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Please use this identifier to cite or link to this item: http://dspace.utalca.cl/handle/1950/4710

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 Bayesianregularized genetic neural networks
Authors: Fernandez, M.
Abreau, J.I.
Caballero, J.
Garriga, M.
Fernandez, L.
Keywords: Bayesian regularization
Point mutations
Artificial neural networks
Protein stability
Issue Date: 2007
Publisher: Taylor & Francis Ltd.
Citation: Molecular Simulation 33 (13):1045 -1056
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 (ΔΔG) 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
Description: Caballero, J. Centro de Bioinformática y Simulación Molecular, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
URI: http://dspace.utalca.cl/handle/1950/4710
ISSN: 0892-7022
Appears in Collections:Artículos en publicaciones ISI - Universidad de Talca

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