RESUMEN
A target-ligand QSAR approach using autocorrelation formalism was developed for modeling the inhibitory potency (pIC(50)) 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 (R(2)(test)) 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.
Asunto(s)
Acetamidas/química , Inhibidores de la Metaloproteinasa de la Matriz , Modelos Moleculares , Algoritmos , Inhibidores Enzimáticos/química , Interacciones Hidrofóbicas e Hidrofílicas , Metaloproteinasas de la Matriz/química , Redes Neurales de la Computación , Relación Estructura-Actividad Cuantitativa , Electricidad EstáticaRESUMEN
Voltage-gated K(+) ion channels (VKCs) are membrane proteins that regulate the passage of potassium ions through membranes. This work reports a classification scheme of VKCs according to the signs of three electrophysiological variables: activation threshold voltage (V(t)), half-activation voltage (V(a50)) and half-inactivation voltage (V(h50)). A novel 3D pseudo-folding graph representation of protein sequences encoded the VKC sequences. Amino acid pseudo-folding 3D distances count (AAp3DC) descriptors, calculated from the Euclidean distances matrices (EDMs) were tested for building the classifiers. Genetic algorithm (GA)-optimized support vector machines (SVMs) with a radial basis function (RBF) kernel well discriminated between VKCs having negative and positive/zero V(t), V(a50) and V(h50) values with overall accuracies about 80, 90 and 86%, respectively, in crossvalidation test. We found contributions of the "pseudo-core" and "pseudo-surface" of the 3D pseudo-folded proteins to the discrimination between VKCs according to the three electrophysiological variables.
Asunto(s)
Canales de Potasio con Entrada de Voltaje/química , Canales de Potasio con Entrada de Voltaje/clasificación , Pliegue de Proteína , Algoritmos , Secuencia de Aminoácidos , Inteligencia Artificial , Datos de Secuencia Molecular , Canales de Potasio con Entrada de Voltaje/genética , Reproducibilidad de los ResultadosRESUMEN
This work reports a novel 3D pseudo-folding graph representation of protein sequences for modeling purposes. Amino acids euclidean distances matrices (EDMs) encode primary structural information. Amino Acid Pseudo-Folding 3D Distances Count (AAp3DC) descriptors, calculated from the EDMs of a large data set of 1363 single protein mutants of 64 proteins, were tested for building a classifier for the signs of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) upon single mutations. An optimum support vector machine (SVM) with a radial basis function (RBF) kernel well recognized stable and unstable mutants with accuracies over 70% in crossvalidation test. To the best of our knowledge, this result for stable mutant recognition is the highest ever reported for a sequence-based predictor with more than 1000 mutants. Furthermore, the model adequately classified mutations associated to diseases of human prion protein and human transthyretin.
Asunto(s)
Mutación Puntual , Pliegue de Proteína , Proteínas/química , Secuencia de Aminoácidos , Animales , Humanos , Datos de Secuencia Molecular , Conformación Proteica , Proteínas/genéticaRESUMEN
Development of novel computational approaches for modeling protein properties is a main goal in applied Proteomics. In this work, we reported the extension of the radial distribution function (RDF) scores formalism to proteins for encoding 3D structural information with modeling purposes. Protein-RDF (P-RDF) scores measure spherical distributions on protein 3D structure of 48 amino acids/residues properties selected from the AAindex data base. P-RDF scores were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) of chymotrypsin inhibitor 2 upon mutations. In this sense, an ensemble of Bayesian-Regularized Genetic Neural Networks (BRGNNs) yielded an optimum nonlinear model for the conformational stability. The ensemble predictor described about 84% and 70% variance of the data in training and test sets, respectively.
Asunto(s)
Teorema de Bayes , Redes Neurales de la Computación , Péptidos/química , Proteínas de Plantas/química , Proteínas/química , Algoritmos , Biología Computacional/métodos , Mutación , Péptidos/genética , Proteínas de Plantas/genética , Conformación ProteicaRESUMEN
Development of novel computational approaches for modeling protein properties from their primary structure is the 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 data base. A total of 720 AASA descriptors were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (delta deltaG) of gene V protein upon mutation. 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 66% variance of the data in training and test sets respectively. Furthermore, the optimum AASA vector subset not only helped to successfully model unfolding stability but also well distributed wild-type and gene V protein mutants on a stability self-organized map (SOM), when used for unsupervised training of competitive neurons.
Asunto(s)
Vectores Genéticos/genética , Modelos Biológicos , Conformación Proteica , Proteínas/química , Proteínas/genética , Secuencia de Aminoácidos , Fenómenos Químicos , Química Física , Biología Computacional , Simulación por Computador , Mutación/genética , Redes Neurales de la Computación , Pliegue de Proteína , Proteínas/metabolismoRESUMEN
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.
Asunto(s)
Proteómica/métodos , Receptores Acoplados a Proteínas G/química , Receptores Acoplados a Proteínas G/genética , Algoritmos , Secuencia de Aminoácidos , Inteligencia Artificial , Bases de Datos de Proteínas , Humanos , Técnicas In Vitro , Análisis de los Mínimos Cuadrados , Modelos Lineales , Modelos Moleculares , Mutación , Dinámicas no Lineales , Proteómica/estadística & datos numéricos , Relación Estructura-Actividad Cuantitativa , Receptores Acoplados a Proteínas G/metabolismo , Receptores de GhrelinaRESUMEN
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.
Asunto(s)
Teorema de Bayes , Muramidasa/química , Mutación , Redes Neurales de la Computación , Algoritmos , Humanos , Muramidasa/genética , Conformación ProteicaRESUMEN
The polysaccharide O-carboxymethyl-poly-beta-cyclodextrin was synthesized (molecular mass 13,000 Da, 40% carboxy groups) and attached to the surface of bovine pancreatic trypsin. The resulting neoglycoenzyme retained high proteolytic and esterolytic activity and contained approx. 1.0 mol of polymer/mol of enzyme. The optimum temperature for trypsin activity was increased by 10 degrees C after this transformation. Thermostability of the polymer-enzyme complex was increased by about 14 degrees C over 10 min incubation. The conjugate was also more resistant to thermal inactivation at different temperatures, ranging from 45 to 60 degrees C, demonstrating the influence of supramolecular and polymer-protein electrostatic interactions on trypsin thermostabilization. Additionally, the conjugate was 36-fold more resistant to the action of the anionic surfactant SDS. This modification also protected the enzyme from autolysis at alkaline pH.