Interactions of Co, Cu, and non-metal phthalocyanines with external structures of SARS-CoV-2 using docking and molecular dynamics.

The new coronavirus, SARS-CoV-2, caused the COVID-19 pandemic, characterized by its high rate of contamination, propagation capacity, and lethality rate. In this work, we approach the use of phthalocyanines as an inhibitor of SARS-CoV-2, as they present several interactive properties of the phthalocyanines (Pc) of Cobalt (CoPc), Copper (CuPc) and without a metal group (NoPc) can interact with SARS-CoV-2, showing potential be used as filtering by adsorption on paints on walls, masks, clothes, and air conditioning filters. Molecular modeling techniques through Molecular Docking and Molecular Dynamics were used, where the target was the external structures of the virus, but specifically the envelope protein, main protease, and Spike glycoprotein proteases. Using the g_MM-GBSA module and with it, the molecular docking studies show that the ligands have interaction characteristics capable of adsorbing the structures. Molecular dynamics provided information on the root-mean-square deviation of the atomic positions provided values between 1 and 2.5. The generalized Born implicit solvation model, Gibbs free energy, and solvent accessible surface area approach were used. Among the results obtained through molecular dynamics, it was noticed that interactions occur since Pc could bind to residues of the active site of macromolecules, demonstrating good interactions; in particular with CoPc. Molecular couplings and free energy showed that S-gly active site residues interacted strongly with phthalocyanines with values ​​of - 182.443 kJ/mol (CoPc), 158.954 kJ/mol (CuPc), and - 129.963 kJ/mol (NoPc). The interactions of Pc's with SARS-CoV-2 may predict some promising candidates for antagonists to the virus, which if confirmed through experimental approaches, may contribute to resolving the global crisis of the COVID-19 pandemic.

Effect of drug metabolism in the treatment of SARS-CoV-2 from an entirely computational perspective.

Understanding the effects of metabolism on the rational design of novel and more effective drugs is still a considerable challenge. To the best of our knowledge, there are no entirely computational strategies that make it possible to predict these effects. From this perspective, the development of such methodologies could contribute to significantly reduce the side effects of medicines, leading to the emergence of more effective and safer drugs. Thereby, in this study, our strategy is based on simulating the electron ionization mass spectrometry (EI-MS) fragmentation of the drug molecules and combined with molecular docking and ADMET models in two different situations. In the first model, the drug is docked without considering the possible metabolic effects. In the second model, each of the intermediates from the EI-MS results is docked, and metabolism occurs before the drug accesses the biological target. As a proof of concept, in this work, we investigate the main antiviral drugs used in clinical research to treat COVID-19. As a result, our strategy made it possible to assess the biological activity and toxicity of all potential by-products. We believed that our findings provide new chemical insights that can benefit the rational development of novel drugs in the future.

Iron and molybdenum mixed oxide supported on Al-PILC for the catalytic oxidative desulfurization of dibenzothiophene in simulated diesel fuel.

In this work, three novel catalysts were prepared by 2.5, 5.0, and 10.0 wt.% facile impregnation with an iron and molybdenum mixed oxide (Fe/Mo) on an aluminum pillared clay (Al-PILC) support. These materials were characterized by scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS), X-ray diffraction (XRD), temperature programed reduction (TPR), and nitrogen (N2) physisorption at 77 K. Characterizations indicated that the metal particles were dispersed on the surface of the three catalysts, and the interlayer d001 spacing of the pillared material remained unchanged after the impregnation process. The catalytic tests showed good results for DBT oxidation using the synthesized catalysts, with high turnover frequency (TOF) values, particularly for the material with 5.0 wt.% Fe/Mo. Theoretical calculations were carried out at the density functional theory (DFT) level, to investigate how the DBT molecules were adsorbed onto the surface of the mixed oxide. The lowest energy proposal was obtained when both Fe and Mo were present at the active sites, indicating a possible synergistic effect of the metals on catalyst activity. Reuse tests indicated that the catalysts could be employed effectively for up to 3 cycles in a row, then a decrease in activity occurred and the active sites needed to be regenerated.

Chitosan-iron oxide hybrid composite: mechanism of hexavalent chromium removal by central composite design and theoretical calculations.

In this study, the synthesis of iron oxide stabilized by chitosan was carried out for the application and optimization in the removal process of aqueous Cr(VI) by central composite design (CCD). The calculation of these effects allowed to know, quantitatively, the variables and the interaction between them that could affect the Cr(VI) removal process. It was also verified that the most favorable conditions for chromium removal were the following: pH 5.0, Cr(VI) concentration of 130 mg L-1, adsorbent mass of 5 mg, and Fe(II) content of 45% (w/w) in the CT-Fe beads. The adsorption kinetics performed under these conditions showed that the chitosan/iron hybrid composite is an adsorbent material with high chromium removal capacity (46.12 mg g-1). It was found that all variables were statistically significant. However, it was observed that the variable that most affected Cr(VI) removal was the pH of the solution, followed by the concentration of chromium ions in solution and the interaction between them. Therefore, the studied experimental conditions are efficient in chromium adsorption, besides the operational simplicity coming from statistical design. Theoretical calculations showed that the most stable chitosan was that with Fe(II) in the structure, that is, in the reaction mechanism, there is no competition of Fe(II) with Cr(III, VI) in the available sites of chitosan. Thus, the theoretical calculations show that the proposed Cr(VI) removal is effective.

Interactions of 5-deazapteridine derivatives with Mycobacterium tuberculosis and with human dihydrofolate reductases.

There are major differences between the structures of human dihydrofolate reductase (hDHFR) and Mycobacterium tuberculosis dihydrofolate reductase (mtDHFR). These differences may allow us to design more selective mtDHFR inhibitors. In this paper we study the reactions of six different compounds derived from 5-deazapteridine with human and bacterial enzymes. Results suggest that the addition of hydrophobic groups to the aminophenyl ring would increase mtDHFR-inhibitor affinity and selectivity.

3D-QSAR CoMFA/CoMSIA models based on theoretical active conformers of HOE/BAY-793 analogs derived from HIV-1 protease inhibitor complexes.

The three-dimensional quantitative structure-activity relationships (3D-QSAR) of a series of HOE/BAY-793 analogs (C(2)-symmetric diol peptidomimetics), developed by Budt and co-workers [Bioorg. Med. Chem. 3 (1995) 559] as inhibitors of HIV-1 protease (HIV-PR), were studied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Theoretical active conformers for these peptidomimetics were generated, derived from modeled protease inhibitor complexes, in order to orient the compounds superposition and to afford a consistent alignment. The best CoMFA model (N=27, q(2)=0.637, R(2)=0.991) showed contributions of the steric (45.7%) and electrostatic (54.3%) fields to the activity, while the best CoMSIA model (N=27, q(2)=0.511, R(2)=0.987) showed contributions of the electrostatic (68.5%) and hydrogen bond donor (37.5%) fields. The models were also external validated using four compounds (test set) not included in the model generation process. The statistical parameters from both models indicate that the data are well fitted and have high predictive ability. Moreover, the resulting 3D CoMFA/CoMSIA contour maps provide useful guidance for designing highly active ligands. The CoMFA/CoMSIA models were also compared with previous 4D-QSAR models [E.F.F. da Cunha, M.G. Albuquerque, O.A.C. Antunes, R.B. de Alencastro, QSAR Comb. Sci. 24 (2005), 240-253.].