In-silico study for the screening and preparation of ionic liquid-AVDs conjugate to combat COVID-19 surge

Abstract

The pandemic due to COVID-19 caused by SARS-CoV-2 has led to the recorded deaths worldwide and is still a matter of concern for scientists to find an effective counteragent. The combination therapy is always been a successful attempt in treating various threatful diseases. Recently, Ionic liquids (ILs) are known for their antiviral activity. Fascinating tunable properties of ILs make them a potential candidate for designing the therapeutic agent. The concern while using ILs in biomedical field remains is toxicity therefore, choline-based ILs were used in the study as they are considered to be greener as compared to other ILs. In the present study strategically, we performed the blind molecular docking of antiviral drug (Abacavir, Acyclovir, and Galidesivir)-choline based ILs conjugates with the target protein (M_pro protease). The molecules were screened on the basis of binding energy. The data suggested that the combination of AVDs-ILs have greater antiviral potential as compared to the drugs and ILs alone. Further, the ADME properties and toxicity analysis of the screened conjugates was done which revealed the non-toxicity of the conjugates. Additionally, the energetic profiling of the ILs drugs and their conjugates was done using DFT calculations which revealed the stability of the conjugates and have a better option to be developed as a therapeutic agent. Also, from molecular dynamic simulation was done and results showed the stability of the complex formed between target protein and the designed conjugates of AVDs and ILs.

Keywords: Antiviral drugs; COVID-19; Conjugates; DFT; Ionic liquids; MD simulation.

Graphical abstract

Fig. 1

Chemical structures of (a) Choline-based ILs (b) antiviral drugs (c) designed conjugates using ILs and AVDs.

Fig. 1

Chemical structures of (a) Choline-based ILs (b) antiviral drugs (c) designed conjugates using ILs and AVDs.

Fig. 2

Representation 3D (a) and 2D (b) of (I)ABA (II) ABA_CL4 (III) ABA_CL6 (IV) ABA_CL8 (V) ABA_CL10 (VI) ABA_CL12 docked M_pro protease.

Fig. 3

Representation 3D (a) and 2D (b) of (I) ACY (II) ACY_CL4 (III) ACY_CL6 (IV) ACY_CL8 (V) ACY_CL10 (VI) ACY_CL12 docked M_pro protease.

Fig. 4

Representation 3D (a) and 2D (b) of (I) GAL (II) GAL_CL4 (III) GAL_CL6 (IV) GAL_CL8 (V) GAL_CL10 (VI) GAL_CL12 docked M_pro protease.

Fig. 5

Representation 3D (a) and 2D (b) of (I) CL4 (II) CL6 (III) CL8 (IV) CL10 (V) CL12 docked M_pro protease.

Fig. 6

Frontier molecular orbitals (HOMO and LUMO) and optimized geometry of (a) ABA (b) CL8 (c) CL10 (d) CL12 (e) ABA_CL8 (f) ABA_CL10 (g) ABA_CL12.

Fig. 6

Frontier molecular orbitals (HOMO and LUMO) and optimized geometry of (a) ABA (b) CL8 (c) CL10 (d) CL12 (e) ABA_CL8 (f) ABA_CL10 (g) ABA_CL12.

Fig. 7

(a) Root mean square deviation of the backbone atoms of the protein and docked complex (b) Root mean square fluctuation of the backbone atoms of the protein and docked complex (c) Radius of gyration of the complex generated from 100 ns MD simulations trajectory.