Molecular modelling studies of tricyclic triazinone analogues as potential PKC-θ inhibitors through combined QSAR, molecular docking and molecular dynamics simulations techniques

Computational strategies towards developing novel SARS-CoV-2 Mpro inhibitors against COVID-19

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains to be a serious threat due to the lack of a specific therapeutic agent. Computational methods are particularly suitable for rapidly fight against SARS-CoV-2. This present research aims to systematically explore the interaction mechanism of a series of novel bicycloproline-containing SARS-CoV-2 M^pro inhibitors through integrated computational approaches. We designed six structurally modified novel SARS-CoV-2 M^pro inhibitors based on the QSAR study. The four designed compounds with higher docking scores were further explored through molecular docking, molecular dynamics (MD) simulations, free energy calculations, and residual energy contributions estimated by the MM-PBSA approach, with comparison to compound 23(PDB entry 7D3I). This research not only provides robust QSAR models as valuable screening tools for the development of anti-COVID-19 drugs, but also proposes the newly designed SARS-CoV-2 M^pro inhibitors with nanomolar activities that can be potentially used for further characterization to treat SARS-CoV-2 virus.

Green Route for the Removal of Pb from Aquatic Environment

AIM AND OBJECTIVE

Wastewater treatment/remediation is a very important process that has a great environmental and economic impact. Therefore, it is crucial to innovate different methods to remove pollutants of different sources from wastewater. This work was conducted in order to study the removal of lead (Pb+2) from wastewater using microspheres of composites of sodium alginate, cellulose and chitosan, as well as using a cost-effective green route through composites of sodium alginate and dried water hyacinth.

MATERIALS AND METHODS

Molecular modeling at B3LYP/6-31g(d,p) was utilized to study sodium alginate, cellulose and chitosan. Sodium alginate was cross-linked with calcium chloride to form microspheres, then both sodium alginate/cellulose and sodium alginate/chitosan were also crosslinked as 50/50 to form microspheres. The roots of the aquatic plant water hyacinth in dry form were added to the cross-linked sodium alginate for up to 70%. SEM and FTIR were employed to study the surface of the prepared microspheres and their structures respectively. Atomic absorption spectroscopy was used to study the levels of Pb.

RESULTS

Molecular modeling indicated that the blending of such structures enhances their ability to bind with surrounding molecules owing to their ability to form hydrogen bonds. SEM results indicated that homogeneous structures of cellulose and chitosan are deformed when blended with sodium alginate, and FTIR confirmed the proper formation of the desired blends. Microspheres from sodium alginate showed the ability to remove Pb+2 from wastewater. SEM indicated further deformation in the morphology with the roughness of sodium alginate/water hyacinth microspheres, while FTIR confirmed the uniform matrices of the microspheres. The removal of Pb+2 was enhanced because of the addition of dried water hyacinth's roots.

CONCLUSION

Modeling, experimental and kinetic data highlight sodium alginate/water hyacinth root as a green route to remediate Pb+2 from wastewater.

QSAR Modeling, Molecular Docking and Molecular Dynamics Simulations Studies of Lysine-Specific Demethylase 1 (LSD1) Inhibitors as Anticancer Agents

BACKGROUND

Histone Lysine Demetylases1 (LSD1) is a promising medication to treat cancer, which plays a crucial role in epigenetic modulation of gene expression. Inhibition of LSD1with small molecules has emerged as a vital mechanism to treat cancer.

OBJECTIVE

In the present research, molecular modeling investigations, such as CoMFA, CoMFA-RF, CoMSIA and HQSAR, molecular docking and Molecular Dynamics (MD) simulations were carried out on some tranylcypromine derivatives as LSD1 inhibitors.

METHODS

The QSAR models were carried out on a series of Tranylcypromine derivatives as data set via the SYBYL-X2.1.1 program. Molecular docking and MD simulations were carried out by the MOE software and the SYBYL program, respectively. The internal and external predictability performances related to the generated models for these LSD1 inhibitors were justified by evaluating cross-validated correlation coefficient (q²), noncross- validated correlation coefficient (r²_ncv) and predicted correlation coefficient (r²_pred) of the training and test set molecules, respectively.

RESULTS

The CoMFA (q², 0.670; r²_ncv, 0.930; r²_pred, 0.968), CoMFA-RF (q², 0.694; r²_ncr, 0.926; r²_pred, 0.927), CoMSIA (q², 0.834; r²_ncv, 0.956; r²_pred, 0.958) and HQSAR models (q², 0.854; r²_ncv, 0.900; r²_pred, 0.728) for training as well as the test set of LSD1 inhibition resulted in significant findings.

CONCLUSION

These QSAR models were found to be perfect and strong with better predictability. Contour maps of all models were generated and it was proven by molecular docking studies and molecular dynamics simulation that the hydrophobic, electrostatic and hydrogen bonding fields are crucial in these models for improving the binding affinity and determining the structure-activity relationship. These theoretical results are possibly beneficial to design new strong LSD1 inhibitors with enhanced activity to treat cancer.