Computational studies indicated the effectiveness of human metabolites against SARS-Cov-2 main protease

Abstract

To fight against the devastating coronavirus disease 2019 (COVID-19), identifying robust anti-SARS-CoV-2 therapeutics from all possible directions is necessary. To contribute to this effort, we selected a human metabolites database containing waters and lipid-soluble metabolites to screen against the 3-chymotrypsin-like proteases (3CL^pro) protein of SARS-CoV-2. The top 8 hits from virtual screening displayed a docking score varying between ~ - 11 and ~ - 14 kcal/mol. Molecular dynamics simulations complement the virtual screening study in conjunction with the molecular mechanics generalized Born surface area (MM/GBSA) scheme. Our analyses revealed that (HMDB0132640) has the best glide docking score, - 14.06 kcal/mol, and MM-GBSA binding free energy, - 18.08 kcal/mol. The other three lead molecules are also selected along with the top molecule through a critical inspection of their pharmacokinetic properties. HMDB0132640 displayed a better binding affinity than the other three compounds (HMDB0127868, HMDB0134119, and HMDB0125821) due to increased favorable contributions from the intermolecular electrostatic and van der Waals interactions. Further, we have investigated the ligand-induced structural dynamics of the main protease. Overall, we have identified new compounds that can serve as potential leads for developing novel antiviral drugs against SARS-CoV-2 and elucidated molecular mechanisms of their binding to the main protease. Identification of probable hits from human metabolites against SARS-CoV-2 using integrated computational approaches-Missed against MS.

Keywords: Free energy; Human metabolites; Main protease; Molecular dynamics; SARS-COV-2; Virtual screening.

Fig. 1

Ribbon representation of COVID-19 3CL^pro complexed with the inhibitor, shown in ball and stick representation. The different part of 3CL^pro is shown in different color, i.e., Green: Domain I, Cyan: Domain II, Blue: Domain III, Brown: Inter-domain connecting loop, and Red: Ligand molecules. The top 8 molecules which are screened by the virtual screening workflow are shown in 2D illustration

Fig. 2

A, B The time evolution of root means square deviation (RMSD) of backbone atoms of 3CL^pro-complex relative to their respective initial coordinates. C, D The root mean square fluctuations (RMSFs) of C_α atoms for all eight 3CL^pro-ligand complexes

Fig. 3

A, B Time evolution of inhibitors’ heavy atoms root mean square deviations (RMSDs) concerning initial conformations. C, D temporal RMSD variations of backbone atoms around the 5 Å of each ligand (binding pocket)

Fig. 4

The time evolution of distance between ligand and domain I (in red), ligand and domain II (in violet), ligand and domain I & II (in green). A complex1, B complex2, C complex3, D complex4, E complex5, F complex6, G complex7 and H complex8, respectively

Fig. 5

Per-residue decomposition of the binding free energy for A complex1, B complex2, C complex3, D complex4, E complex5, F complex6, G complex7 and H complex8, respectively. The energy contribution ≥ − 1.5 kcal/mol is shown in each plot

Fig. 6

The time evolution of the center of the mass distance (A, B) and the number of hydrogen bonds between 3CL^pro and human metabolites (C, D). In figure legend, the complex is termed as com in all cases

Fig. 7

The main protease, 3CL^pro, and human metabolites interaction profiles (affinity more than − 9 kcal/mol). 3D interaction profile for selected ligands where key residues are shown ball and stick model A Ligand1, B Ligand6, C Ligand7, and D Ligand8. 2D interaction plots are generated by using Ligplot+, E Ligand1, F Ligand6, G Ligand7, and H Ligand8. Pink spike semicircles show the residues involved in the hydrophobic interactions, and hydrogen bonds are shown as green dotted lines