A structure-based QSAR and docking study on imidazo[1,5-a][1,2,4]-triazolo[1,5-d][1,4,]benzodiazepines as Selective GABA(A) α5 inverse agonists

Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands

A novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.

Dependence of the kinetic and thermodynamic parameters on hydrophilic-lipophilic character of alprazolam, clonazepam, diazepam, doxepin and haloperidol in alkaline environment

Examination of the stability of clonazepam, diazepam, alprazolam, haloperidol, and doxepin in basic solutions was performed, together with an assessment of the kinetic (k, t0.1i t0.5) and thermodynamic (Ea, ΔH(++)i ΔS(++)) stability-indicating parameters, which were compared with the lipophilicity (logP) of the studied drugs. It was observed that the calculated values of Ea, ΔH(++) and ΔS(++) for the studied drugs increased from 41.04 kJ/mol to 125.50 kJ/mol, from 37.82 kJ/mol to 122.24 kJ/mol and from -167.09 J/Kmol to 53.02 J/Kmol, respectively, along with an increase of lipophilicity (logP) from 2.12 to 4.30 for the most hydrophilic alprazolam to the most lipophilic haloperidol. The degradation products were identified using UPLC/MS/MS method.

Quantitative analysis of the effect of triglyceride alkyl-chain length on the partitioning of highly lipophilic compounds to the mesenteric lymph in intestinal cells

The purpose of this study was to quantitatively clarify the effect of alky-chain length of a triglyceride in an emulsion on the partitioning of highly lipophilic compounds into the lymph fluid after their oral administration. Highly lipophilic anthraquinone derivatives were orally administered in emulsions to rats. Emulsions composed of long-, medium-, and short-chain triglycerides (LCT, MCT, and SCT emulsions, respectively) were used. The concentrations of the compounds in plasma and lymph fluid were periodically determined and their partitioning to the lymph was calculated using a mathematical model. Intestinal absorption of all compounds was enhanced and the plasma concentrations of the compounds were found to be in the following order: LCT emulsion > MCT emulsion > SCT emulsion. The amounts of each compound recovered in the lymph were not in agreement with their lipophilicity. Quantitative analysis revealed that the partitioning of the compounds to the lymph may be determined by the solubility of the compound in the triglyceride in the form of an emulsion and the amount of triglyceride transferred to the lymph fluid. These results suggest a possibility that the amount of a compound absorbed via the lymph route after oral administration can be quantitatively controlled by the formulations.

Molecular dynamics simulation study and molecular docking descriptors in structure-based QSAR on acetylcholinesterase (AChE) inhibitors

In this study we present an approach for predicting the inhibitory activity of acetylcholinesterase (AChE) inhibitors by combining molecular dynamics (MD) simulation and docking studies in a structure-based quantitative structure-activity relationship (QSAR) model. The MD simulation was performed on AChE to obtain enzyme conformation in a water environment. The resulting conformation of the enzyme was used for docking with the most potent inhibitor (26a). Docking analysis revealed that hydrophobic interactions play important roles in the AChE-inhibitor complex. Then, all inhibitors that could bind simultaneously at the catalytic site and at the peripheral anionic site of AChE were docked into the enzyme and their interactions with AChE were used as new interpretable descriptors in a structure-based QSAR model. The least squares support vector regression was constructed using the four most relevant docking descriptors and one molecular structure descriptor. The Q(2) value of the model was found to be 0.790. Furthermore, to study the enzyme conformation stability, a second MD simulation was performed on AChE-inhibitor 26a complex. In MD simulation, the topological parameters of the inhibitor were derived from the PRODRG server, and partial atomic charges were modified using the B3LYP/6-31G level of theory. The radius of gyration for the complex showed that AChE conformation did not change in the presence of the inhibitors.

Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of SVM models

A new sensitivity analysis (SA) method for variable selection in support vector machine (SVM) was proposed to improve the performance level of the QSAR model to predict carcinogenicity based on the correlation coefficient (CC) method used in our preceding study. The performances of both methods were also compared with that of the F-score (FS) method proposed by Chang and Lin. The 911 non-congeneric chemicals were classified into 20 mutually overlapping groups according to contained substructures, and a specific SVM model created on chemicals belonging to each group was optimized by searching the best set of SVM parameters while successively omitting descriptors of lower absolute values of sensitivity, CC or FS until the maximum predictive performance was obtained. The SA method improves the overall accuracy from 80% of CC and FS to 84%, which is considerably higher than those of existing models for predicting the carcinogenicity of non-congeneric chemicals. It selects the optimum sets of effective descriptors fewer than the CC and FS methods, and is not time-consuming and can be applied to a large set of initial descriptors. It is concluded that SA is superior as a variable selection method in SVM models.

A unified model of the GABA(A) receptor comprising agonist and benzodiazepine binding sites

We present a full-length α(1)β(2)γ(2) GABA receptor model optimized for agonists and benzodiazepine (BZD) allosteric modulators. We propose binding hypotheses for the agonists GABA, muscimol and THIP and for the allosteric modulator diazepam (DZP). The receptor model is primarily based on the glutamate-gated chloride channel (GluCl) from C. elegans and includes additional structural information from the prokaryotic ligand-gated ion channel ELIC in a few regions. Available mutational data of the binding sites are well explained by the model and the proposed ligand binding poses. We suggest a GABA binding mode similar to the binding mode of glutamate in the GluCl X-ray structure. Key interactions are predicted with residues α(1)R66, β(2)T202, α(1)T129, β(2)E155, β(2)Y205 and the backbone of β(2)S156. Muscimol is predicted to bind similarly, however, with minor differences rationalized with quantum mechanical energy calculations. Muscimol key interactions are predicted to be α(1)R66, β(2)T202, α(1)T129, β(2)E155, β(2)Y205 and β(2)F200. Furthermore, we argue that a water molecule could mediate further interactions between muscimol and the backbone of β(2)S156 and β(2)Y157. DZP is predicted to bind with interactions comparable to those of the agonists in the orthosteric site. The carbonyl group of DZP is predicted to interact with two threonines α(1)T206 and γ(2)T142, similar to the acidic moiety of GABA. The chlorine atom of DZP is placed near the important α(1)H101 and the N-methyl group near α(1)Y159, α(1)T206, and α(1)Y209. We present a binding mode of DZP in which the pending phenyl moiety of DZP is buried in the binding pocket and thus shielded from solvent exposure. Our full length GABA(A) receptor is made available as Model S1.