13},
Pages = {1045-1056},
Volume = {33},
Abstract = {Predicting protein stability changes upon point mutation is important for understanding protein structure and designing new proteins. Autocorrelation vector formalism was extended to amino acid sequences and 3D conformations for encoding protein structural information with modeling purpose. Protein autocorrelation vectors were weighted by 48 amino acid/residue properties selected from the AAindex database. Ensembles of Bayesian-regularized genetic neural networks (BRGNNs) trained with amino acid sequence autocorrelation (AASA) vectors and amino acid 3D autocorrelation (AA3DA) vectors yielded predictive models of the change of unfolding Gibbs free energy change (G) of chymotrypsin Inhibitor 2 protein mutants. The ensemble predictor described about 58 and 72% of the data variances in test sets for AASA and AA3DA models, respectively. Optimum sequence and 3D-based ensembles exhibit high effects on relevant structural (volume, solvent-accessible surface area), physico-chemical (hydrophilicity/hydrophobicity-related) and thermodynamic (hydration parameters) properties.},
Affiliation = {Faculty of Agronomy, Molecular Modeling Group, University of Matanzas, 44740 Matanzas, Cuba; Artificial Intelligence Laboratory, Faculty of Informatics, University of Matanzas, 44740 Matanzas, Cuba; Centro de Bioinformática y Simulación Molecular, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile; Faculty of Agronomy, Plant Biotechnology Group, University of Matanzas, C.P., 44740 Matanzas, Cuba},
Author_keywords = {Artificial neural networks; Bayesian regularization; Point mutations; Protein stability},
Comment = {http://www.scopus.com/inward/record.url?eid=2-s2.0-36248942933&partnerID=40&md5=28a982cc9acb862a0d3afbf47cebf96e http://www.tandfonline.com/doi/abs/10.1080/08927020701564479#.Ub-EJsob-QI},
Document_type = {Article},
Doi = {http://dx.doi.org/10.1080/08927020701564479},
Owner = {2007_Mol_Simulat_33_1045},
Source = {Scopus},
Url = {http://www.tandfonline.com/doi/abs/10.1080/08927020701564479}
}