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

Abstract

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.

Keywords: Deep learning; Main protease; Molecular docking; Molecular dynamic simulation; SARS-CoV-2.

Fig. 1

(a). A block diagram illustrating the network's training phase. (b) A block diagram depicting the generation, fine-tuning, and evaluation sessions.

Fig. 2

The outcome of the hierarchical clustering process. By setting the cut-off distance to six, similar ligands were divided into four clusters.

Fig. 3

The total number of fingerprints in each cluster.

Fig. 4

The 2D binding mode and molecular interaction of selected ligands in Mpro's active site (PDB ID: 6LU7).

Fig. 5

(A) RMSD of the protein backbone in complex with four ligands. (B) RMSD of the protein backbone in complex with N3 and Remdesivir at 150 ns MD simulations. (C) RMSD of the four ligands. (D) RMSD of N3 and Remdesivir in complex with Mpro.

Fig. 6

The RMSF values of the protein's backbone throughout the simulations, where the ordinate is the RMSF (nm), and the abscissa is the residue number.

Fig. 7

(A) The total number of H-bonds formed during the simulation of the selected ligand with Mpro residue. (B) The total number of H-bonds formed during the simulation of N3 and Remdesivir with Mpro residue.

Fig. 8

Contribution of individual residues to the binding energy of each complex (ligands A, B, C, D, and N3).

Fig. 9

Comparing the initial (a) and final (b) structures obtained from MD simulations.

Fig. 10

(A) The radius of the protein complex's gyration (Rg) profile in the presence of the selected ligands after 150000 ps MD simulations. (B) Radius of gyration (Rg) profile of the protein complex in the presence of N3 and Remdsivir after 150000 ps MD simulations. The plots display time (ps) on the x-axis and the gyration radius (nm) on the y-axis.

Fig. 11

(A) The projected trajectory of Remdesivir (positive reference) (left panel). (B) The projected trajectory of ligand 8 (right panel) for the last 60 ns subtrajectory.

Fig. 12

minimum and average binding affinity scores for the ten generations.