Biomedical subjects

Julio Caballero

Publications and source records attributed to Julio Caballero.

18 recordsLinked to original sources

PubMed2007

QSAR models for predicting the activity of non-peptide luteinizing hormone-releasing hormone (LHRH) antagonists derived from erythromycin A using quantum chemical properties.

Multiple linear regression (MLR) combined with genetic algorithm (GA) and Bayesian-regularized Genetic Neural Networks (BRGNNs) were used to model the binding affinity (pK(I)) of 38 11,12-cyclic carbamate derivatives of 6-O-methylerythromycin A for the Human Luteinizing Hormone-Releasing Hormone (LHRH) receptor using quantum chemical descriptors. A multiparametric MLR equation with good statistical quality was obtained that describes the features relevant for antagonistic activity when the substituent at the position 3 of the erythronolide core was varied. In addition, four-descriptor linear and nonlinear models were established for the whole dataset. Such models showed high statistical quality. However, the BRGNN model was better than the linear model according to the external validation process. In general, our linear and nonlinear models reveal that the binding affinity of the compounds studied for the LHRH receptor is modulated by electron-related terms.

Animals↗

PubMed2006

Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines.

Functional variations on the human ghrelin receptor upon mutations have been associated with a syndrome of short stature and obesity, of which the obesity appears to develop around puberty. In this work, we reported a proteometrics analysis of the constitutive and ghrelin-induced activities of wild-type and mutant ghrelin receptors using amino acid sequence autocorrelation (AASA) approach for protein structural information encoding. AASA vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. Genetic algorithm-based multilinear regression analysis (GA-MRA) and genetic algorithm-based least square support vector machines (GA-LSSVM) were used for building linear and non-linear models of the receptor activity. A genetic optimized radial basis function (RBF) kernel yielded the optimum GA-LSSVM models describing 88% and 95% of the cross-validation variance for the constitutive and ghrelin-induced activities, respectively. AASA vectors in the optimum models mainly appeared weighted by hydrophobicity-related properties. However, differently to the constitutive activity, the ghrelin-induced activity was also highly dependent of the steric features of the receptor.

Algorithms↗

PubMed2006

QSAR for non-nucleoside inhibitors of HIV-1 reverse transcriptase.

By means of QSAR algorithms we model the potency pIC(90) [mM] of 154 non-nucleoside reverse transcriptase inhibitors (NNRTI) of the wild-type HIV-1 virus, considered as the second generation analogues of Efavirenz. In addition, 56 inhibitors of the K-103N viral mutant form are also investigated. A pool of 1494 theoretical molecular descriptors provided mainly by the Dragon 5 software is explored by several methods of variable selection: forward stepwise regression, the replacement method, and the genetic algorithm approach. The optimal models found include up to seven parameters: R = 0.7991, R(l-20%-o) = 0.7233 for the case of wild-type, and R = 0.9261, R(l-5%-o) = 0.8802 for the K-103N mutation.

Alkynes↗

PubMed2006

2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors.

The inhibitory activity towards p34(cdc2)/cyclin b kinase (CBK) enzyme of 30 cytokinin-derived compounds has been successfully modelled using 2D spatial autocorrelation vectors. Predictive linear and non-linear models were obtained by forward stepwise multi-linear regression analysis (MRA) and artificial neural network (ANN) approaches respectively. A variable selection routine that selected relevant non-linear information from the data set was employed prior to networks training. The best ANN with three input variables was able to explain about 87% data variance in comparison with 80% by the linear equation using the same number of descriptors. Similarly, the neural network had higher predictive power. The MRA model showed a linear dependence between the inhibitory activities and the spatial distributions of masses, electronegativities and van der Waals volumes on the inhibitors molecules. Meanwhile, ANN model evidenced the occurrence of non-linear relationships between the inhibitory activity and the mass distribution at different topological distance on the cytokinin-derived compounds. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (SOM) built using the input variables of the best neural network.

Animals↗

PubMed2006

Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives as matrix metalloproteinase inhibitors.

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.

Acetamides↗

PubMed2006

Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptor.

Bayesian-regularized genetic neural networks (BRGNNs) were used to model the binding affinity (IC(50)) for 128 non-peptide antagonists for the human luteinizing hormone-releasing hormone (LHRH) receptor using 2D spatial autocorrelation vectors. As a preliminary step, a linear dependence was established by multiple linear regression (MLR) approach, selecting the relevant descriptors by genetic algorithm (GA) feature selection. The linear model showed to fit the training set (N=102) with R(2)=0.746, meanwhile BRGNN exhibited a higher value of R(2)=0.871. Beyond the improvement of training set fitting, the BRGNN model overcame the linear one by being able to describe 85% of test set (N=26) variance in comparison with 73% the MLR model. Our non-linear QSAR model illustrates the importance of an adequate distribution of atomic properties represented in topological frames and reveals the electronegativities, masses and polarizabilities as the most influencing atomic properties in the structures of the heterocycles under analysis for having an appropriate LHRH antagonistic activity. Furthermore, the ability of the non-linear selected variables for differentiating the data was evidenced when total data set was well distributed in a Kohonen self-organizing map (SOM).

Algorithms↗

PubMed2006

2D Autocorrelation modeling of the negative inotropic activity of calcium entry blockers using Bayesian-regularized genetic neural networks.

Negative inotropic potency of 60 benzothiazepine-like calcium entry blockers (CEBs), Diltiazem analogs, was successfully modeled using Bayesian-regularized genetic neural networks (BRGNNs) and 2D autocorrelation vectors. This approach yielded reliable and robust models whilst by means of a linear genetic algorithm (GA) search routine no multilinear regression model was found describing more than 50% of the training set. On the contrary, the optimum neural network predictor with five inputs described about 84% and 65% variances of 50 randomly selected training and test sets. Autocorrelation vectors in the nonlinear model contained information regarding 2D spatial distributions on the CEB structure of van der Waals volumes, electronegativities, and polarizabilities. However, a sensitivity analysis of the network inputs pointed out to the electronegativity and polarizability 2D topological distributions at substructural fragments of sizes 3 and 4 as the most relevant features governing the nonlinear modeling of the negative inotropic potency.

Bayes Theorem↗

PubMed2006

Improved pharmacological properties for superoxide dismutase modified with mannan.

Mannan from Sacharomyces cerevisiae was activated by oxidation with NaIO(4) (sodium m-periodate) and further linked to SOD (superoxide dismutase) via reductive alkylation with NaBH(4) (sodium borohydride). The glycosidated enzyme contained an average of 1.2 mol of polysaccharide per mol of protein and retained 52% of its initial activity. The modified enzyme was 560-fold more resistant to inactivation with H(2)O(2) and acquired a lectin-recognition capacity in respect of concanavalin A. The anti-inflammatory activity of SOD was increased 2-fold and its plasma half-life time was prolonged from 4.8 min to 1.7 h after glycosylation with the polymer.

Alkylation↗

PubMed2006

Ensembles of Bayesian-regularized genetic neural networks for modeling of acetylcholinesterase inhibition by huprines.

Acetylcholinesterase inhibition was modeled for a set of huprines using ensembles of Bayesian-regularized Genetic Neural Networks. In the Bayesian-regularized Genetic Neural Network approach the Bayesian regularization avoids overfitted regressions and the genetic algorithm allows exploring a wide pool of three-dimensional descriptors. The predictive capacity of our selected model was evaluated by averaging multiple validation sets generated as members of neural network ensembles. When 60 members are assembled, the neural network ensemble provides a reliable measure of training and test set R(2)-values of 0.945 and 0.850 respectively. In other respects, the ability of the nonlinear selected genetic algorithm space for differentiate the data were evidenced when total data set was well distributed in a Kohonen self-organizing map. The analysis of the self-organizing map zones allows establishing the main structural features differentiated by our vectorial space.

Acetylcholinesterase↗

PubMed2005

Genetic neural network modeling of the selective inhibition of the intermediate-conductance Ca2+ -activated K+ channel by some triarylmethanes using topological charge indexes descriptors.

Selective inhibition of the intermediate-conductance Ca(2+)-activated K(+ )channel (IK (Ca)) by some clotrimazole analogs has been successfully modeled using topological charge indexes (TCI) and genetic neural networks (GNNs). A neural network monitoring scheme evidenced a highly non-linear dependence between the IK (Ca) blocking activity and TCI descriptors. Suitable subsets of descriptors were selected by means of genetic algorithm. Bayesian regularization was implemented in the network training function with the aim of assuring good generalization qualities to the predictors. GNNs were able to yield a reliable predictor that explained about 97% data variance with good predictive ability. On the contrary, the best multivariate linear equation with descriptors selected by linear genetic search, only explained about 60%. In spite of when using the descriptors from the linear equations to train neural networks yielded higher fitted models, such networks were very unstable and had relative low predictive ability. However, the best GNN BRANN 2 had a Q ( 2 ) of LOO of cross-validation equal to 0.901 and at the same time exhibited outstanding stability when calculating 80 randomly constructed training/test sets partitions. Our model suggested that structural fragments of size three and seven have relevant influence on the inhibitory potency of the studied IK (Ca) channel blockers. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (KSOM) built using the inputs of the best neural network predictor.

Algorithms↗

PubMed2005

Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks.

Antifungal activity was modeled for a set of 96 heterocyclic ring derivatives (2,5,6-trisubstituted benzoxazoles, 2,5-disubstituted benzimidazoles, 2-substituted benzothiazoles and 2-substituted oxazolo(4,5-b)pyridines) using multiple linear regression (MLR) and Bayesian-regularized artificial neural network (BRANN) techniques. Inhibitory activity against Candida albicans (log(1/C)) was correlated with 3D descriptors encoding the chemical structures of the heterocyclic compounds. Training and test sets were chosen by means of k-Means Clustering. The most appropriate variables for linear and nonlinear modeling were selected using a genetic algorithm (GA) approach. In addition to the MLR equation (MLR-GA), two nonlinear models were built, model BRANN employing the linear variable subset and an optimum model BRANN-GA obtained by a hybrid method that combined BRANN and GA approaches (BRANN-GA). The linear model fit the training set (n = 80) with r2 = 0.746, while BRANN and BRANN-GA gave higher values of r2 = 0.889 and r2 = 0.937, respectively. Beyond the improvement of training set fitting, the BRANN-GA model was superior to the others by being able to describe 87% of test set (n = 16) variance in comparison with 78 and 81% the MLR-GA and BRANN models, respectively. Our quantitative structure-activity relationship study suggests that the distributions of atomic mass, volume and polarizability have relevant relationships with the antifungal potency of the compounds studied. Furthermore, the ability of the six variables selected nonlinearly to differentiate the data was demonstrated when the total data set was well distributed in a Kohonen self-organizing neural network (KNN).

Antifungal Agents↗

PubMed2005

Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks.

Artificial neural networks (ANNs) were used to model both inhibition of HIV-1 protease (K(i)) and inhibition of HIV replication (IC90) for 55 cyclic urea derivatives using constitutional and 2D descriptors. As a preliminary step, linear dependences were established by multiple linear regression (MLR) approaches, selecting the relevant descriptors by genetic algorithm (GA) feature selection. For ANN models non-linear GA feature selection was also applied. Non-linear modeling of K(i) overcame the results of the linear one using four properties, keeping in mind standard Pearson R correlation coefficients (0.931 vs. 0.862) and leave one out (LOO) cross-validation analysis (Q(LOO)2 = 0.703 vs. 0.510). On the other hand, IC90 modeling was insoluble by a linear approach: no predictive model was achieved; however, a non-linear relation was encountered according to statistic results (R = 0.891; Q(LOO)2 = 0.568). The best non-linear models suggested the influence of the presence of nitrogen atoms and the molecular volume distribution in the inhibitor structures on the HIV-1 protease inhibition as well as that the inhibition of HIV replication was dependent on the occurrence of five-member rings. Finally, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map built using the input variables of the best non-linear models.

Algorithms↗

PubMed2005

Modeling of farnesyltransferase inhibition by some thiol and non-thiol peptidomimetic inhibitors using genetic neural networks and RDF approaches.

Inhibition of farnesyltransferase (FT) enzyme by a set of 78 thiol and non-thiol peptidomimetic inhibitors was successfully modeled by a genetic neural network (GNN) approach, using radial distribution function descriptors. A linear model was unable to successfully fit the whole data set; however, the optimum Bayesian regularized neural network model described about 87% inhibitory activity variance with a relevant predictive power measured by q2 values of leave-one-out and leave-group-out cross-validations of about 0.7. According to their activity levels, thiol and non-thiol inhibitors were well-distributed in a topological map, built with the inputs of the optimum non-linear predictor. Furthermore, descriptors in the GNN model suggested the occurrence of a strong dependence of FT inhibition on the molecular shape and size rather than on electronegativity or polarizability characteristics of the studied compounds.

Enzyme Inhibitors↗

PubMed2005

Quantitative structure-activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds.

Inhibitory activity against aldose reductase enzyme of flavonoid derivatives were modelled using 11 kinds of molecular descriptors from Dragon software. Model with four Galvez Charge Indices described 67% of data variance and overtaken other models using the same number of variables. Galvez indices showed to contain important information on the relationship between the inhibitor structures and its activity by describing the molecular topology and charge transfer through the molecule. In addition, artificial neural networks were trained using charge indices from the linear models but the obtaining networks overfitted the data having low predictive power.

Aldehyde Reductase↗

PubMed2005

Improved anti-inflammatory and pharmacokinetic properties for superoxide dismutase by chemical glycosidation with carboxymethylchitin.

O-carboxymethylchitin (molecular weight = 1.07 x 10(5), degree of carboxymethylation = 80%, degree of N-acetylation = 91%) was chemically attached to superoxide dismutase by the formation of amide linkages through a carbodiimide catalyzed reaction. The glycosidated enzyme contained about 1.8 mole of polysaccharide per mole of protein and retained 57% of the initial catalytic activity. The anti-inflammatory activity of the enzyme was 2.4 times increased after conjugation with the polysaccharide. The modified superoxide dismutase preparation was remarkably more resistant to inactivation with H(2)O(2) and its plasma half-life time was prolonged from 4.8 min to 69 h.

Animals↗

PubMed2003

Effects of beta-cyclodextrin-dextran polymer on stability properties of trypsin.

Dextran modified with the mono-6-pentylene-diamino-6-deoxy-beta-cyclodextrin derivative was evaluated as a thermoprotectant additive for trypsin. The optimum temperature for trypsin activity was increased by 7 degrees C in the presence of this polymer. The enzyme thermostability was increased from 48.5 to 64 degrees C over 10 min of incubation, and the activation free energy of thermoinactivation at 50 degrees C was increased by 4.1 kJ/mol in the presence of the additive. Trypsin was 6-fold more resistant to autolytic inactivation at alkaline pH in the presence of the polymer.

Animals↗

PubMedDate not supplied

Modeling of cyclin-dependent kinase inhibition by 1H-pyrazolo[3,4-d]pyrimidine derivatives using artificial neural network ensembles.

Artificial neural network ensembles were used for modeling the cyclin-dependent kinase inhibition of 1H-pyrazolo[3,4-d]pyrimidine derivatives. The structural characteristics of these inhibitors were encoded in relevant 3D-spatial descriptors extracted by genetic algorithm feature selection. Bayesian-regularized multilayer neural networks, trained by the back-propagation algorithm, were developed using these variables as inputs. The predictive power of the model was tested by leave-one-out cross validation. In addition, for a more rigorous measure of the predictive capacity, multiple validation sets were randomly generated as members of neural network ensembles, which makes doing averaged predictions feasible. In this way, the predictive power was analyzed accounting for the averaged test set R values and test set mean-square errors. Otherwise, Kohonen self-organizing maps were used as an additional tool for the same modeling. The location of the inhibitors in a map facilitates the analysis of the connection between compounds and serves as a useful tool for qualitative predictions.

Algorithms↗

PubMedDate not supplied

Amino Acid Sequence Autocorrelation vectors and ensembles of Bayesian-Regularized Genetic Neural Networks for prediction of conformational stability of human lysozyme mutants.

Development of novel computational approaches for modeling protein properties from their primary structure is a main goal in applied proteomics. In this work, we reported the extension of the autocorrelation vector formalism to amino acid sequences for encoding protein structural information with modeling purposes. Amino Acid Sequence Autocorrelation (AASA) vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. A total of 720 AASA descriptors were tested for building predictive models of the thermal unfolding Gibbs free energy change of human lysozyme mutants. In this sense, ensembles of Bayesian-Regularized Genetic Neural Networks (BRGNNs) were used for obtaining an optimum nonlinear model for the conformational stability. The ensemble predictor described about 88% and 68% variance of the data in training and test sets, respectively. Furthermore, the optimum AASA vector subset was shown not only to successfully model unfolding thermal stability but also to distribute wild-type and mutant lysozymes on a stability Self-organized Map (SOM) when used for unsupervised training of competitive neurons.

Algorithms↗