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Item Proteometric modelling of protein conformational stability using amino acid sequence autocorrelation vectors and genetic algorithm-optimised support vector machinesAutores: Fernandez, M.; Fernandez, L.; Sanchez, P.; Caballero, J.; Abreu, J.I.The conformational stability of more than 1500 protein mutants was modelled by a proteometric approach using amino acid sequence autocorrelation vector (AASA) formalism. 48 amino acid/residue properties selected from the AAindex database weighted the AASA vectors. Genetic algorithm-optimised support vector machine (GA-SVM), trained with subset of AASA descriptors, yielded predictive classification and regression models of unfolding Gibbs free energy change (Delta Delta G). Function mapping and binary SVM models correctly predicted about 50 and 80% of Delta Delta G variances and signs in crossvalidation experiments, respectively. Test set prediction showed adequate accuracies about 70% for stable single and double point mutants. Conformational stability depended on autocorrelations at medium and long ranges in the mutant sequences of general structural, physico-chemical and thermodynamical properties relative to protein hydration process. A preliminary version of the predictor is available online at http://gibk21.bse.kyutech.ac.jp/llamosa/ddG-AASA/ddG_AASA.html.Item Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)Autores: Fernandez, M.; Caballero, J.; Fernandez, L.; Sarai, A.Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.Item Classification of conformational stability of protein mutants from 2D graph representation of protein sequences using support vector machinesAutores: Fernandez, M.; Caballero, J.; Fernandez, L.; Abreau, J.I.; Acostas, G.Item Identification of a potent and selective sigma(1) receptor agonist potentiating NGF-induced neurite outgrowth in PC12 cellsAutores: Rossi, D.; Pedrali, A.; Urbano, M.; Gaggeri, R.; Serra, M.; Fernandez, L.; Fernandez, M.; Caballero, J.; Ronsisvalle, S.; Prezzavento, O.; Schepmann, D.; Wuensch, B.; Peviani, M.; Curti, D.; Azzolina, O.; Collina, S.Herein we report the synthesis, drug-likeness evaluation, and in vitro studies of new sigma (sigma) ligands based on arylalkenylaminic scaffold. For the most active olefin the corresponding arylalkylamine was studied. Novel arylalkenylamines generally possess high sigma(1) receptor affinity (K-i values <25 nM) and good sigma(1)/sigma(2) selectivity (K-i sigma(2) > 100). Particularly, the piperidine derivative (E)-17 and its arylalkylamine analog (R, S)-33 were observed to be excellent sigma(1) receptor ligands (K-i = 0.70 and 0.86 nM, respectively) and to display significantly high selectivity over sigma(2), mu-, and kappa-opioid receptors and phencyclidine (PCP) binding site of the N-methyl-D-aspartate (NMDA) receptors. Moreover in PC12 cells (R, S)-33 promoted the nerve growth factor (NGF)-induced neurite outgrowth and elongation. Co-administration of the selective sigma(1) receptor antagonist BD-1063 totally counteracted this effect, confirming that sigma(1) receptors are involved in the (R,S)-33 modulation of the NGF effect in PC12 cells and suggesting a sigma(1) agonist profile. As a part of our work, a threedimensional sigma(1) pharmacophore model was also developed employing GALAHAD methodology. Only active compounds were used for deriving this model. The model included two hydrophobes and a positive nitrogen as relevant features and it was able to discriminate between molecules with and without affinity toward sigma(1) receptor subtype. (C) 2011 Elsevier Ltd. All rights reserved.Item 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 networksAutores: Fernandez, M.; Abreau, J.I.; Caballero, J.; Garriga, M.; Fernandez, L.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
