Screening marine algae metabolites as high-affinity inhibitors of SARS-CoV-2 main protease (3CLpro): an in silico analysis to identify novel drug candidates to combat COVID-19 pandemic

Abstract

The recent dissemination of SARS-CoV-2 from Wuhan city to all over the world has created a pandemic. COVID-19 has cost many human lives and created an enormous economic burden. Although many drugs/vaccines are in different stages of clinical trials, still none is clinically available. We have screened a marine seaweed database (1110 compounds) against 3CLpro of SARS-CoV-2 using computational approaches. High throughput virtual screening was performed on compounds, and 86 of them with docking score < - 5.000 kcal mol^-1 were subjected to standard-precision docking. Based on binding energies (< - 6.000 kcal mol^-1), 9 compounds were further shortlisted and subjected to extra-precision docking. Free energy calculation by Prime-MM/GBSA suggested RC002, GA004, and GA006 as the most potent inhibitors of 3CLpro. An analysis of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties of RC002, GA004, and GA006 indicated that only RC002 (callophysin A, from red alga Callophycus oppositifolius) passed Lipinski's, Veber's, PAINS and Brenk's filters and displayed drug-like and lead-like properties. Analysis of 3CLpro-callophysin A complex revealed the involvement of salt bridge, hydrogen bonds, and hydrophobic interactions. callophysin A interacted with the catalytic residues (His41 and Cys145) of 3CLpro; hence it may act as a mechanism-based competitive inhibitor. Docking energy and docking affinity of callophysin A towards 3CLpro was - 8.776 kcal mol^-1 and 2.73 × 10⁶ M^-1, respectively. Molecular dynamics simulation confirmed the stability of the 3CLpro-callophysin A complex. The findings of this study may serve as the basis for further validation by in vitro and in vivo studies.

Keywords: Callophysin A; Marine-derived compounds; Molecular docking and simulation; SARS-CoV-2; Seaweeds.

Fig. 1

Molecular docking between callophysin A and 3CLpro in extra-precision (XP) mode. a 2D and b 3D representation of the binding mode of callophysin A to the active site of 3CLpro, c Interaction between callophysin A and active site residues, and d Interaction between 3CLpro and callophysin A, showing the involvement of different amino acid residues and the molecular forces between protein and inhibitor

Fig. 2

MD (Molecular dynamics) simulation of 3CLpro-callophysin A complex. a RMSD (Root mean square deviation) of 3CLpro alone (teal color) and in the presence of callophysin A (brown color), b RMSF (root mean square fluctuation) of 3CLpro in the presence of callophysin A (teal color), as compared with B-factor, which is determined during X-ray crystallography (brown color). Vertical green lines represent the location of amino acid residue forming an interaction with callophysin A. Light brown, and teal color bars represent the regions of α-helices and β-sheets

Fig. 3

Interaction between 3CLpro and callophysin A during the simulation. a Amino acid residues of 3CLpro making different kinds of interaction with callophysin A, b Upper panel: variation in the number of contacts between 3CLpro and callophysin A during the simulation. Lower panel: participation of different amino acid residues in making contacts with callophysin A as a function of simulation, and c Percentage of simulation time for which some significant amino acid residues participate in the interaction with callophysin A

Fig. 4

a Percentage of 3CLpro-callophysin A secondary structure element (SSE) varied during simulation, and b Involvement of 3CLpro amino acid residues in SSEs formation, wherein α-helices β-sheets are represented in light brown and teal colors, respectively

Fig. 5

Variation in a rGyr (radius of gyration), b MSA (molecular surface area), c SASA (solvent accessible surface area), and d PSA (polar surface area) of 3CLpro-callophysin A complex during the simulation