A TOPS-MODE approach to predict affinity for A1 adenosine receptors. 2-(Arylamino)adenosine analogues

HP-Lattice QSAR for dynein proteins: experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence

The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region M<20,200 and pI<4. We used MASCOT search engine to find proteins in the L. major data base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without relying upon alignment. In order to confirm the previous function annotation we predicted the sequences as dynein with BLAST and the omniBLAST tools (96% alignment similarity to dyneins of other species). Using this combined strategy, we have successfully identified L. infantum protein containing dynein heavy chain, and illustrated the potential use of the QSAR model as a complement to alignment tools.

Three-dimensional holograph vector of atomic interaction field (3D-HoVAIF): a novel rotation-translation invariant 3D structure descriptor and its applications to peptides

Quantitative structure-activity relationship (QSAR) study, important in drug design, mainly involves two aspects, molecular structural characterization (MSC) and construction of a statistical model. MSC focuses on transforming molecular structural and property characteristics into a group of numerical codes, dedicated to minimizing information loss during this process. In this context, common atoms in organic compounds are classified according to their families in the periodic table, and hybridization states, and on the basis of these, three nonbonding interactions (i.e. electrostatic, van der Waals and hydrophobic) are calculated, ultimately resulting in a new rotation-translation invariant, 3D-MSC, as a three-dimensional holograph vector of atomic interaction field (3D-HoVAIF). By applying 3D-HoVAIF to QSAR studies on two classical peptides including 58 angiotensin-converting enzyme (ACE) inhibitors and 48 bitter-tasting dipeptides, we get two excellent genetic algorithm-partial least squares (GA-PLS) models, with statistics r(2), q(2), root mean square error (RMSEE), and root mean square error of cross-validation (RMSCV) of 0.857, 0.811, 0.376, and 0.432 for ACE inhibitors and 0.940, 0.892, 0.153 and 0.205 for bitter-tasting dipeptides, respectively. By equally dividing the two datasets into training and test sets by D-optimal, the 3D-HoVAIF approach undergoes rigorous statistical validation. Furthermore, the superior performance of 3D-HoVAIF is confirmed in comparison with two other peptide MSC approaches referring to z-scale and ISA-ECI. For 58 ACE inhibitors, the GA-PLS model yields two principal components, with the following statistics: r(2) = 0.893, q(2) = 0.824, RMSEE = 0.349, RMSCV = 0.425, q2(ext) = 0.739, r2(ext)= 0.784, r2(0.ext) = 0.781, rf2(0.ext) = 0.77, k = 0.962, k' = 1.019, and RMSEP = 0.460; for 48 bitter-tasting dipeptides, three principal components resulted, with the statistics as: r(2) = 0.950, q(2) = 0.893, RMSEE = 0.152, RMSCV = 0.222, q2(ext)= 0.875, r2(ext) = 0.919, r2(0.ext)= 0.919, rf2(0.ext)= 0.919, k = 1.018, k' = 0.974, and RMSEP = 0.198. In addition, the relationship of ACE-inhibiting activities with bitter-tasting thresholds has been investigated by applying the above-constructed models to predictions on 400 theoretically possible dipeptides. Through analysis, the ACE-inhibiting activities are found to be prominently related to bitter-tasting intensities. Thus, it is deemed to be difficult to find such dipeptides that simultaneously satisfy pharmacodynamic action (high ACE-inhibiting activities) and comfortable tastes, suggesting that active components of dipeptides that are served as functional food to lower blood pressure are not very ideal.

Computational chemistry comparison of stable/nonstable protein mutants classification models based on 3D and topological indices

In principle, there are different protein structural parameters that can be used in computational chemistry studies to classify protein mutants according to thermal stability including: sequence, connectivity, and 3D descriptors. Connectivity parameters (called topological indices, TIs) are simpler than 3D parameters being then less computationally expensive. However, TIs ignore important aspects of protein structure and hence are expected to be inaccurate. In any case, a comparison of 3D and TIs has not been reported with respect to the power of discrimination of proteins according to stability. In this study, we compare both classes of indices in this sense by the first time. The best model found, based on 3D spectral moments correctly classified 507 out of 525 (96.6%) proteins while TIs model correctly classified 404 out of 525 (77.0%) proteins. We have shown that, in fact, 3D descriptor models gave more accurate results than TIs but interestingly, TIs give acceptable results in a timely way in spite of their simplicity.

QSAR Studies Using Radial Distribution Function for Predicting A1 Adenosine Receptors Agonists

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.

In silico studies using Radial Distribution Function approach for predicting affinity of 1 alpha,25-dihydroxyvitamin D(3) analogues for Vitamin D receptor

The Radial Distribution Function (RDF) approach has been applied to the study of the chick intestinal VDR affinity of 49 Vitamin D analogues. A model able to describe more than 77.5% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of four different approaches, including the use of Topological, BCUT, Randić molecular profiles and Geometrical descriptors were able to explain more than 55% of the variance in the mentioned property, with the same number of variables in the equation.

A topological function based on spectral moments for predicting affinity toward A3 adenosine receptors

The spectral moment descriptors have been applied to the study of affinity for A(3) adenosine receptors of 32 adenosine analogues. A model, able to describe more than 95% of the variance in the experimental activity, was developed with the use of the above-mentioned approach. The fragment contributions to the activity carried out show that the sulfonamido moiety at the N(6) position and hydrogen bonding play an important role in the interaction with the receptor.

GETAWAY descriptors to predicting A2A adenosine receptors agonists

The GEometry, Topology and Atom-Weights AssemblY approach has been applied to the study of the A(2A) adenosine receptors agonist effect of 29 adenosine analogues: N(6)-arylcarbamoyl, 2-arylalkynyl-N(6)-arylcarbamoyl, and N(6)-carboxamido derivatives. A model able to describe more than 77% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, no one of four different approaches, including the use of Topological, Galvez Topological Charges indexes, Geometrical and WHIM descriptors were able to explain more than 70% of the variance in the mentioned property with the same number of variables in the equation.

2D RNA-QSAR: assigning ACC oxidase family membership with stochastic molecular descriptors; isolation and prediction of a sequence from Psidium guajava L

Quantitative structure-activity relationship (QSAR) techniques for small molecules could be applied to nucleic acids. Unfortunately, almost all molecular descriptors are more successful at encoding branching information than sequences and/or cannot be back-projected. A solution for scaling the QSAR problem up to RNA may be to transform sequences into secondary structures first. Our group has used Markovian negentropies as molecular descriptors for drug design with preliminary results in bioinformatics [Bioinformatics 2003, 19, 2079]. However, RNA-QSAR studies on RNA molecules have not been described to date. Novel Markovian negentropies have been introduced here as molecular descriptors for 2D-RNA structures. An RNA-QSAR study of the ACC proteins from different plants has been carried out. The QSAR recognizes 19/20 sequences (95.0%) within the ACC family and 12/17 (70.6%) of the control group sequences. The model has a high Matthews' regression coefficient (C = 0.68). Overall cross-validation average accuracies were 14 out of 15 for ACC sequences (93.3%) and 10 out of 13 for control sequences (76.9%). Finally, ACC oxidase family membership was assigned to a new sequence isolated for the first time in this work from Psidium guajava L. A backprojection map for this sequence identifies the left stem (40%) and the main stem (45%) as highly important substructures. Results of an nBLAST experiment are consistent with this finding and indicate a high conservation score (>70) for left stem and main stem; whereas major loop, right stem, cap and major loop right half were hardly conserved.

Quantitative structure-activity relationship studies of vitamin D receptor affinity for analogues of 1alpha,25-dihydroxyvitamin D3. 1: WHIM descriptors

The weighted holistic invariant molecular (WHIM) approach has been applied to the study of the VDR affinity of 86 vitamin D analogues. A model able to describe more than 71% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of three different approaches, including the use of BCUT, Galvez topological charge indices, and 2D autocorrelations descriptors, was able to explain more than 38% of the variance in the mentioned property, even with more variables in the equation.

BCUT descriptors to predicting affinity toward A3 adenosine receptors

The BCUT descriptors have been applied to the study of the A(3) adenosine receptor agonist effect of 32 adenosine analogues. A model, able to describe more than 80% of the variance in the experimental activity was developed with the use of the above-mentioned approach. Four different approaches (topological, Galvez topological charges indexes, Randić molecular profiles, and geometrical descriptors) failed to give satisfactory models for this property with the same number of variables in the equation. Although statistically significant models were derived containing descriptors other than BCUT, the best fitted model was still found with these descriptors.

Geometry, topology, and atom-weights assembly descriptors to predicting A1 adenosine receptors agonists

The GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) approach has been applied to the study of the A1 adenosine receptors agonist effect of 32 adenosine analogues: N6-arylcarbamoyl, 2-arylalkynyl-N6-arylcarbamoyl, and N6-carboxamido derivatives. A model, able to describe more than 77% of the variance in the experimental activity, was developed with the use of the above mentioned approach. Five different approaches (Topological, Galvez Topological Charges indexes, Randic Molecular Profiles, Geometrical, and WHIM descriptors) failed to give satisfactory models (R2=0.70) for this property with the same number of variables in the equation. Although statistically significant models were derived containing descriptors other than GETAWAY, the best fitted out model was still found with these descriptors.

QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices

The unified representation of spectral moments, classic topologic indices, quadratic indices, and stochastic molecular descriptors show that all these molecular descriptors lie within the same family. Consequently, the same prior probability for a successful quantitative-structure-activity-relationship (QSAR) may be expected irrespective of which indices are selected. Herein, we used stochastic spectral moments as molecular descriptors to seek a QSAR using a database of 221 bioactive compounds previously tested against diverse RNA-viruses and 402 nonactive ones. The QSAR model thus obtained correctly classifies 90.9% of compounds in training. The model also correctly classifies a total of 87.9% of 207 compounds on additional external predicting series, 73 of them having anti-RNA-virus activity and 134 nonactive ones. In addition, all compounds were regrouped into five different subsets for leave-group-out studies: (1) anti-influenza, (2) anti-picornavirus, (3) anti-paramyxovirus, (4) anti-RSV/anti-influenza, and (5) broad range anti-RNA-virus activity. The model has retained overall accuracies of about 90% on these studies validating model robustness. Finally, we exemplify the practical use of the model with the discovery of compounds 124 and 128. These compounds presented MIC50 values=3.2 and 8 microg/mL against respiratory syncytial virus (RSV) respectively. Both compounds also have low cytotoxicity expressed by their Minimal Cytotoxic Concentrations >400 microg/mL for HeLa cells. The present approach represents an effort toward a formalization and application of molecular indices in bioorganic and medicinal chemistry.

Unified Markov thermodynamics based on stochastic forms to classify drugs considering molecular structure, partition system, and biological species:

To date, molecular descriptors do not commonly account for important information beyond chemical structure. The present work, attempts to extend, in this sense, the stochastic molecular descriptors, incorporating information about the specific biphasic partition system, the biological species, and chemical structure inside the molecular descriptors. Consequently, MARCH-INSIDE molecular descriptors may be identified with time-dependent thermodynamic parameters (entropy and mean free energy) of partition process. A classification function was developed to classify data of 423 drugs and up to 14 different partition systems at the same time. The model has shown a high overall accuracy of 92.1% (293 out of 318 cases) in training series and 90% (36 out of 40 cases) in predicting ones. Finally, we illustrate the use of the model by predicting a high probability (%) for G1 (a novel antibacterial drug) to undergo partition on different biotic systems (rat organs): liver (97.7), spleen (97.5), lung (97.4), and adipose tissue (97.6). These theoretical results coincide with herein reported steady state plasma concentrations (c) and partition coefficients (P) in liver (c=42.25+/-7.86/P=4.75), spleen (11.47+/-4.43/P=1.29), lung (17.04+/-3.58/P=1.91), and adipose tissue (28.19+/-11.82/P=3.17). All values were relative to (14)C-labeled-radioactive-G1 in plasma (c=8.9+/-3.05) after 3h of oral administration. In closing, the present stochastic forms derive average thermodynamic parameters fitting on a more clearly physicochemical framework with respect to classic vector-matrix-vector forms, which include, as particular cases, quadratic forms such as Wiener index, Randic invariants, Zagreb descriptors, Harary index, Balaban index, and Marrero-Ponce quadratic molecular indices.

Predicting stability of Arc repressor mutants with protein stochastic moments

As more and more protein structures are determined and applied to drug manufacture, there is increasing interest in studying their stability. In this study, the stochastic moments ((SR)pi(k)) of 53 Arc repressor mutants were introduced as molecular descriptors modeling protein stability. The Linear Discriminant Analysis model developed correctly classified 43 out of 53, 81.13% of proteins according to their thermal stability. More specifically, the model classified 20/28 (71.4%) proteins with near wild-type stability and 23/25 (92%) proteins with reduced stability. Moreover, validation of the model was carried out by re-substitution procedures (81.0%). In addition, the stochastic moments based model compared favorably with respect to others based on physicochemical and geometric parameters such as D-Fire potential, surface area, volume, partition coefficient, and molar refractivity, which presented less than 77% of accuracy. This result illustrates the possibilities of the stochastic moments' method for the study of bioorganic and medicinal chemistry relevant proteins.

In silico prediction of central nervous system activity of compounds. Identification of potential pharmacophores by the TOPS–MODE approach

The central nervous system (CNS) activity has been investigated by using a topological substructural molecular approach (TOPS-MODE). A discriminant analysis to classify CNS and non-CNS drugs was developed on a data set (302 compounds) of great structural variability where more than 81% (247/302) were well classified. Randic's orthogonalization procedures was carried out to allow the interpretation of the model and to avoid the collinearity among descriptors. The discriminant model was assessed by a leave-n-out (when n varies from 2 to 20) cross-validation procedure (79.94% of correct classification), an external prediction set composed by 78 CNS/non-CNS drugs (80.77% of correct classification) and a 5-fold full cross-validation (removing 78 compounds in each cycle, 80.00% of good classification). With this methodology was demonstrated that the hydrophobicity increase the CNS activity, while the dipole moment and the polar surface area decrease it; evidencing the capacity of the TOPS-MODE descriptors to estimate CNS activity for new drug candidates. The structural contributions to the CNS activity for two compounds are presented on the basis of fragment contributions. The model has also been able to identify potential structural pharmacophore, showing its possibilities in the lead generation and optimization processes.