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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.

За допомогою докінгу відомих інгібіторів досліджено типові конформації протеїнтирозинфосфатази 1B.Найкращої конформації не виявлено, хоча для кожного інгібітора існує найбільш відповідна конформація.

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 PTPβ 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.

The study of the inhibition of human protein tyrosine phosphatases by water-soluble [60]fullerene derivatives bearing five organic addends loaded with carboxylic groups demonstrated that a fullerene core is responsible for interactions with the enzymes while organic addends play a minor role modifying the selectivity of the inhibition.

Conformations of the catalytic center of protein tyrosine phosphatase 1B (PTP1B) and surrounding loops are known to be important in catalysis and inhibition of the enzyme. There were 98 conformations from 88 PDB files representing PTP1B with different ligands which were analyzed to investigate the details of loop 110-120 movement and mobility of separate residues. The differences were identified by a special software tool which performs multiple comparisons of selected parts of PDB files. The conformations were divided into 6 clusters. It was found that the loop formed by residues 110-120 can be characterized by four main conformations. Predominantly, the loop 110-120 adopts the main conformation and keeps it during WPD loop movement. Three other conformations appear to be stabilized in case of closed WPD loop and seem to be favorable for PTP1B with subunit structure.

The cone shaped сalix[4]arene hydroxyphosponous, ketophosphonous, methylenebisphosphonous, and hydroxymethylenebisphosphonous acids are specific inhibitors of glutathione S-transferase, ATPases of smooth muscle cells, fibrin polymerization processes

Display Omitted A series of new predictive QSAR models is presented.Artificial Neural Networks used to build QSAR models.The models demonstrated good predictive ability.Cytotoxic activity of 3 compounds against Hep-2 cell line was the highest.Docking studies of 3 compounds predict their binding to the colchicine binding site of tubulin. 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: 0.960.97) and test sets (total accuracy: 0.9597). 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=60.2M. 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.

A series of dipropoxy-, tripropoxy- and tetrapropoxycalix(4)arenes bearing one or two fragments of α-hydroxymethylphosphonic acid at the upper rim of the macrocycle was prepared by the reaction of the corresponding mono- and di-formylcalixarenes with sodium salts of dialkyl phosphites or with trialkyl (tristrimethylsilyl)phosphites followed by dealkylation (desilylation) of the ester derivatives. The conformations of the macrocyclic skeleton and the stereoisomeric forms of the compounds obtained were investigated by 1 H NMR. The resulting α- hydroxymethylphosphonic acids were found to be able to inhibit the activity of glutathione S- transferase in vitro.

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.

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.

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.

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.

α,α-Difluoro-β-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 PTPβ. 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.

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.

Inhibition of some PTPases by mono-, bis-, and tetrakisphosphorylated calix[4]arenes have been investigated. Kinetics and computational docking studies provide a basis for understanding the mechanism of enzyme–inhibitor complexation, which may be useful for the development of potent inhibitors of the PTPases.

Phosphorylated derivatives of calix[4]arene were evaluated as inhibitors of glutathione S-transferase. Computer-simulated docking studies showed that phosphorylated macrocycle is located near the G-site of the enzyme.

The molecular weight and electrotopological E-state indices were used to estimate by Artificial Neural Networks aqueous solubility for a diverse set of 1291 organic compounds. The neural network with 33-4-1 neurons provided highly predictive results with r(2) = 0.91 and RMS = 0.62. The used parameters included several combinations of E-state indices with similar properties. The calculated results were similar to those published for these data by Huuskonen (2000). However, in the current study only E-state indices were used without need of additional indices (the molecular connectivity, shape, flexibility and indicator indices) also considered in the previous study. In addition, the present neural network contained three times less hidden neurons. Smaller neural networks and use of one homogeneous set of parameters provides a more robust model for prediction of aqueous solubility of chemical compounds. Limitations of the developed method for prediction of large compounds are discussed. The developed approach is available online at http://www.lnh.unil.ch/~itetko/logp.

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.

Artificial neural networks were used to analyze and predict the human immunodeficiency virus type 1 reverse transcriptase inhibitors. The training and control sets included 44 molecules (most of them are well-known substances such as AZT, dde, etc.). The activities of the molecules were taken from literature. Topological indices were calculated and used as molecular parameters. The four most informative parameters were chosen and applied to predict activities of both new and control molecules. We used a network pruning algorithm and network ensembles to obtain the final classifier. Increasing of neural network generalization of the new data was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly very active. It was confirmed by further biological tests.

The inhibition effects of enantiomerically pure α-(N-benzylamino)benzylphosphonic acids and their derivatives on human prostatic acid phosphatase have been investigated. As expected, (R)-α-(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.

Isoindolin-1-one-2-ylmethylphosphonic acid derivatives have been synthesized as potential inhibitors of protein tyrosine phosphatases. Molecular docking calculations and assays in vitro showed that isoindolin-1-one-2-ylmethylphosphonic acid can be used as a scaffold for the design of new inhibitors of the enzymes.

A new method, ALOGPS v 2.0 (http://www.lnh.unil.ch/~itetko/logp/), for the assessment of n-octanol/water partition coefficient, log P, was developed on the basis of neural network ensemble analysis of 12 908 organic compounds available from PHYSPROP database of Syracuse Research Corporation. The atom and bond-type E-state indices as well as the number of hydrogen and non-hydrogen atoms were used to represent the molecular structures. A preliminary selection of indices was performed by multiple linear regression analysis, and 75 input parameters were chosen. Some of the parameters combined several atom-type or bond-type indices with similar physicochemical properties. The neural network ensemble training was performed by efficient partition algorithm developed by the authors. The ensemble contained 50 neural networks, and each neural network had 10 neurons in one hidden layer. The prediction ability of the developed approach was estimated using both leave-one-out (LOO) technique and training/test protocol. In case of interseries predictions, i.e., when molecules in the test and in the training subsets were selected by chance from the same set of compounds, both approaches provided similar results. ALOGPS performance was significantly better than the results obtained by other tested methods. For a subset of 12 777 molecules the LOO results, namely correlation coefficient r(2)= 0.95, root mean squared error, RMSE = 0.39, and an absolute mean error, MAE = 0.29, were calculated. For two cross-series predictions, i.e., when molecules in the training and in the test sets belong to different series of compounds, all analyzed methods performed less efficiently. The decrease in the performance could be explained by a different diversity of molecules in the training and in the test sets. However, even for such difficult cases the ALOGPS method provided better prediction ability than the other tested methods. We have shown that the diversity of the training sets rather than the design of the methods is the main factor determining their prediction ability for new data. A comparative performance of the methods as well as a dependence on the number of non-hydrogen atoms in a molecule is also presented.

In this paper we describe an Internet Java-based technology that allows scientists to make their analytical software available worldwide. The implementation of this technology is exemplified by programs for the calculation of the lipophilicity and water solubility of chemical compounds available at http://www.lnh.unil.ch/~itetko/logp. Both these molecular properties are key parameters in quantitative structure-activity relationship studies and are used to provide invaluable information for the overall understanding of the uptake distribution, biotransformation, and elimination of a wide variety of chemicals. The compounds can be analyzed in batch or single-compound mode. The single-compound analysis offers the possibility to compare our results with several popular lipophilicity calculation methods, including CLOGP, KOWWIN, and XLOGP. The chemical compounds are analyzed according to SMILES line notation that can be prepared with the JME molecular editor of Peter Ertl. Conversion to SMILES from 56 formats is also available using the molecular structure information interchange hub developed by Pat Walters and Matt Stahl.

The inhibition of alkaline phosphatase from bovine intestine mucosa and bovine kidney by calix[4]arenes functionalized at the marcocyclic upper rim by one or two methylene bisphosphonic acid fragments have been investigated. The mechanisms of enzyme inhibition have been discussed using a molecular docking approach by computational modeling of inhibitors into active centers of E. coli alkaline phosphatase.

The Online Chemical Modeling Environment is a unique platform on the Web 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. The database is user-contributed and 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 on data quality and verification. 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. Our intention is to make OCHEM an ultimate platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The OCHEM is free for the web users and it is available online at http://ochem.eu. “Computing chemistry on the web” [1] is becoming a reality.

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 . © 2011 The Author(s).

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 Q 2 =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.

Inhibition of PTP1B by polyhydroxylated fullerenes was studied in silico and in vitro. The enzyme kinetics in the presence of polyhydroxy small gap fullerenes showed that reciprocal value of maximum velocity non-linearly increases with increasing the inhibitor concentration. Analysis of the dose-dependent curve of PTP1B inhibition suggests an apparent positive cooperativity with involvement of at least two binding sites for the hydroxylated fullerene cages. Molecular docking calculations indicated that highly hydroxylated fullerene C60 may occupy the active site and additional allosteric binding site with similar affinity. In silico analysis of a number of fullerenols with 6, 12, 18, 24, 30, and 36 hydroxyl groups showed that the inhibitory activity may depend on the degree of hydroxylation of the nanoparticles surface. These data provide some understanding of the mechanisms of inhibitory action of fullerenols on activity of protein tyrosine phosphatases.