RESUMO
BACKGROUND: Virtual methodologies have become essential components of the drug discovery pipeline. Specifically, structure-based drug design methodologies exploit the 3D structure of molecular targets to discover new drug candidates through molecular docking. Recently, dual target ligands of the Adenosine A2A Receptor and Monoamine Oxidase B enzyme have been proposed as effective therapies for the treatment of Parkinson's disease. METHODS: In this paper we propose a structure-based methodology, which is extensively validated, for the discovery of dual Adenosine A2A Receptor/Monoamine Oxidase B ligands. This methodology involves molecular docking studies against both receptors and the evaluation of different scoring functions fusion strategies for maximizing the initial virtual screening enrichment of known dual ligands. RESULTS: The developed methodology provides high values of enrichment of known ligands, which outperform that of the individual scoring functions. At the same time, the obtained ensemble can be translated in a sequence of steps that should be followed to maximize the enrichment of dual target Adenosine A2A Receptor antagonists and Monoamine Oxidase B inhibitors. CONCLUSION: Information relative to docking scores to both targets have to be combined for achieving high dual ligands enrichment. Combining the rankings derived from different scoring functions proved to be a valuable strategy for improving the enrichment relative to single scoring function in virtual screening experiments.
Assuntos
Antagonistas do Receptor A2 de Adenosina/uso terapêutico , Simulação de Acoplamento Molecular , Inibidores da Monoaminoxidase/uso terapêutico , Monoaminoxidase/metabolismo , Doença de Parkinson/tratamento farmacológico , Receptor A2A de Adenosina/metabolismo , Antagonistas do Receptor A2 de Adenosina/química , Animais , Sítios de Ligação/efeitos dos fármacos , Humanos , Ligantes , Inibidores da Monoaminoxidase/química , Ligação Proteica/efeitos dos fármacos , Relação Estrutura-Atividade , Interface Usuário-ComputadorRESUMO
DNA gyrase is a well-established antibacterial target consisting of two subunits, GyrA and GyrB, in a heterodimer A(2)B(2), where GyrB catalyzes the hydrolysis of ATP. Cyclothialidine (Ro 09-1437) has been considered as a promising inhibitor whose modifications might lead to more potent compounds against the enzyme. We report here for the first time, QSAR studies regarding to ATPase inhibitors of DNA Gyrase. 1D, 2D and 3D descriptors from DRAGON software were used on a set of 42 cyclothialidine derivatives. Based on the core of the cyclothialidine GR122222X, different conformations were created by using OMEGA. FRED was used to dock these conformers in the cavity of the GyrB subunit to select the best conformations, paying special attention to the 12-membered ring. Three QSAR models were developed considering the dimension of the descriptors. The models were robust, predictive and good in statistical significance, over 70% of the experimental variance was explained. Interpretability of the models was possible by extracting the SAR(s) encoded by these predictive models. Analyzing the compound-enzyme interactions of the complexes obtained by docking allowed us to increase the reliability of the information obtained for the QSAR models.
Assuntos
Antibacterianos/química , DNA Girase/química , Peptídeos Cíclicos/química , Inibidores da Topoisomerase II/química , Trifosfato de Adenosina/química , Bactérias/química , Bactérias/enzimologia , Sítios de Ligação , Desenho de Fármacos , Simulação de Acoplamento Molecular , Ligação Proteica , Relação Quantitativa Estrutura-Atividade , TermodinâmicaRESUMO
Chemical carcinogenicity is of primary interest, because it drives much of the current regulatory actions regarding new and existing chemicals, and its conventional experimental test takes around three years to design, conduct, and interpret as well as the costs of hundreds of millions of dollars, millions of skilled personnel hours, and several animal lives. Both academia and private companies are actively trying to develop alternative methods, such as QSAR models. This paper reports a QSAR study for predicting carcinogenic potency of nitrocompounds bioassayed in female rats. Several different theoretical molecular descriptors, calculated only on the basis of knowledge of the molecular structure and an efficient variable selection procedure, such as Genetic Algorithm, led to models with satisfactory predictive ability. But the best-final QSAR model is based on the GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors capturing a reasonable interpretation. In fact, structural features such as molecular shape-linear, branched, cyclic, and polycyclic--and bond length are some of the key factors flagging the carcinogenicity of this set of nitrocompounds. This QSAR model, after removal of one identified nitrocompound outlier, is able to explain around 86% of the variance in the experimental activity and manifest good predictive ability as indicated by the higher q(2)s of cross- and external-validations, which demonstrate the practical value of the final QSAR model for screening and priority testing. This model can be applied to nitrochemicals different from the studied nitrocompounds (even those not yet synthesized) as it is based on theoretical molecular descriptors that might be easily and rapidly calculated.
Assuntos
Neoplasias/induzido quimicamente , Nitrocompostos/farmacologia , Relação Quantitativa Estrutura-Atividade , Animais , Avaliação Pré-Clínica de Medicamentos/métodos , Feminino , Nitrocompostos/química , RatosRESUMO
Prevention of environmentally induced cancers is a major health problem of which solutions depend on the rapid and accurate screening of potential chemical hazards. Lately, theoretical approaches such as the one proposed here - Quantitative Structure-Activity Relationship (QSAR) - are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the Topological Substructural Molecular Design (TOPS-MODE) approach, aiming at predicting the rodent carcinogenicity of a set of nitroso-compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises nitrosoureas (14 chemicals), N-nitrosamines (18 chemicals) C-nitroso-compounds (1 chemical), nitrosourethane (1 chemical) and nitrosoguanidine (1 chemical), which have been bioassayed in male rat using gavage as the route of administration. Here we are especially concerned in gathering the role of both parameters on the carcinogenic activity of this family of compounds. First, the regression model was derived, upon removal of one identified nitrosamine outlier, and was able to account for more than 84% of the variance in the experimental activity. Second, the TOPS-MODE approach afforded the bond contributions -- expressed as fragment contributions to the carcinogenic activity -- that can be interpreted and provide tools for better understanding the mechanisms of carcinogenesis. Finally, and most importantly, we demonstrate the potentialities of this approach towards the recognition of structural alerts for carcinogenicity predictions.
Assuntos
Carcinógenos/química , Carcinógenos/toxicidade , Compostos Nitrosos/química , Compostos Nitrosos/toxicidade , Animais , Testes de Carcinogenicidade , Masculino , Modelos Moleculares , Relação Quantitativa Estrutura-Atividade , RatosRESUMO
The radial distribution function (RDF) approach has been applied to the study of the A(1) adenosine receptors agonist effect of 32 adenosine analogues. A model able to describe more than 79% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the three different approaches, including the use of 2D autocorrelations, BCUT and 3D-MORSE descriptors were able to explain more than 72% of the variance in the mentioned property with the same number of variables in the equation. In addition, we established a comparison with other models reported by us for this receptor subtype using this data set, and the RDF descriptors continue getting the best results.
Assuntos
Agonistas do Receptor A1 de Adenosina , Adenosina/análogos & derivados , Modelos Biológicos , Adenosina/química , Adenosina/farmacologia , Animais , Relação Quantitativa Estrutura-Atividade , RatosRESUMO
In order to minimize expensive drug failures it is essential to determine the potential biological activity of new candidates as early as possible. In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of a drugs biological activity is advisable even before synthesis and this can be achieved using predictive biological activity methods. In this sense, computer aided rational drug design strategies like Quantitative Structure Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of traditional QSAR applications in the development of new agonist molecules with affinity toward adenosine receptors is scarce. This review attempts to summarize the current level of knowledge concerning computational affinity predictions for adenosine receptors using QSAR models based on knowledge of the agonist ligands. Several computational protocols and different 2D and 3D descriptors have been described in the literature for these targets, but more effort is still required in this area.
Assuntos
Adenosina/análogos & derivados , Adenosina/uso terapêutico , Desenho de Fármacos , Agonistas do Receptor Purinérgico P1 , Relação Quantitativa Estrutura-Atividade , Adenosina/química , Humanos , Ligantes , Receptor A3 de Adenosina/fisiologia , Receptores Purinérgicos P1/fisiologiaRESUMO
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.
Assuntos
Quinases Ciclina-Dependentes/antagonistas & inibidores , Citocininas/farmacologia , Modelos Biológicos , Animais , Proteína Quinase CDC2/antagonistas & inibidores , Citocininas/química , Feminino , Técnicas In Vitro , Matemática , Redes Neurais de Computação , Inibidores de Proteínas Quinases/química , Inibidores de Proteínas Quinases/farmacologia , Análise de Regressão , Estrelas-do-Mar/enzimologia , Relação Estrutura-AtividadeRESUMO
A TOPological Sub-structural MOlecular DEsign (TOPS-MODE) approach was used to predict the soil sorption coefficients for a set of pesticide compounds. The obtained model accounted for more than 85% of the data variance and demonstrated the importance of the dipole moment, the standard distance, the polarizability, and the hydrophobicity in describing the property under study. In addition, we compared this new model to a previous one using different descriptors such as WHIM and molecular connectivity indices. Finally, the TOPS-MODE was used to calculate the contribution of different fragments to the soil sorption coefficient of the compounds studied. The present approximation proved to be a good method for studying the soil sorption coefficient for pesticides, but it could also be extended to other series of chemicals.
Assuntos
Modelos Químicos , Praguicidas/química , Relação Quantitativa Estrutura-Atividade , Poluentes do Solo/análise , Solo/análise , Adsorção , Simulação por Computador , Praguicidas/toxicidadeRESUMO
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.
Assuntos
Inibidores Enzimáticos/farmacologia , Farnesiltranstransferase/antagonistas & inibidores , Modelos Moleculares , Mimetismo Molecular , Redes Neurais de Computação , Peptídeos/farmacologia , Compostos de Sulfidrila/farmacologia , Inibidores Enzimáticos/química , Peptídeos/química , Compostos de Sulfidrila/químicaRESUMO
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.
Assuntos
Aldeído Redutase/antagonistas & inibidores , Flavonoides/química , Flavonoides/farmacologia , Relação Quantitativa Estrutura-Atividade , Aldeído Redutase/química , Biologia Computacional/métodos , Simulação por Computador , Estrutura Molecular , Análise de RegressãoRESUMO
The carcinogenic activity has been investigated by using a topological substructural molecular design approach (TOPS-MODE). A discriminant model was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 189 compounds. The percentage of correct classification was 76.32%. The predictive power of the model was validated by three test: an external test set (compounds not used in the develop of the model, with a 72.97% of good classification), a leave-group-out cross-validation procedure (4-fold full cross-validation, removing 20% of compounds in each cycle, with a good prediction of 76.31%) and two external prediction sets (the first and second exercises of the National Toxicology Program). This methodology evidenced that the hydrophobicity increase the carcinogenic activity and the dipole moment of the molecule decrease it; suggesting the capacity of the TOPS-MODE descriptors to estimate this property for new drug candidates. Finally, the positive and negative fragment contributions to the carcinogenic activity were identified (structural alerts) and their potentialities in the lead generation process and in the design of 'safer' chemicals were evaluated.
Assuntos
Testes de Carcinogenicidade/estatística & dados numéricos , Carcinógenos/química , Simulação por Computador , Modelos Teóricos , Relação Quantitativa Estrutura-Atividade , Animais , Bases de Dados Factuais/estatística & dados numéricos , Conformação Molecular , Valor Preditivo dos Testes , RoedoresRESUMO
TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was used to assess acute aquatic toxicity of a series of 69 benzene derivatives. The obtained model was able to explain more than 88% of data variance, stressing the importance of molecule hydrophobicity and its dipolar moment, as well as the distance between their bonds to describe the property under study. On the other hand, this model was better than those obtained with Dragon software (Constitutional, Galvez topological charges indices and BCUT) using the same number of variables. This approach proved to be a very good method to assess acute aquatic toxicity of these king of compounds, which could be applied to other series of substances.
Assuntos
Derivados de Benzeno/toxicidade , Animais , Análise por Conglomerados , Cyprinidae , Relação Quantitativa Estrutura-AtividadeRESUMO
A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in anti-inflammatory compounds using computer-aided molecular design. Two series of compounds, one containing anti-inflammatory and the other containing nonanti-inflammatory compounds were processed by a k-means cluster analysis in order to design the training and prediction sets. A linear classification function to discriminate the anti-inflammatory from the inactive compounds was developed. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441). While in the prediction set, they showed an overall predictability of 88% and 84% for active and inactive compounds, being the global percentage of good classification of 85%. Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments towards anti-inflammatory property, also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general 'in silico' technique to experimentation in anti-inflammatory discovery.