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. 2022 Mar 20;20(3):215.
doi: 10.3390/md20030215.

In Silico Evaluation of Antifungal Compounds from Marine Sponges against COVID-19-Associated Mucormycosis

Omkar Pokharkar  1 , Hariharan Lakshmanan  2 , Grigory Zyryanov  1   3 , Mikhail Tsurkan  4
Affiliations

In Silico Evaluation of Antifungal Compounds from Marine Sponges against COVID-19-Associated Mucormycosis

Omkar Pokharkar et al. Mar Drugs. .

Abstract

The world is already facing the devastating effects of the SARS-CoV-2 pandemic. A disseminated mucormycosis epidemic emerged to worsen this situation, causing havoc, especially in India. This research aimed to perform a multitargeted docking study of marine-sponge-origin bioactive compounds against mucormycosis. Information on proven drug targets and marine sponge compounds was obtained via a literature search. A total of seven different targets were selected. Thirty-five compounds were chosen using the PASS online program. For homology modeling and molecular docking, FASTA sequences and 3D structures for protein targets were retrieved from NCBI and PDB databases. Autodock Vina in PyRx 0.8 was used for docking studies. Further, molecular dynamics simulations were performed using the IMODS server for top-ranked docked complexes. Moreover, the drug-like properties and toxicity analyses were performed using Lipinski parameters in Swiss-ADME, OSIRIS, ProTox-II, pkCSM, and StopTox servers. The results indicated that naamine D, latrunculin A and S, (+)-curcudiol, (+)-curcuphenol, aurantoside I, and hyrtimomine A had the highest binding affinity values of -8.8, -8.6, -9.8, -11.4, -8.0, -11.4, and -9.0 kcal/mol, respectively. In sum, all MNPs included in this study are good candidates against mucormycosis. (+)-curcudiol and (+)-curcuphenol are promising compounds due to their broad-spectrum target inhibition potential.

Keywords: CAM; COVID-19; SARS-CoV-2; antifungal; antiviral; bioactive compounds; disseminated mucormycosis; marine drugs; marine sponges; molecular docking.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Ramachandran plots for energy-minimized proteins from YASARA: (a) CotH3; (b) mucoricin; (c) RdRp; (d) lanosterol 14 alpha-demethylase.
Figure 2
Figure 2
Ramachandran plots for energy-minimized proteins from UCSF Chimera: (a) CotH3; (b) mucoricin; (c) RdRp; (d) lanosterol 14 alpha-demethylase.
Figure 3
Figure 3
ERRAT plots for energy-minimized proteins from YASARA: (a) CotH3; (b) mucoricin; (c) RdRp; (d) lanosterol 14 alpha-demethylase. (* The Y-axis is the error axis with two lines which indicates the confidence with the possibility to reject regions that exceed error value).
Figure 4
Figure 4
ERRAT plots for energy-minimized proteins from UCSF Chimera: (a) CotH3; (b) mucoricin; (c) RdRp; (d) lanosterol 14 alpha-demethylase. (* The Y-axis is the error axis with two lines which indicates the confidence with the possibility to reject regions that exceed error value).
Figure 5
Figure 5
Three-dimensional protein target structures: (a) CotH3; (b) mucoricin; (c) exo-1,3-beta-glucan synthase (d) RdRp; (e) rhizopuspepsin; (f) lanosterol 14 alpha-demethylase; (g) fungal lipase.
Figure 6
Figure 6
CAST-p pocket estimation: (a) CotH3 (b) mucoricin (c) exo-1,3-beta-glucan synthase (d) RdRp (e) rhizopuspepsin (f) lanosterol 14 alpha-demethylase (g) fungal lipase.
Figure 7
Figure 7
Ligand selection criteria—a schematic representation (MNPs—marine natural products).
Figure 8
Figure 8
CotH3-hyrtimomine A docked complex and interaction map.
Figure 9
Figure 9
Mucoricin-latrunculin A docked complex and interaction map.
Figure 10
Figure 10
exo-1,3-beta-glucan synthase-aurantoside I docked complex and interaction map.
Figure 11
Figure 11
RdRp-naamine D docked complex and interaction map.
Figure 12
Figure 12
Rhizopuspepsin-latrunculin S docked complex and interaction map.
Figure 13
Figure 13
Lanosterol 14 alpha-demethylase-(+)-curcudiol docked complex and interaction map.
Figure 14
Figure 14
Fungal lipase-(+)-curcuphenol docked complex and interaction map.
Figure 15
Figure 15
MD simulation output for CotH3-hyrtimomine A docked complex: (A) deformability; (B) B-factor; (C) variance; (D) eigenvalue; (E) covariance map; (F) elastic network model.
Figure 16
Figure 16
MD simulation output for mucoricin-latrunculin A docked complex: (A) deformability; (B) B-factor; (C) variance; (D) eigenvalue; (E) covariance map; (F) elastic network model.
Figure 17
Figure 17
MD simulation output for exo-1,3-beta-glucan synthase-aurantoside I docked complex: (A) deformability; (B) B-factor; (C) variance; (D) eigenvalue; (E) covariance map; (F) elastic network model.
Figure 18
Figure 18
MD simulation output for RdRp-naamine D docked complex: (A) deformability; (B) B-factor; (C) variance; (D) eigenvalue; (E) covariance map; (F) elastic network model.
Figure 19
Figure 19
MD simulation output for rhizopuspepsin-latrunculin S docked complex: (A) deformability; (B) B-factor; (C) variance; (D) eigenvalue; (E) covariance map; (F) elastic network model.
Figure 20
Figure 20
MD simulation output for lanosterol 14 alpha-demethylase-(+)-curcudiol docked complex: (A) deformability; (B) B-factor; (C) variance; (D) eigenvalue; (E) covariance map; (F) elastic network model.
Figure 21
Figure 21
MD simulation output for fungal lipase-(+)-curcuphenol docked complex: (A) deformability; (B) B-factor; (C) variance; (D) eigenvalue; (E) covariance map; (F) elastic network model.
Figure 22
Figure 22
Swiss-ADME boiled egg graph showing permeability of all 35 compounds.
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