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Xanthine oxidase is a known therapeutic target for the treatment of hyperuricemia and related diseases. Despite the availability of current drugs such as allopurinol and febuxostat, the search for new compounds to effectively inhibit this enzyme remains relevant. In our study, 75 virtual structures of 4-(5-aminosubstituted-4-cyanooxazol-2-yl)benzoic acids with structural similarity to febuxostat were designed for evaluation of their potency against xanthine oxidase. After molecular docking simulations, eight compounds were selected for synthesis and in vitro testing. The synthesized compounds were found to exhibit in vitro xanthine oxidase inhibitory activity in the nanomolar concentration range. The most effective inhibitors with 4-benzylpiperidin-1-yl or 1,2,3,4-tetrahydroisoquinolin-2-yl substituents at position 5 of the oxazole ring had IC50 values close to that of febuxostat. The kinetic data suggest a mixed-type inhibition when the inhibitor binds preferentially to the free enzyme rather than to the enzyme-substrate complex. Molecular docking and molecular dynamic simulations were carried out to get insight into the key interactions of the inhibitors bound to the xanthine oxidase active site.

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Microtubules play a significant role in cell growth and functioning. Therefore inhibition of the microtubule assemblies has emerged as one of the most promising cancer treatment strategies. Predictive QSAR models were built on a series of selective inhibitors of the tubulin were performed by using Associative Neural Networks (ANN). To overcome the problem of data overfitting due to the descriptor selection, a 5-fold cross-validation with variable selection in each step of the analysis was used. All developed QSAR models showed excellent statistics on the training (total accuracy: 0.96-0.97) and test sets (total accuracy: 0.95-97). The models were further validated by 11 synthesized 1,3-oxazole derivatives and all of them showed inhibitory effect on the Hep-2 cancer cell line. The most promising compound showed inhibitory activity IC50=60.2µM. In order to hypothesize their mechanism of action the top three compounds were docked in the colchicine binding site of tubulin and showed reasonable docking scores as well as favorable interactions with the protein.

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Automated docking is one of the most important tools for structure-based drug design that allows prediction of ligand binding poses and also provides an estimate of how well small molecules fit in the binding site of a protein. A new scoring function based on AutoDock and AutoDock Vina has been introduced. The new hybrid scoring function is a linear combination of the two scoring function components derived from a multiple linear regression fitting procedure. The scoring function was built on a training set of 2412 protein-ligand complexes from pdbbind database (www.pdbbind.org.cn, version 2012). A test set of 313 complexes that appeared in the 2013 version was used for validation purposes. The new hybrid scoring function performed better than the original functions, both on training and test sets of protein-ligand complexes, as measured by the non-parametric Pearson correlation coefficient, R, mean absolute error (MAE), and root-mean-square error (RMSE) between the experimental binding affinities and the docking scores. The function also gave one of the best results among more than 20 scoring functions tested on the core set of the pdbbind database. The new AutoDock hybrid scoring function will be implemented in modified version of AutoDock.

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Molecular docking of small molecules in the protein binding sites is the most widely used computational technique in modern structure-based drug discovery. Although accurate prediction of binding modes of small molecules can be achieved in most cases, estimation of their binding affinities remains mediocre at best. As an attempt to improve the correlation between the inhibitory constants, pKi, and scoring, we created a new, hybrid scoring function. The new function is a linear combination of the terms of the scoring functions of AutoDock and AutoDock Vina. It was trained on 2,412 protein-ligand complexes from the PDBbind database (www.pdbbind.org.cn, version 2012) and validated on a set of 313 complexes released in the 2013 version as a test set. The new function was included in a modified version of AutoDock. The hybrid scoring function showed a statistically significant improvement in both training and test sets in terms of correlation with and root mean square and mean absolute errors in prediction of pKi values. It was also tested on the CSAR 2014 Benchmark Exercise dataset (team T) and produced reasonably good results.

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Monoester derivatives of thiacalix[4]arene tetrakis(methylphosphonic) acid were found to be capable of inhibiting protein tyrosine phosphatase 1B. In addition, these compounds can strongly bind to human serum albumin.

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α,α-Difluoro-ß-ketophosphonated derivatives of tetraazamacrocycles were synthesized and found to be potential inhibitors of protein tyrosine phosphatases. N-Substituted conjugates of cyclam and cyclen with bioisosteric phosphonate groups displayed good activities toward T-cell protein tyrosine phosphatase with IC50 values in the micromolar to nanomolar range and showed selectivity over PTP1B, CD45, SHP2, and PTPß. Kinetic studies indicated that the inhibitors can occupy the region of the active site of TC-PTP. This study demonstrates a new approach which employs tetraazamacrocycles as a molecular platform for designing inhibitors of protein tyrosine phosphatases.

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In this study, we identified water-soluble C60 and C70 fullerene derivatives as a novel class of protein tyrosine phosphatase inhibitors. The evaluated compounds were found to inhibit CD45, PTP1B, TC-PTP, SHP2, and PTPß with IC50 values in the low micromolar to high nanomolar range. These results demonstrate a new strategy for designing effective nanoscale protein tyrosine phosphatase inhibitors.

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Quantitative structure-activity relationship studies on a series of selective inhibitors of thrombin and factor Xa were performed by using Associative Neural Network. To overcome the problem of overfitting due to descriptor selection, 5-fold cross-validation with variable selection in each step of the analysis was performed. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q2=0.74-0.87 for regression models. Predictions for the external evaluation sets obtained accuracies in the range of 0.71-0.82 for regressions. The proposed models can be potential tools for finding new drug candidates.

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Сalix[4]arenes bearing methylenebisphosphonic or hydroxymethylenebisphosphonic acid fragments at the wide rim of the macrocycle were studied as inhibitors of PTP1B. Some of the inhibitors showed IC50 values in the micromolar range and good selectivity in comparison with other protein tyrosine phosphatases such as TC-PTP, PTPß, LAR, and CD45. Kinetic studies indicated that the calix[4]arene derivatives influence PTP1B activity as slow-binding inhibitors. Based on molecular docking results, the binding modes of the macrocyclic bisphosphonates in the active centre of PTP1B are discussed.

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Hundred and two binding sites from 91 Protein Data Bank files for protein tyrosine phosphatase 1B with different ligands have been compared. It was found that they can be divided into five clusters. Additional clusters were formed by the unliganded and oxidized enzyme. The centroids of the clusters can be used as starting points for further studies of enzyme-inhibitor interaction by computer simulations. A special software tool has been created for the investigation of protein tyrosine phosphatase 1B and other enzymes. It performs multiple comparisons of selected parts of Protein Data Bank files, as well as further clustering, and determines mobility of separate residues.

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A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC50 of new drug-like candidates at early stages of drug development.

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The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

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Inhibition of Yersinia protein tyrosine phosphatase by calix[4]arene mono-, bis-, and tetrakis(methylenebisphosphonic) acids as well as calix[4]arene and thiacalix[4]arene tetrakis(methylphosphonic) acids have been investigated. The kinetic studies revealed that some compounds in this class are potent competitive inhibitors of Yersinia PTP with inhibition constants in the low micromolar range. The binding modes of macrocyclic phosphonate derivatives in the enzyme active center have been explained using computational docking approach. The results obtained indicate that calix[4]arenes are promising scaffolds for the development of inhibitors of Yersinia PTP.

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The inhibition effects of enantiomerically pure alpha-(N-benzylamino)benzylphosphonic acids and their derivatives on human prostatic acid phosphatase have been investigated. As expected, (R)-alpha-(N-benzylamino)benzylphosphonic acid demonstrated higher affinity for the enzyme than (S)-enantiomer. At the same time, (1R,2S)-phenyl[(1-phenylethyl)amino]methylphosphonic acid was found to be a significantly weaker inhibitor than its (1S,2R)-analogue. The enantioselectivity has been explained using a molecular modeling approach by computational docking of inhibitors into active center of prostatic acid phosphatase.

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Internet technology offers an excellent opportunity for the development of tools by the cooperative effort of various groups and institutions. We have developed a multi-platform software system, Virtual Computational Chemistry Laboratory, http://www.vcclab.org, allowing the computational chemist to perform a comprehensive series of molecular indices/properties calculations and data analysis. The implemented software is based on a three-tier architecture that is one of the standard technologies to provide client-server services on the Internet. The developed software includes several popular programs, including the indices generation program, DRAGON, a 3D structure generator, CORINA, a program to predict lipophilicity and aqueous solubility of chemicals, ALOGPS and others. All these programs are running at the host institutes located in five countries over Europe. In this article we review the main features and statistics of the developed system that can be used as a prototype for academic and industry models.

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This article provides a systematic study of several important parameters of the Associative Neural Network (ASNN), such as the number of networks in the ensemble, distance measures, neighbor functions, selection of smoothing parameters, and strategies for the user-training feature of the algorithm. The performance of the different methods is assessed with several training/test sets used to predict lipophilicity of chemical compounds. The Spearman rank-order correlation coefficient and Parzen-window regression methods provide the best performance of the algorithm. If additional user data is available, an improved prediction of lipophilicity of chemicals up to 2-5 times can be calculated when the appropriate smoothing parameters for the neural network are selected. The detected best combinations of parameters and strategies are implemented in the ALOGPS 2.1 program that is publicly available at http://www.vcclab.org/lab/alogps.