Quantitative structure-activity relationship studies on cyclic cyanoguanidines acting as HIV-1 protease inhibitors

Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks

Artificial neural networks (ANNs) were used to model both inhibition of HIV-1 protease (K(i)) and inhibition of HIV replication (IC90) for 55 cyclic urea derivatives using constitutional and 2D descriptors. As a preliminary step, linear dependences were established by multiple linear regression (MLR) approaches, selecting the relevant descriptors by genetic algorithm (GA) feature selection. For ANN models non-linear GA feature selection was also applied. Non-linear modeling of K(i) overcame the results of the linear one using four properties, keeping in mind standard Pearson R correlation coefficients (0.931 vs. 0.862) and leave one out (LOO) cross-validation analysis (Q(LOO)2 = 0.703 vs. 0.510). On the other hand, IC90 modeling was insoluble by a linear approach: no predictive model was achieved; however, a non-linear relation was encountered according to statistic results (R = 0.891; Q(LOO)2 = 0.568). The best non-linear models suggested the influence of the presence of nitrogen atoms and the molecular volume distribution in the inhibitor structures on the HIV-1 protease inhibition as well as that the inhibition of HIV replication was dependent on the occurrence of five-member rings. Finally, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map built using the input variables of the best non-linear models.

Comparing the conformational behavior of a series of diastereomeric cyclic urea HIV-1 inhibitors using the low mode:monte carlo conformational search method

The conformational flexibility of a series of diastereomeric cyclic urea HIV-1 protease inhibitors has been examined using the Low Mode:Monte Carlo conformational search method. Force fields were validated by a comparison of the energetic ordering of the minimum energy structures on the AMBER/GBSA(water), OPLSAA/GBSA(water) and HF/6-311G/SCRF(water) surfaces. The energetic ordering of the minima on the OPLSAA /GBSA(water) surface was in better agreement with the quantum calculations than the ordering on the AMBER/GBSA(water) surface. An ensemble of low energy structures was generated using OPLSAA/GBSA(water) and used to compare the molecular shape and flexibility of each diastereomer to the experimentally determined binding affinities and crystal structures of closely related systems. The results indicate that diastereomeric solution-phase energetic stability, conformational rigidity and ability to adopt a chair conformation correlate strongly with experimental binding affinities. Rigid body docking suggests that all of the diastereomers adopt solution-phase conformations suitable for alignment with the HIV-1 protease; however, these results indicate that the binding affinities are dependent upon subtle differences in the P1/P1' and P2/P2' substituent orientations.

A simple clustering technique to improve QSAR model selection and predictivity: application to a receptor independent 4D-QSAR analysis of cyclic urea derived inhibitors of HIV-1 protease

A training set of 50 tetrahydropyrimidine-2-one based inhibitors of HIV-1 protease, for which the -log K(i) values were measured, was used to construct receptor independent 4D-QSAR models. A novel clustering technique was employed to facilitate and improve model selection as well as test set predictions. Following the manifold model theory, five unique models were chosen by the clustering algorithm (q(2) = 0.81-0.84). The models were used to map the atom type morphology of the inhibitor binding site of HIV-1 protease as well as to predict the potencies (-log K(i)) of 10 test set compounds. The rank-difference correlation coefficient was used to evaluate the quality of the test set predictions, which was improved from 0.39 to 0.68 when the clustering technique was applied. The set of five models, collectively, identify the important binding characteristics of the HIV protease receptor site. This study demonstrates that the selected simple clustering technique provides a discrete algorithm for model selection, as well as improving the quality of test set, or unknown, compound prediction as determined by the rank-difference correlation coefficient.

Receptor-independent 4D-QSAR analysis of a set of norstatine derived inhibitors of HIV-1 protease

A training set of 27 norstatine derived inhibitors of HIV-1 protease, based on the 3(S)-amino-2(S)-hydroxyl-4-phenylbutanoic acid core (AHPBA), for which the -log IC(50) values were measured, was used to construct 4D-QSAR models. Five unique RI-4D-QSAR models, from two different alignments, were identified (q(2) = 0.86-0.95). These five models were used to map the atom type morphology of the lining of the inhibitor binding site at the HIV protease receptor site as well as predict the inhibition potencies of seven test set compounds for model validation. The five models, overall, predict the -log IC(50) activity values for the test set compounds in a manner consistent with their q(2) values. The models also correctly identify the hydrophobic nature of the HIV protease receptor site, and inferences are made as to further structural modifications to improve the potency of the AHPBA inhibitors of HIV protease. The finding of five unique, and nearly statistically equivalent, RI-4D-QSAR models for the training set demonstrates that there can be more than one way to fit structure-activity data even within a given QSAR methodology. This set of unique, equally good individual models is referred to as the manifold model.

Comparative study of some energetic and steric parameters of the wild type and mutants HIV-1 protease: a way to explain the viral resistance

Because, in vivo, the HIV-1 PR ( HIV-1 protease) present a high mutation rate we performed a comparative study of the energetic behaviors of the wild type HIV-1 PR and four type of mutants: Val82/Asn; Val82/Asp; Gln7/Lys, Leu33/Ile, Leu63/Ile; Ala71/Thr, Val82/Ala. We suggest that the energetic fluctuation (electrostatic, van der Waals and torsion energy) of the mutants and the solvent accessible surface (SAS) values can be useful to explain the viral resistance process developed by HIV-1 PR. The number and localization of enzyme mutations induce important modifications of the van der Waals and torsional energy, while the electrostatic energy has an insignificant fluctuation. We showed that the viral resistance can be explored if the solvent accessible surfaces of the active site for the mutant structures are calculated. In this paper we have obtained the solvent accessible surface for a group of 15 mutants (11 mutants obtained by Protein Data Bank (PDB) file, 4 mutants modeled by CHARMM software) and for the wild type HIV-1 PR). Our study try to show that the number and localization of the mutations are factors which induce the HIV-1 PR viral resistance. The larger solvent accessible surface could be recorded for the point mutant Val 82/Phe.

Elucidating the inhibiting mode of AHPBA derivatives against HIV-1 protease and building predictive 3D-QSAR models

The Lamarckian genetic algorithm of AutoDock 3.0 has been used to dock 27 3(S)-amino-2(S)-hydroxyl-4-phenylbutanoic acids (AHPBAs) into the active site of HIV-1 protease (HIVPR). The binding mode was demonstrated in the aspects of the inhibitor's conformation, subsite interaction, and hydrogen bonding. The data of geometrical parameters (tau(1), tau(2), and tau(3) listed in Table 2) and root mean square deviation values as compared with the known inhibitor, kni272,(28) show that both kinds of inhibitors interact with HIVPR in a very similar way. The r(2) value of 0.860 indicates that the calculated binding free energies correlate well with the inhibitory activities. The structural and energetic differences in inhibitory potencies of AHPBAs were reasonably explored. Using the binding conformations of AHPBAs, consistent and highly predictive 3D-QSAR models were developed by performing CoMFA, CoMSIA, and HQSAR analyses. The reasonable r(corss)(2) values were 0.613, 0.530, and 0.717 for CoMFA, CoMSIA, and HQSAR models, respectively. The predictive ability of these models was validated by kni272 and a set of nine compounds that were not included in the training set. Mapping these models back to the topology of the active site of HIVPR leads to a better understanding of vital AHPBA-HIVPR interactions. Structural-based investigations and the final 3D-QSAR results provide clear guidelines and accurate activity predictions for novel HIVPR inhibitors.

A quantitative structure-activity relationship study on some HIV-1 protease inhibitors using molecular connectivity index

A quantitative structure-activity relationship (QSAR) study has been made on two different series of tetrahydropyrimidinones acting as HIV-1 protease inhibitors. A structural parameter, the first order valence molecular connectivity index ((1)chi(v)), has been used to account for the variation in the activity. The protease inhibition activity as well as the antiviral potency of the compounds are found to be significantly correlated with (1)chi(v) of P(2)/P(2') substituents attached to the two nitrogens N1 and N3, suggesting that substituents containing less electronegative and more saturated atoms, meaning thereby the less polar or more hydrophobic substituents, will be more advantageous. Further, if P(2) and P(2') are dissimilar, the former is found to be more effective than the latter. This difference is attributed to a conformational change in the enzyme that may be more favorable to P(2) binding than to P(2') binding.