key: cord-0998205-5u6oqh2z
authors: Kumar, K. Amith; Sharma, Monica; Dalal, Vikram; Singh, Vishakha; Tomar, Shailly; Kumar, Pravindra
title: Multifunctional Inhibitors of SARS CoV2 by MM/PBSA, Essential dynamics, and Molecular dynamic investigations.
date: 2021-06-17
journal: J Mol Graph Model
DOI: 10.1016/j.jmgm.2021.107969
sha: 692b0a451e877d18cd5c223c115070406742b05d
doc_id: 998205
cord_uid: 5u6oqh2z

The ongoing COVID-19 pandemic demands a novel approach to combat and identify potential therapeutic targets. The SARS-CoV2 infection causes a hyperimmune response followed by a spectrum of diseases. Limonoids are a class of triterpenoids known to prevent the release of IL-6, IL-15, IL-1α, IL-1β via TNF and are also known to modulate PI3K/Akt/GSK-3β, JNK1/2, MAPKp38, ERK1/2, and PI3K/Akt/mTOR signaling pathways and could help to avoid viral infection, persistence, and pathogenesis. The present study employs a computational approach of virtual screening and molecular dynamic (MD) simulations of such compounds against RNA-dependent RNA polymerase (RdRp), Main protease (Mpro), and Papain-like protease (PLpro) of SARS-CoV2. MD simulation, Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA), and Essential dynamics revealed that the macromolecule-ligand complexes are stable with very low free energy of binding. Such compounds that could modulate both host responses and inhibit viral machinery could be beneficial in effectively controlling the global pandemic.

AutoDock Tools was used for molecular docking of azadirachtins, ceramicines, and withanolides 24 with RdRp, Mpro, and PLpro [35] . The hydrogen atoms and gasteiger charges were added and 25 saved in .pdbqt format. The molecular grid was set in between motif A (612-626) and motif G 26 (499-511) covering the catalytic core of RdRp [39] . For PLpro the grid was set around the S3 27 pocket and S4 pocket that opens into the active site for Mpro the grid was set in between 28 domains 1 and 2 covering the catalytic dyad of His41 and Cys145 [40] . The details of the grid 29 are mentioned in Table S1 . The Lamarckian genetic algorithm was used to generate 25 docked The molecular dynamics simulations were performed to explore the structural and 4 conformational stability of protein and protein-ligand complexes. Protein and protein-ligand 5 complexes were subjected to molecular dynamics was using the GROMOS96 43a1 force field in 6 GROMACS 2019.5 and the topology of the ligands was generated using PRODRG online server 3Clwere added to RdRp, Mpro and PLpro respectively;to maintain the overall neutrality of the 10 system. The steric clashes were dealt with the steepest descent minimization algorithm, carried 11 for 50,000 steps with a maximum force of 10 kJmol -1 . The systems were equilibrated using NVT 12 and NPT for 100 ps of position restrain. Periodic boundary conditions were employed at a 13 constant temperature of 300 K and 1 atm pressure using the V-rescale temperature coupling 14 method and Parrinello-Rahman coupling method, respectively. The Particle Mesh Ewald method 15 was used for computing the long-range electrostatic interactions [46] . Finally, the leap-frog 16 algorithm was employed to carry out the 50 ns of production dynamics for the equilibrated 17 system [47] . The generated trajectories were used to monitor the structural deviations and 18 fluctuations of the protein and protein-ligand complexes. [48, 49] . Linear Constraint Solver 19 (LINCS) algorithm was used to constrain the bond lengths of heavy atoms [50] . The short-range 20 forces were calculated with a minimum cutoff set to 1.2 nm using verlet cutoff scheme [51] . The 21 relative root mean square deviation (RMSD) with respect to the initial reference trajectory was 

MM/PBSA was performed over a total of 1000 snapshots, generated at every 10 ps from the final 16 10 ns of molecular dynamics using a one step calculation method as described earlier [55-57]. 20 Virtual screening of azadirachtins, ceramicines, and withanolides were done against RdRp, 21 PLpro, and Mpro. In RdRp, AutoDock Vina results showed that all the screened molecules bind 22 in a similar mode with a binding affinity in the range of -8.6 to -7.8 kcal/mol, as shown in Table   23 2. Screened azadirachtins, ceramicines, and withanolides bound at the substrate-binding pocket 24 of Mpro with a binding affinity of -9.2 to -7.0 kcal/mol, as shown in Table 2 . The compounds 25 were estimated to bind within the S3 S4 subsites of SARS-CoV-2 PLpro. The binding affinity of 26 ligands with PLpro is mention in Table 2 . Three molecules from each class exhibit the highest 27 binding affinities with macromolecules were considered for molecular docking. Lys545, Arg553, Arg555, Asp623, Asp760, and Asp761 residues of RdRp with a binding 3 affinity in the range of that -13.7 to -8.7 kcal/mol, as shown in Figure 4 and Table 2. Figure 5   4 shows that azadirachtin, ceramicine, and withanolide interact with the catalytic triad (His41 Table 2 . Azadirachtin, ceramicine, 10 and withanolide bind with amino acid residues (Trp106, Lys157, Leu162, Gly163, Asp164, 11 Arg166, Glu167, Pro247, Pro248, Tyr264, Tyr267, Tyr268, Tyr273, Thr301, and Thr302) of 12 PLpro, as shown in Figure 6 . In Table 2 , it can be seen that all the ligands with PLpro exhibit 13 binding affinity in the range of -11.2 to -6.2 kcal/mol. Overall, molecular docking results 14 suggested that azadirachtin, ceramicine, and withanolide can bind at the active site of RdRp, 15 Mpro, and PLpro. respectively, as shown in Figure 7C and Table 3 . 19 Hydrogen bonding is a critical driving force defining the stability and flexibility of the ligands in (Table 3) . CoV-2 and are also known to inhibit cathepsin-L, which is critical for the entry of SARS-CoV2 12 [18, 19, 21] . They are reported to inhibit cytoprotective autophagy and promote apoptosis of J o u r n a l P r e -p r o o f 

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