Biovia discovery studio visualizer

In advance of building pharmacophore models, structures of 15 compounds were drawn in the ACD/ChemSketch program (freeware) 2020 1.2 (Hunter, 1997 ) and submitted to geometry optimization in the program BIOVIA Discovery Studio Visualizer (v 17.2.0.16349) (Biovia et al., 2000 (link)). The force field used was the MM+ (Molecular Mechanics), according to the methodological strategy proposed by da Silva Costa et al. (2018) (link); afterwards the structures underwent refinement by using the Dreiding-like force field (Hahn, 1995 (link)).
After optimization of compounds, their structures were inputted in the BIOVIA Discovery Studio Visualizer (v17.2.0.16349) and gathered into a single file (mol2). Then, this file was submitted to the BindingDB webserver (https://www.bindingdb.org/bind/index.jsp) for calculation of similarities values by means of Tanimoto index (TI) (Liu et al., 2007 (link)). TI values (Eq. 1) varies between 0 and 1, representing the overall similarity between two compounds based on their fingerprint bits (molecular fragments), so that the closer to 1, greater the similarity (Gimeno et al., 2019 (link)). $Tanimoto Index=\frac{\mathit{c}}{\left(\mathit{a}+\mathit{b}-\mathit{c}\right)\mathit{ }}$ Where, for two generic compounds A and B: a: number of bits in A; b: number of bits in B; c: number of common bits between A and B.

We explored the interactions between bioactive ingredients and their target proteins to evaluate their binding site and affinity. We selected three major alkaloids of CR with the highest content [51 (link)] as ligands and M1 and M2 macrophages as receptors to explore their actions on the macrophages. We also included TNF as a receptor, which is involved in the hub signaling pathways obtained from the functional enrichment analysis. Three-dimensional (3D) structural data of berberine (CID 2353), palmatine (CID 19009), and coptisine (CID 72322) were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/) (accessed on 10 January 2021). PDB data (https://www.rcsb.org) (accessed on 10 January 2021) were used to obtain the structures of M1 macrophages (PDBID: 1GD0), M2 macrophages (PDBID: 1JIZ), and TNF (PDBID: 2AZ5). We used the Biovia Discovery Studio Visualizer to pretreat target proteins by removing HETATM and water and adding polar groups. We used Pyrx to perform molecular docking and assess binding affinity and utilized the Biovia Discovery Studio Visualizer to visualize the binding structures. Virtual screening and calculation of the binding score (kcal/mol) were performed using Autodock VINA [52 (link)].

The top 81 NP candidates from docking runs performed against the 6LU7 crystal structure of the SARS-CoV-2 M^pro were further analyzed. The docking visualization of these NP candidates was performed on PyMOL 2.5 and BIOVIA Discovery Studio Visualizer (https://discover.3ds.com/ (accessed on 12 July 2021)) to visually inspect the docking location of the NP ligand within the M^pro’s target cavity. The types and number of bonds present between the NP ligand and the Thr24, Thr26, His41, Phe140, Asn142, Gly143, Cys145, His163, His164, Glu166, and His172 amino acid residues of each M^pro protein structure were observed using the BIOVIA Discovery Studio Visualizer. The given 11 amino acid residues were previously shown in the literature to play important roles in the activity of the SARS-CoV-2 M^pro. These interactions were analyzed and weighted to further validate the efficacies of the results obtained from the molecular docking analysis from the previous step. Observations were made based on the number of hydrogen bonds and other interactions present between the NP ligands and residues within M^pro’s target cavity. An arbitrary “Hit Score” was calculated by assigning a value of “1” if the ligand had a hydrogen bond with the 11 amino acid residues of M^pro and a value of “0.5” with any other types of interactions with the 11 amino acids (Table S2).