De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations.

The main protease of SARS-CoV-2 is a critical target for the design and development of antiviral drugs. 2.5 M compounds were used in this study to train an LSTM generative network via transfer learning in order to identify the four best candidates capable of inhibiting the main proteases in SARS-CoV-2. The network was fine-tuned over ten generations, with each generation resulting in higher binding affinity scores. The binding affinities and interactions between the selected candidates and the SARS-CoV-2 main protease are predicted using a molecular docking simulation using AutoDock Vina. The compounds selected have a strong interaction with the key MET 165 and Cys145 residues. Molecular dynamics (MD) simulations were run for 150ns to validate the docking results on the top four ligands. Additionally, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and hydrogen bond analysis strongly support these findings. Furthermore, the MM-PBSA free energy calculations revealed that these chemical molecules have stable and favorable energies, resulting in a strong binding with Mpro's binding site. This study's extensive computational and statistical analyses indicate that the selected candidates may be used as potential inhibitors against the SARS-CoV-2 in-silico environment. However, additional in-vitro, in-vivo, and clinical trials are required to demonstrate their true efficacy.

Atomistic insight into 2D COFs as antiviral agents against SARS-CoV-2.

The recent pandemic of COVID-19 has raised global health concerns. Preventing severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) activity in the body is a very promising method to overcome the COVID-19 pandemic. One of the prevention methods is constraining the binding process among the human cell receptor-ACE2 and coronavirus spike protein. In the research done, the effect of deformation of the spike protein structure, due to the covalent organic frameworks (COFs), in reducing the interactions of ACE2 and the spike protein by the computational method was investigated. In this regard, atomic analysis of the interactions of ACE2 and the spike protein is provided using a molecular dynamics simulation. First, we investigated the interactions of the three different COFs, including COF-78, DAAQ-TFP, and COF-OEt, with the spike protein by analyzing the bond energies, as well as structural changes of the spike protein. Then, intermolecular interactions of the deformed spike protein along with ACE2 were assessed to clarify the protein's fusion after the deformation. As indicated by the results, although all introduced COFs deformed the spike protein in an effective way, COF-78 showed the best performance in the prevention of spike protein-ACE2 interactions by changing the molecular structure of the protein. Indeed, the interaction analysis of the deformed spike protein by COF-78 with the ACE2 showed that their interactions had the lowest absolute value of energy, along with the least amount of hydrogen bonds, in which the compaction of the protein was lower compared to the other deformed proteins. Moreover, having a high contact area with an aqueous media as well as severe fluctuations during the simulation time confirmed the positive performance of COF-78. In the current study, we aimed to introduce novel materials and COVID-19 prevention methodology that can be used in face masks and for surface disinfection.

Potential inhibitors of the main protease of SARS-CoV-2 and modulators of arachidonic acid pathway: Non-steroidal anti-inflammatory drugs against COVID-19.

The main protease of SARS-CoV-2 is one of the key targets to develop and design antiviral drugs. There is no general agreement on the use of non-steroidal anti-inflammatory drugs (NSAIDs) in COVID-19. In this study, we investigated NSAIDs as potential inhibitors for chymotrypsin-like protease (3CLpro) and the main protease of the SARS-CoV-2 to find out the best candidates, which can act as potent inhibitors against the main protease. We also predicted the effect of NSAIDs on the arachidonic pathway and evaluated the hepatotoxicity of the compounds using systems biology techniques. Molecular docking was conducted via AutoDock Vina to estimate the interactions and binding affinities between selected NSAIDs and the main protease. Molecular docking results showed the presence of 10 NSAIDs based on lower binding energy (kcal/mol) toward the 3CLpro inhibition site compared to the co-crystal native ligand Inhibitor N3 (-6.6 kcal/mol). To validate the docking results, molecular dynamic (MD) simulations on the top inhibitor, Talniflumate, were performed. To obtain differentially-expressed genes under the 27 NSAIDs perturbations, we utilized the L1000 final Z-scores from the NCBI GEO repository (GSE92742). The obtained dataset included gene expression profiling signatures for 27 NSAIDs. The hepatotoxicity of NSAIDs was studied by systems biology modeling of Disturbed Metabolic Pathways. This study highlights the new application of NSAIDs as anti-viral drugs used against COVID-19. NSAIDs may also attenuate the cytokine storm through the downregulation of inflammatory mediators in the arachidonic acid pathway.