Design of novel quinoline-aminopiperidine derivatives as Mycobacterium tuberculosis (MTB) GyrB inhibitors: an in silico study

Deciphering the mechanism of resistance by novel double mutations in pncA in Mycobacterium tuberculosis using protein structural graphs (PSG) and structural bioinformatic approaches

The evolution of MDR and XDR-TB is a growing concern and public health safety threat around the world. Gene mutations are the prime cause of drug resistance in tuberculosis, however the reports of double mutations further aggravated the situation. Despite the large-scale genomic sequencing and identification of novel mutations, structure investigation of the protein is still required to structurally and functionally characterize these novel mutations to design novel drugs for improved clinical outcome. Hence, we used structural bioinformatics approaches i.e. molecular modeling, residues communication and molecular simulation to understand the impact of novel double S59Y-L85P, D86G-V180F and S104G-V130 M mutation on the structure, function of pncA encoded Pyrazinamidase (PZase) and resistance of Pyrazinamide (PZA). Our results revealed that these mutations alter the binding paradigm and destabilize the protein to release the drug. Protein commination network (PCN) revealed variations in the hub residues and sub-networks which consequently alter the internal communication and signaling. The region 1-75 demonstrated higher flexibility in the mutant structures and minimal by the wild type which destabilize of the internally arranged beta-sheets which consequently reduce the binding of PZA and potentially Fe ion in the mutants. Hydrogen bonding analysis further validated the findings. The total binding free energy (ΔG) for each complex i.e. wild type -7.46 kcal/mol, S59Y-L85P -5.21 kcal/mol, S104G-V130 M -5.33 kcal/mol while for the D86G-V180F mutant the TBE was calculated to be -6.26 kcal/mol. This further confirms that these mutations reduce the binding energy of PZA for PZase and causes resistance in the effective therapy for TB. The trajectories motion was also observed to be affected by these mutations. In conclusion, these mutations use destabilizing approach to reduce the binding of PZA and causes resistance. These features can be used to design novel structure-based drugs against Tuberculosis.

Structure-Based Screening of DNA GyraseB Inhibitors for Therapeutic Applications in Tuberculosis: a Pharmacoinformatics Study

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (MTB) and considered as serious public health concern worldwide which kills approximately five thousand people every day. Therefore, TB drug development efforts are in gigantic need for identification of new potential chemical agents to eradicate TB from the society. The bacterial DNA gyrase B (GyrB) protein as an experimentally widely accepted effective drug target for the development of TB chemotherapeutics. In the present study, advanced pharmacoinformatics approaches were used to screen the Mcule database against the GyrB protein. Based on a number of chemometric parameters, five molecules were found to be crucial to inhibit the GyrB. A number of molecular binding interactions between the proposed inhibitors and important active site residues of GyrB were observed. The predicted drug-likeness properties of all molecules were indicated that compounds possess characteristics to be the drug-like candidates. The dynamic nature of each molecule was explored through the molecular dynamics (MD) simulation study. Various analyzing parameters from MD simulation trajectory have suggested rationality of the molecules to be potential GyrB inhibitor. Moreover, the binding free energy was calculated from the entire MD simulation trajectories highlighted greater binding free energy values for all newly identified compounds also substantiated the strong binding affection towards the GyrB in comparison to the novobiocin. Therefore, the proposed molecules might be considered as potential anti-TB chemical agents for future drug discovery purposes subjected to experimental validation. Graphical Abstract.

Design, Synthesis, and Dual Evaluation of Quinoline and Quinolinium Iodide Salt Derivatives as Potential Anticancer and Antibacterial Agents

A series of novel quinoline and quinolinium iodide derivatives were designed and synthesized to discover potential anticancer and antibacterial agents. With regard to anticancer properties, in vitro cytotoxicities against three human cancer cell lines (A-549, HeLa and SGC-7901) were evaluated. The antibacterial properties against two strains, Escherichia coli (ATCC 29213) and Staphylococcus aureus (ATCC 8739), along with minimum inhibitory concentration (MIC) values were evaluated. The target alkyliodine substituted compounds exhibited significant antitumor and antibacterial activity, of which compound 8-((4-(benzyloxy)phenyl)amino)-7-(ethoxycarbonyl)-5-propyl-[1,3]dioxolo[4,5-g]quinolin-5-ium (12) was found to be the most potent derivative with IC₅₀ values of 4.45±0.88, 4.74±0.42, 14.54±1.96, and 32.12±3.66 against A-549, HeLa, SGC-7901, and L-02 cells, respectively, stronger than the positive controls 5-FU and MTX. Furthermore, compound 12 had the most potent bacterial inhibitory activity. The MIC of this compound against both E. coli and S. aureus was 3.125 nmol ⋅ mL^-1 , which was smaller than that against the reference agents amoxicillin and ciprofloxacin.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.