METHODS: Pkdhps were amplified and sequenced from 28 P. knowlesi samples collected in 2008 and 2020 from nine provinces across Thailand. Combining pkdhfr sequencing data from previous work with pkdhps data to analyze polymorphisms of pkdhfr and pkdhps haplotype. Protein modeling and molecular docking were constructed using two inhibitors, sulfadoxine and sulfamethoxazole, and further details were obtained through analyses of protein-ligand interactions by using the Genetic Optimisation for Ligand Docking program. A phylogenetic tree cluster analysis was reconstructed to compare the P. knowlesi Malaysia isolates.
RESULTS: Five nonsynonymous mutations in the pkdhps were detected outside the equivalence of the binding pocket sites to sulfadoxine and sulfamethoxazole, which are at N391S, E421G, I425R, A449S, and N517S. Based on the modeling and molecular docking analyses, the N391S and N517S mutations located close to the enzyme-binding pocket demonstrated a different docking score and protein-ligand interaction in loop 2 of the enzyme. These findings indicated that it was less likely to induce drug resistance. Of the four haplotypes of pkdhfr-pkdhps, the most common one is the R34L pkdhfr mutation and the pkdhps quadruple mutation (GRSS) at E421G, I425R, A449S, and N517S, which were observed in P. knowlesi in southern Thailand (53.57%). Based on the results of neighbor-joining analysis for pkdhfr and pkdhps, the samples isolated from eastern Thailand displayed a close relationship with Cambodia isolates, while southern Thailand isolates showed a long branch separated from the Malaysian isolates.
CONCLUSIONS: A new PCR protocol amplification and evaluation of dihydropteroate synthase mutations in Knowlesi (pkdhps) has been developed. The most prevalent pkdhfr-pkdhps haplotypes (53.57%) in southern Thailand are R34L pkdhfr mutation and pkdhps quadruple mutation. Further investigation requires additional phenotypic data from clinical isolates, transgenic lines expressing mutant alleles, or recombinant proteins.
AIM OF THE STUDY: This study explores the anti-inflammatory effect of TPTQ in silico, in vitro, and in vivo.
MATERIALS AND METHODS: In silico testing used the Gnina application, opened via Google Colab. The TPTQ structure was docked with the nuclear factor kappa B (NF-ĸB) protein (PDB: 2RAM). In vitro testing began with testing the cytotoxicity of TPTQ against Raw 264.7 cells, using the 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) method. A phagocytic activity test was carried out using the neutral red uptake method, and interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) secretion tests were carried out using the enzyme-linked immunosorbent assay (ELISA) method. In vivo, tests were carried out on mice by determining cluster of differentiation 8+ (CD8+), natural killer cell (NK cell), and IL-6 parameters, using the ELISA method.
RESULTS: TPTQ has a lower binding energy than the native ligand and occupies the same active site as the native ligand. TPTQ decreased the phagocytosis index and secretion of IL-6 and TNF-α experimentally in vitro. TPTQ showed significant downregulation of CD8+ and slightly decreased NK cells and IL-6 secretion in vivo.
CONCLUSION: The potent inhibitory effect of TPTQ on the immune response suggests that TPTQ can be developed as an anti-inflammatory agent, especially in the treatment of Covid-19.
METHODS: The study utilized the docked dataset (Induced Fit Docking with Glide XP scoring function) from Loo et al., consisting of 46 ligands-23 agonists and 23 antagonists. The equilibrated structures from Loo et al. were subjected to 30 ns production simulations using GROMACS 2018 at 300 K and 1 atm with the velocity rescaling thermostat and the Parinello-Rahman barostat. AMBER ff99SB*-ILDN was used for the proteins, General Amber Force Field (GAFF) was used for the ligands, and Slipids parameters were used for lipids. MM/PBSA and MM/GBSA binding free energies were then calculated using gmx_MMPBSA. The solute dielectric constant was varied between 1, 2, and 4 to study the effect of different solute dielectric constants on the performance of MM/PB(GB)SA. The effect of entropy on MM/PB(GB)SA binding free energies was evaluated using the interaction entropy module implemented in gmx_MMPBSA. Five GB models, GBHCT, GBOBC1, GBOBC2, GBNeck, and GBNeck2, were evaluated to study the effect of the choice of GB models in the performance of MM/GBSA. Pearson correlation coefficients were used to measure the correlation between experimental and predicted binding free energies.