Topological sub-structural molecular design (TOPS-MODE): a useful tool to explore key fragments of human A3 adenosine receptor ligands.

Adenosine regulates tissue function by activating four G-protein-coupled adenosine receptors (ARs). Selective agonists and antagonists for A3 ARs have been investigated for the treatment of a variety of immune disorders, cancer, brain, and heart ischemic conditions. We herein present a QSAR study based on a Topological sub-structural molecular design (TOPS-MODE) approach, intended to predict the A3 ARs of a diverse dataset of 124 (94 training set/ 30 prediction set) adenosine derivatives. The final model showed good fit and predictive capability, displaying 85.1 % of the experimental variance. The TOPS-MODE approach afforded a better understanding and interpretation of the developed model based on the useful information extracted from the analysis of the contribution of different molecular fragments to the affinity.

Thermodynamic computational approach to capture molecular recognition in the binding of different inhibitors to the DNA gyrase B subunit from Escherichia coli.

DNA gyrase subunit B, that catalyzes the hydrolysis of ATP, is an attractive target for the development of antibacterial drugs. This work is intended to rationalize molecular recognition at DNA gyrase B enzyme - inhibitor binding interface through the evaluation of different scoring functions in finding the correct pose and scoring properly 50 Escherichia coli DNA Gyrase B inhibitors belonging to five different classes. Improving the binding free energy calculation accuracy is further attempted by using rescoring schemes after short molecular dynamic simulations of the obtained docked complexes. These data are then compared with the corresponding experimental enzyme activity data. The results are analyzed from a structural point of view emphasizing the strengths and limitations of the techniques applied in the study.

Combining molecular docking and QSAR studies for modelling the antigyrase activity of cyclothialidine derivatives.

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.

Exploring the conformational changes of the ATP binding site of gyrase B from Escherichia coli complexed with different established inhibitors by using molecular dynamics simulation: protein-ligand interactions in the light of the alanine scanning and free energy decomposition methods.

Currently, bacterial diseases cause a death toll around 2 million people a year encouraging the search for new antimicrobial agents. DNA gyrase is a well-established antibacterial target consisting of two subunits, GyrA and GyrB, in a heterodimer A(2)B(2). GyrA is involved in DNA breakage and reunion and GyrB catalyzes the hydrolysis of ATP. The GyrB subunit from Escherichia coli has been investigated, namely the ATP binding pocket both considering the protein without ligands and bound with the inhibitors clorobiocin, novobiocin and 5'-adenylyl-ß-γ-imidodiphosphate. The stability of the systems was studied by molecular dynamics simulation with the further analysis of the time dependent root-mean-square coordinate deviation (RMSD) from the initial structure, and temperature factors. Moreover, exploration of the conformational space of the systems during the MD simulation was carried out by a clustering data mining technique using the average-linkage algorithm. Recognizing the key residues in the binding site of the enzyme that are involved in the binding mode with the aforementioned inhibitors was investigated by using two techniques: free energy decomposition and computational alanine scanning. The results from these simulations highlight the important residues in the ATP binding site and can be useful in the design process of potential new inhibitors.

Global antifungal profile optimization of chlorophenyl derivatives against Botrytis cinerea and Colletotrichum gloeosporioides.

Twenty-two aromatic derivatives bearing a chlorine atom and a different chain in the para or meta position were prepared and evaluated for their in vitro antifungal activity against the phytopathogenic fungi Botrytis cinerea and Colletotrichum gloeosporioides. The results showed that maximum inhibition of the growth of these fungi was exhibited for enantiomers S and R of 1-(4'-chlorophenyl)-2-phenylethanol (3 and 4). Furthermore, their antifungal activity showed a clear structure-activity relationship (SAR) trend confirming the importance of the benzyl hydroxyl group in the inhibitory mechanism of the compounds studied. Additionally, a multiobjective optimization study of the global antifungal profile of chlorophenyl derivatives was conducted in order to establish a rational strategy for the filtering of new fungicide candidates from combinatorial libraries. The MOOP-DESIRE methodology was used for this purpose providing reliable ranking models that can be used later.

Synthesis and quantitative structure-antifungal activity relationships of clovane derivatives against Botrytis cinerea.

Twenty-three clovane derivatives, nine described here for the first time, bearing substituents on carbon C-2, have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The results showed that compounds 9, 14, 16, and 18 bearing nitrogen atoms in the chain attached at C-2 displayed potent antifungal activity, whereas mercapto derivatives 13, 19, and 22 displayed low activity. The antifungal activity showed a clear structure-activity relationship (SAR) trend, which confirmed the importance of the nature of the C-2 chain on the antifungal activity. On the basis of these observations, the metabolism of compounds 8 and 14 by the fungus B. cinerea, and the metabolism of other clovanes by this fungus, described previously, a pro-drug action mechanism for 2-alkoxyclovane compounds is proposed. Quantitative structure-activity relationship (QSAR) studies were performed to rationalize the results and to suggest further optimization, using a topological sub-structural molecular design (TOPS-MODE) approach. The model displayed good fit and predictive capability, describing 85.5% of the experimental variance, with a standard deviation of 9.502 and yielding high values of cross-validation determination coefficients (q2CV-LOO = 0.784 and q2boot = 0.673). The most significant variables were the spectral moments weighted by bond dipole moment (Dip), hydrophobicity (Hyd), and the combined dipolarity/polarizability Abraham molecular descriptor (Ab-pi2H).

A TOPological Sub-structural Molecular Design (TOPS-MODE)-QSAR approach for modeling the antiproliferative activity against murine leukemia tumor cell line (L1210).

Lately, Quantitative Structure-Activity Relationship (QSAR) studies have been afar used to predict anticancer activity taking into account different molecular descriptors, statistical techniques, cell lines and data set of congeneric and non-congeneric compounds. Herein we report a QSAR study based on a TOPological Sub-structural Molecular Design (TOPS-MODE) approach, aiming at predicting the anticancer leukemia activity of a diverse data set of indolocarbazoles derivatives. Finally, several aspects of the structural activity relationships are discussed in terms of the contribution of different bonds to the anticancer activity, thereby making the relationship between structure and biological activity more transparent.

Variable selection methods in QSAR: an overview.

Variable selection is a procedure used to select the most important features to obtain as much information as possible from a reduced amount of features. The selection stage is crucial. The subsequent design of a quantitative structure-activity relationship (QSAR) model (regression or discriminant) would lead to poor performance if little significant features are selected. In drug design modern era, by the means of combinatorial chemistry and high throughput screening, an unprecedented amount of experimental information has been generated. In addition, many molecular descriptors have been defined in the last two decays. All this information can be analyzed by QSAR techniques using adequate statistical procedures. These techniques and procedures should be fast, automated, and applicable to large data sets of structurally diverse compounds. For that reason, the identification of the best one seems to be a very difficult task in view of the large variable selection techniques existing nowadays. The intention of this review is to summarize some of the present knowledge concerning to variable selection methods applied to some well-known statistical techniques such as linear regression, PLS, kNN, Artificial Neural Networks, etc, with the aim to disseminate the advances of this important stage of the QSAR building model.

Quantitative structure-antifungal activity relationships of some benzohydrazides against Botrytis cinerea.

Fourteen benzohydrazides have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The best antifungal activity was observed for the N',N'-dibenzylbenzohydrazides 3b-d and for the N-aminoisoindoline-derived benzohydrazide 5. A quantitative structure-activity relationship (QSAR) study has been developed using a topological substructural molecular design (TOPS-MODE) approach to interpret the antifungal activity of these synthetic compounds. The model described 98.3% of the experimental variance, with a standard deviation of 4.02. The influence of an ortho substituent on the conformation of the benzohydrazides was investigated by X-ray crystallography and supported by QSAR study. Several aspects of the structure-activity relationships are discussed in terms of the contribution of different bonds to the antifungal activity, thereby making the relationships between structure and biological activity more transparent.

2D-autocorrelation descriptors for predicting cytotoxicity of naphthoquinone ester derivatives against oral human epidermoid carcinoma.

A QSAR study was developed, employing 2D-autocorrelation descriptors and a set of 37 naphthoquinone ester derivatives, in order to model the cytotoxicity of these compounds against oral human epidermoid carcinoma (KB). A comparison with other approaches such as the BCUT, Galvez topological charge indexes, Randic molecular profile, Geometrical, and RDF descriptors was carried out. Mathematical models were obtained by means of the multiple regression analysis (MRA) and the variables were selected using genetic algorithm. Based on the statistical results the 2D-autocorrelation descriptors were considered the best and were able to describe more than 84.2% of the variance in the experimental activity once we controlled for outliers.

Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments.

Three-dimensional (3D) protein structures now frequently lack functional annotations because of the increase in the rate at which chemical structures are solved with respect to experimental knowledge of biological activity. As a result, predicting structure-function relationships for proteins is an active research field in computational chemistry and has implications in medicinal chemistry, biochemistry and proteomics. In previous studies stochastic spectral moments were used to predict protein stability or function (González-Díaz, H. et al. Bioorg Med Chem 2005, 13, 323; Biopolymers 2005, 77, 296). Nevertheless, these moments take into consideration only electrostatic interactions and ignore other important factors such as van der Waals interactions. The present study introduces a new class of 3D structure molecular descriptors for folded proteins named the stochastic van der Waals spectral moments ((o)beta(k)). Among many possible applications, recognition of kinases was selected due to the fact that previous computational chemistry studies in this area have not been reported, despite the widespread distribution of kinases. The best linear model found was Kact = -9.44 degrees beta(0)(c) +10.94 degrees beta(5)(c) -2.40 degrees beta(0)(i) + 2.45 degrees beta(5)(m) + 0.73, where core (c), inner (i) and middle (m) refer to specific spatial protein regions. The model with a high Matthew's regression coefficient (0.79) correctly classified 206 out of 230 proteins (89.6%) including both training and predicting series. An area under the ROC curve of 0.94 differentiates our model from a random classifier. A subsequent principal components analysis of 152 heterogeneous proteins demonstrated that beta(k) codifies information different to other descriptors used in protein computational chemistry studies. Finally, the model recognizes 110 out of 125 kinases (88.0%) in a virtual screening experiment and this can be considered as an additional validation study (these proteins were not used in training or predicting series).

A model for the recognition of protein kinases based on the entropy of 3D van der Waals interactions.

The study and prediction of kinase function (kinomics) is of major importance for proteome research due to the widespread distribution of kinases. However, the prediction of protein function based on the similarity between a functionally annotated 3D template and a query structure may fail, for instance, if a similar protein structure cannot be identified. Alternatively, function can be assigned using 3D-structural empirical parameters. In previous studies, we introduced parameters based on electrostatic entropy (Proteins 2004, 56, 715) and molecular vibration entropy (Bioinformatics 2003, 19, 2079) but ignored other important factors such as van der Waals (vdw) interactions. In the work described here, we define 3D-vdw entropies (degrees theta(k)) and use them for the first time to derive a classifier for protein kinases. The model classifies correctly 88.0% of proteins in training and more than 85.0% of proteins in validation studies. Principal components analysis of heterogeneous proteins demonstrated that degrees theta(k) codify information that is different to that described by other bulk or folding parameters. In additional validation experiments, the model recognized 129 out of 142 kinases (90.8%) and 592 out of 677 non-kinases (87.4%) not used above. This study provides a basis for further consideration of degrees theta(k) as parameters for the empirical search for structure-function relationships.

Quantitative structure-activity relationship studies for the prediction of antifungal activity of N-arylbenzenesulfonamides against Botrytis cinerea.

The Botrytis cinerea is one of the most interesting fungal pathogens. It can infect almost every plant and plant part and cause early latent infections which damage the fruit before ripening. The QSAR is an alternative method for the research of new and better fungicides against B. cinerea. This paper describes the results of applying a topological sub-structural molecular design (TOPS-MODE) approach for predicting the antifungal activity of 28 N-arylbenzenesulfonamides. The model described 86.1% of the experimental variance, with a standard deviation of 0.223. Leave-one-out and leave-group-out cross validation was carried out with the aim of evaluating the predictive power of the model. The values of their respective squared correlations coefficients were 0.754 and 0.741. The TOPS-MODE approach was compared with three other predictive models, but none of these could explain more than 72.8% of the variance with the same number of variables. In addition, this approach enabled the assessment of the contribution of different bonds to antifungal activity, thereby making the relationships between structure and biological activity more transparent. It was found that the fungicidal activity of the chemicals analyzed was increased by the presence of a sulfonamide group bonded to two aromatics rings, making this group the most important of the molecule. The majority of the substituents present in the aromatic rings have an electron withdrawing effect and they contribute to a smaller degree than the sulfonamide group to the property under study. The aromatic moiety plays an important role in this activity; its contribution changes with different substituents. Generally, the nitro group has a positive and great contribution to the biological property but when this group is involved in some compounds in ortho effect with the SO2 moiety of the sulfonamide group a lower value of contribution is observed for both groups.

Quantitative structure-activity relationship studies of HIV-1 integrase inhibition. 1. GETAWAY descriptors.

The GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) approach has been applied to the study of the HIV-1 integrase inhibition of 172 compounds that belong to 11 different chemistry families. A model able to describe more than 68.5% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the five different approaches, including the use of Randic Molecular Profiles, Geometrical, RDF, 3D-MORSE and WHIM descriptors was able to explain more than 62.4% of the variance in the mentioned property with the same number of variables in the equation. Finally, after extracting five compounds considered by us as outliers the model was able to describe more than 72.5% of the variance in the experimental activity.

QSAR studies about cytotoxicity of benzophenazines with dual inhibition toward both topoisomerases I and II: 3D-MoRSE descriptors and statistical considerations about variable selection.

Deoxyribonucleic acid (DNA) topoisomerases are involved in diverse cellular processes, such as replication, transcription, recombination, and chromosome segregation. Searching new compounds that inhibit both topoisomerases I and II is very important due to the deficiency of the specific inhibitors to overcome multidrug resistance (MDR). A QSAR study was developed, employing the 3D-MoRSE descriptors and a set of 64 benzophenazines in order to model the inhibition of the topoisomerases I and II, expressed by the cytotoxicity of these compounds (IC(50)) versus drug-resistant human small cell lung carcinoma line cell H69/LX4. A comparison with other approaches such as the Topological, BCUT, Galvez topological charge indexes, 2D autocorrelations, Randic molecular profile, Geometrical, RDF, and WHIM descriptors was carried out. The mathematical models were obtained by means of the multiple regression analysis (MRA) and the variables were selected using the genetic algorithm. The model relative to the 3D-MoRSE descriptors was considered as the best, taking into account its statistical parameters. It was able to describe more than 82.2% of the variance in the experimental activity once the outliers were extracted.

Proteins Markovian 3D-QSAR with spherically-truncated average electrostatic potentials.

Proteins 3D-QSAR is an emerging field of bioorganic chemistry. However, the large dimensions of the structures to be handled may become a bottleneck to scaling up classic QSAR problems for proteins. In this sense, truncation approach could be used as in molecular dynamic to perform timely calculations. The spherical truncation of electrostatic field with different functions breaks down long-range interactions at a given cutoff distance (r(off)) resulting in short-range ones. Consequently, a Markov chain model may approach to the average electrostatic potentials of spatial distribution of charges within the protein backbone. These average electrostatic potentials can be used to predict proteins properties. Herein, we explore the effect of abrupt, shifting, force shifting, and switching truncation functions on 3D-QSAR models classifying 26 proteins with different functions: lysozymes, dihydrofolate reductases, and alcohol dehydrogenases. Almost all methods have shown overall accuracies higher than 73%. The present result points to an acceptable robustness of the MC for different truncation schemes and r(off) values. The results of best accuracy 92% with abrupt truncation coincide with our recent communication. We also developed models with the same accuracy value for other truncation functions; however they are more complex functions. PCA analysis for 152 non-homologous proteins has shown that there are five main eigenvalues, which explain more than 87% of the variance of the studied properties. The present molecular descriptors may encode structural information not totally accounted for the previous ones, so success with these descriptors could be expected when classic fails. The present result confirms the utility of our Markov models combined with truncation approach to generate bioorganic structure protein molecular descriptors for QSAR.