Odd Loop Regions of XenA and XenB Enzymes Modulate Their Interaction with Nitro-explosives Compounds and Provide Structural Support for Their Regioselectivity

Structural Factors That Determine the Activity of the Xenobiotic Reductase B Enzyme from Pseudomonas putida on Nitroaromatic Compounds

Xenobiotic reductase B (XenB) catalyzes the reduction of the aromatic ring or nitro groups of nitroaromatic compounds with methyl, amino or hydroxyl radicals. This reaction is of biotechnological interest for bioremediation, the reuse of industrial waste or the activation of prodrugs. However, the structural factors that explain the binding of XenB to different substrates are unknown. Molecular dynamics simulations and quantum mechanical calculations were performed to identify the residues involved in the formation and stabilization of the enzyme/substrate complex and to explain the use of different substrates by this enzyme. Our results show that Tyr65 and Tyr335 residues stabilize the ligands through hydrophobic interactions mediated by the aromatic rings of these aminoacids. The higher XenB activity determined with the substrates 1,3,5-trinitrobenzene and 2,4,6-trinitrotoluene is consistent with the lower energy of the highest occupied molecular orbital (LUMO) orbitals and a lower energy of the homo orbital (LUMO), which favors electrophile and nucleophilic activity, respectively. The electrostatic potential maps of these compounds suggest that the bonding requires a large hydrophobic region in the aromatic ring, which is promoted by substituents in ortho and para positions. These results are consistent with experimental data and could be used to propose point mutations that allow this enzyme to process new molecules of biotechnological interest.

Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach

The rapid emergence and spread of new variants of coronavirus type 2, as well as the emergence of zoonotic viruses, highlights the need for methodologies that contribute to the search for new pharmacological treatments. In the present work, we searched for new SARS-CoV-2 papain-like protease inhibitors in the PubChem database, which has more than 100 million compounds. Based on the ligand efficacy index obtained by molecular docking, 500 compounds with higher affinity than another experimentally tested inhibitor were selected. Finally, the seven compounds with ADME parameters within the acceptable range for such a drug were selected. Next, molecular dynamics simulation studies at 200 ns, ΔG calculations using molecular mechanics with generalized Born and surface solvation, and quantum mechanical calculations were performed with the selected compounds. Using this in silico protocol, seven papain-like protease inhibitors are proposed: three compounds with similar free energy (D28, D04, and D59) and three compounds with higher binding free energy (D60, D99, and D06) than the experimentally tested inhibitor, plus one compound (D24) that could bind to the ubiquitin-binding region and reduce the effect on the host immune system. The proposed compounds could be used in in vitro assays, and the described protocol could be used for smart drug design.