In silico Screening of Food and Drug Administration-approved Compounds against Trehalose 2-sulfotransferase (Rv0295c) in Mycobacterium tuberculosis: Insights from Molecular Docking and Dynamics Simulations

BACKGROUND

Tuberculosis (TB) remains a prominent global health challenge, distinguished by substantial occurrences of infection and death. The upsurge of drug-resistant TB strains underscores the urgency to identify novel therapeutic targets and repurpose existing compounds. Rv0295c is a potentially druggable enzyme involved in cell wall biosynthesis and virulence. We evaluated the inhibitory activity of Food and Drug Administration (FDA)-approved compounds against Rv0295c of Mycobacterium tuberculosis, employing molecular docking, ADME evaluation, and dynamics simulations.

METHODS

The study screened 1800 FDA-approved compounds and selected the top five compounds with the highest docking scores. Following this, we subjected the initially screened ligands to ADME analysis based on their dock scores. In addition, the compound exhibited the highest binding affinity chosen for molecular dynamics (MD) simulation to investigate the dynamic behavior of the ligand-receptor complex.

RESULTS

Dihydroergotamine (CHEMBL1732) exhibited the highest binding affinity (-12.8 kcal/mol) for Rv0295c within this set of compounds. We evaluated the stability and binding modes of the complex over extended simulation trajectories.

CONCLUSION

Our in silico analysis demonstrates that FDA-approved drugs can serve as potential Rv0295c inhibitors through repurposing. The combination of molecular docking and MD simulation offers a comprehensive understanding of the interactions between ligands and the protein target, providing valuable guidance for further experimental validation. Identifying Rv0295c inhibitors may contribute to new anti-TB drugs.

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

The Application of CA and PCA to the Evaluation of Lipophilicity and Physicochemical Properties of Tetracyclic Diazaphenothiazine Derivatives

The subject of the study was 11 new synthetized tetracyclic diazaphenothiazine derivatives. Using thin-layer chromatography in a reverse phase system (RP-TLC), their R _M0 lipophilicity parameter was determined. The mobile phase was composed of 0.2 M Tris buffer (pH = 7.4) and acetone (POCH S.A., Gliwice, Poland) in different concentrations. Using computer programs, based on different computational algorithms, theoretical values of lipophilicity (AClogP, ALOGP, ALOGPs, miLogP, MLOGP, XLOGP2, and XLOGP3) as well as molecular descriptors (molecular weight, volume of a molecule, dipole moment, polar surface, and energy of HOMO orbitals and LUMO orbitals) and parameters of biological activity: human intestinal absorption (HIA), plasma protein binding (PPB), and blood-brain barrier (BBB), were determined. The correlations between the experimental values of lipophilicity and theoretically calculated lipophilic values and also between experimental values of lipophilicity and values of physicochemical or biological properties were assessed. A certain relationship between structure and lipophilicity was found. On the other hand, the relationships between R _M0 and physicochemical or biological properties were not statistically significant and therefore unusable. For all analysed values, an analysis of similarities and principal component analyses were also made. The obtained dendrograms for the analysis of lipophilicity and physicochemical and biological properties indicate the relationship between experimental values of lipophilicity and structure in the case of theoretical lipophilicity values only. PCA, on the other hand, showed that ALOGP, MLOGP, miLogP, and BBB and molar volume have the largest share in the description of the entire system. Distribution of compounds on the area of factors also indicates the connections between them related to their structure.

QSAR Development for Plasma Protein Binding: Influence of the Ionization State

Purpose

This study explored several strategies to improve the performance of literature QSAR models for plasma protein binding (PPB), such as a suitable endpoint transformation, a correct representation of chemicals, more consistency in the dataset, and a reliable definition of the applicability domain.

Methods

We retrieved human fraction unbound (Fu) data for 670 compounds from the literature and carefully checked them for consistency. Descriptors were calculated taking account of the ionization state of molecules at physiological pH (7.4), in order to better estimate the affinity of molecules to blood proteins. We used different algorithms and chemical descriptors to explore the most suitable strategy for modeling the endpoint. SMILES (simplified molecular input line entry system)-based string descriptors were also tested with the CORAL software (CORelation And Logic). We did an outlier analysis to establish the models to use (or not to use) in case of well recognized families.

Results

Internal validation of the selected models returned Q² values close to 0.60. External validation also gave r² values always greater than 0.60. The CORAL descriptor based model for √fu was the best, with r² 0.74 in external validation.

Conclusions

Performance in prediction confirmed the robustness of all the derived models and their suitability for real-life purposes, i.e. screening chemicals for their ADMET profiling. Optimization of descriptors can be useful in order to obtain the correct results with a ionized molecule.

Electronic supplementary material

The online version of this article (10.1007/s11095-018-2561-8) contains supplementary material, which is available to authorized users.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Molecular Features Underlying Selectivity in Chicken Bitter Taste Receptors

Chickens sense the bitter taste of structurally different molecules with merely three bitter taste receptors (Gallus gallus taste 2 receptors, ggTas2rs), representing a minimal case of bitter perception. Some bitter compounds like quinine, diphenidol and chlorpheniramine, activate all three ggTas2rs, while others selectively activate one or two of the receptors. We focus on bitter compounds with different selectivity profiles toward the three receptors, to shed light on the molecular recognition complexity in bitter taste. Using homology modeling and induced-fit docking simulations, we investigated the binding modes of ggTas2r agonists. Interestingly, promiscuous compounds are predicted to establish polar interactions with position 6.51 and hydrophobic interactions with positions 3.32 and 5.42 in all ggTas2rs; whereas certain residues are responsible for receptor selectivity. Lys^3.29 and Asn^3.36 are suggested as ggTas2r1-specificity-conferring residues; Gln^6.55 as ggTas2r2-specificity-conferring residue; Ser^5.38 and Gln^7.42 as ggTas2r7-specificity conferring residues. The selectivity profile of quinine analogs, quinidine, epiquinidine and ethylhydrocupreine, was then characterized by combining calcium-imaging experiments and in silico approaches. ggTas2r models were used to virtually screen BitterDB compounds. ~50% of compounds known to be bitter to human are likely to be bitter to chicken, with 25, 20, 37% predicted to be ggTas2r1, ggTas2r2, ggTas2r7 agonists, respectively. Predicted ggTas2rs agonists can be tested with in vitro and in vivo experiments, contributing to our understanding of bitter taste in chicken and, consequently, to the improvement of chicken feed.