Structure Based Screening Of Fungal Bioactive Metabolites As Potential Inhibitors Of Main Protease (Mpro) Of SARS-CoV-2

The pandemic disease COVID-19 has put the world into a massive threat. Till now no effective treatment has been established and the world is applying lockdowns as a preventive measure against this. Recent studies have identied that COVID-19 main protease (M pro ) is essential for its life cycle and is responsible for the proteolytic mutation of this virus. Thus, destruction of the complete viral particle can be achieved through inhibition of this main protease. The fungal metabolites have already been proved as potential antimicrobial agents hence were selected for in-silico molecular docking, to check their anity towards COVID-19 main protease. Metabolites like 2H-oxecin-3-one,3,4,7,8,9,10hexahydro-4-methoxy-10-methyl, Eicosanoic acid phenylmethyl ester, HEPES, 2,4-dihydroxy,2,5-dimethyl-furan(3)one and Dodecanoic acid-3-hydroxy have revealed strong binding anity towards the main protease. Thus, the present study enlightens a path to combat COVID-19 by the use of bioactive metabolites.


Introduction
In 1965, two scientists Tyrell and Bynoe discovered the rst human coronavirus named B814 (Tyrell and Bynoe, 1966). With the onset of the 21 st century as many as 5 new types of human coronavirus were identi ed with SARS (Severe Acute Respiratory Syndrome) coronavirus considered as the most harmful till the end of 2019 (Khan et al,2020). The end of 2019 witnessed the emergence of the deadliest and extremely contagious new coronavirus, o cially termed as SARS-COV-2 or Novel Coronavirus (Mittal et al, 2020). A rapid outbreak of pneumonia like illness was identi ed in Wuhan city, Hubei province, China (Anderson et al,2020, later coined as COVID-19(Corona Virus like Disease-19) (Al-Khafaji et al,2020). The community transmission was rst con rmed in Guadong province, China (Chen et al, 2020). World Health Organization has declared this as global pandemic with more than 150 countries being affected. The progressing lockdown in most of the countries and the extreme contagious nature of the virus has left the scienti c community to rely on the in-silico approach as this is the most feasible and possible way which can save precious time and also give some insights and hope for a novel drug discovery effective against this deadly virus.
SARS-CoV-2 belongs to Sarbecovirus genus or β-coronavirus subgenus, generally identi ed as enveloped virus containing single positive stranded RNA , Chan et al 2020, Licastro et al 2020, Lu et al, 2020. The genome of SARS-CoV-2 is the largest among all other RNA virus reported so far (El ky, 2020). The principle function of the genome begins after its infection into the host cell. The host infection is performed by the angiotensin converting enzyme 2 ie ACE2 receptors (Babadei Nejadi et al,2020). The single stranded RNA acts as messenger RNA and synthesizes two long polyproteins, polyprotein1a and polyprotein 1ab. These translated polyproteins then in turn code for new viral particles (Boopathi et al, 2020). The polyproteins also code for several mRNAs, structural proteins and two essential proteases that perform the cutting of polyproteins into other more important functional slices (John et al,2015, Cui et al,2019. Recently, various strategies have been designed for the drug development for SARS-CoV-2, structure based virtual screening being the most likely (Krishnan et al,2020). Many researchers have already screened the already available databases for potential compounds. Docking studies have also been effectively employed for screening small molecules against the major proteins of COVID-19 (Benitez-Cardova et al, 2020, Enmozhi et al,2020. Scientists worldwide have analysed through experimental ndings three main drug targets for SARS-CoV-2 which includes the RNA dependant RNA Polymerase (RdRp), the Angiotensin -converting enzyme 2 entry receptor (ACE2) and the main protease (M pro ) (Borgio et al,2020). The COVID-19 main protease (M pro ) is an essential protease and plays key role in viral proteolytic mutation (Jin et al, 2020). Thus, disruption or blocking of this main protease can led us towards effective drug development for this deadly virus (Gupta et al,2020). The drug molecules targeted towards ACE2 and RdRp have exhibited lower potency and signi cant side effects (Cameron and Castro, 2001). The M pro has emerged as the most promising drug targets for SARS-CoV-2 (Blanchard et al,2004).
In recent years fungal world has gained worldwide attention as producers of some immensely valuable metabolites that have also been found effective against variety of pathogens (Shrivastava and Saraf, 2019). Our study thus focuses on employing these metabolites for drug designing against SARS-CoV-2 through structure based virtual screening.

Materials And Methods
Fungi are repertoires of a large variety of secondary metabolites, the most important being penicillin, lovastatin, β-lactam antibiotics and a cholesterol lowering drug (Boruta, 2018). However, production of pharmaceutically important lead compounds can be better achieved by altering the standard growth conditions which in turn trigger the associated biosynthetic genes.
Scientists have already studied the effect of co-cultivation of fungi with Lactobacillus (Strom,2005). Similar strategy has been applied in our work.
Production and characterization of fungal secondary metabolites-The production of fungal secondary metabolites was done according to the steps described by Shrivastava et al (2017). The media was supplemented with milk, hence providing the altered growth conditions in presence of Lactobacillus. Two fungal strains were procured from MTCC, IMTECH, Chandigarh, India to serve as control, namely Aspergillus avus (MTCC 2725) and Penicillium chrysogenum (MTCC 3380). The GC-MS analysis was used for the identi cation of bioactive molecules. A total of 16 molecules were identi ed as potential lead compounds and were then employed for molecular docking.
Target Selection-The three-dimensional structure of SARS-CoV-2 main protease (6LU7) with resolution 2.16Å was collected from Protein data bank (Jin et al,2020). The target protein was pre-prepared with Autodock v4.2.6. The non-bonded and water atoms were deleted and partial charges were added.
Ligand Preparation: The GC-MS analysis of fungal secondary metabolites revealed 9 druggable compounds. The SDF les were converted to mol and mol2 les and were prepared for docking. The 3d conformations were determined through ChemSketch (ACD Labs).
Drug Likeness Prediction:-The drug likeness property is predicted to check the pharmacological properties and their ability to form a standard drug. The druggability of the compounds were checked through DruLito software (an open access software developed by the Department of Pharmacoinformatics, NIPER, India) and were con rmed through SwissADME Predictions. In addition to this, for drug designing PAINS Alert consideration is necessary. Pan-assay interference compounds (PAINS) are chemical compounds that during high throughput screening, frequently give false positive results (Dahlin et al,2015).
Molecular Docking:-AutoDock v4.2.6 (Trott and Olsen, 2010) tools have been used for the virtual docking of various ligands to the target protein. The protein was prepared by deleting the water molecules and adding partial charges. The active site was determined by deleting the ligand N3.A grid box with co-ordinates X= -19.09, Y= 21.93 and Z= 67.924 was generated and all the compounds were docked onto this active site of the target protein.
Molecular Dynamics Simulations: MD simulation was performed for the analysis of the dynamic properties and conformational exibility of protein and selected docked ligand into the target site. The protein-ligand complex giving the best docked score was selected for the MD simulation run. GROMACS 2018.1 package was used to run and analyze 100 ns MD simulation. GROMOS96 43a1(force eld) in single point charge (SPC) water models was used to generate protease and ligand force eld and parameter les for the protein and PRODRG server for ligand respectively. The system was then solvated with water molecules in separated cubic boxes with 10 Å distance from the edge of the box. The system was neutralized by adding 4 NA + counter ions. The energy minimization the system was performed through running the steepest descent minimization algorithm with 50000 steps to achieve stable system with maximum force < 1000 kJ mol −1 nm −1 . Prior to the running of real dynamics, the solvent and ions of the system was equilibrated by NVT (constant under number of particles, volume, and temperature) and NPT (constant number of particles, pressure, and temperature) ensemble. After the completion of the MD simulation run, the trajectories were used for various dynamics analysis such as root mean square deviation (RMSD), root mean square uctuation (RMSF), radius of gyration (Rg), number of hydrogen bonds, etc.
MM-PBSA Calculations: Docking and scoring are the most widely used computational methods for drug designing estimating the binding a nity followed by the binding energy between the receptor and ligand. This approach sometimes cannot be particularly accurate (Genheden and Ryde, 2015). Hence MD Simulations combined with MM-PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) calculations is performed to compute interaction energies and to study biomolecular complexes (Kumari et al, 2014). The MM-PBSA binding free energy, the change in potential energy in vacuum, Vander-waals and electrostatic interaction and polar and non-polar solvation energies of receptor-ligand docked complex was estimated by using this technique.

Results
Identi cation of bioactive metabolites isolated from fungal colonies: The majority of bioactive metabolites were isolated from 2 main fungal colonies namely Acremonium cellulolyticus and Aspergillus chevialeri. In addition the positive control fungal species also produced metabolites. The individual metabolites revealed via GC-MS analysis are as follows:-From Acremonium cellulolyticus-6-Acetyl-β-D-mannose, 2-Furancarboxaldehyde-5 methyl, HEPES, Desulphosinigrin.
2. Radius of gyration (Rg) analysis-The Rg analysis describes the compactness of receptor-ligand complex during the simulations. Less variation in the Rg value indicates the compact structure of the receptor-ligand while a high variation in the Rg value indicates the unstablility or less compactness of the structure. In this study the Rg value was found to variate from 1.945 nm to 1.967 nm (Represented in gure 21).
3. Root mean square uctuation (RMSF) analysis-The RMSF analysis determines the exibility and mobility of the structure. In this study the RMSF value of protein and ligand complex was found to be around 0.1 to 0.4 nm reaching a peak at 0.397 nm.
From the above analysis it can be concluded that the protein ligand structure exhibited less conformational changes and can act as a stable complex.
MM/PBSA Binding Free Energy calculations-The binding energy, Vander-waals energy, Electrostatic energy, Potential energy and polar and non polar solvation energies of the complex are as represented in Table 3.
The binding energy calculated as -978.405 KJ/mol can be considered as a good score con rming a stable binding a nity of ligand towards the protein's active site. Other analyses like the Electrostatic energy and Vander waals energy contribute highly towards the stable binding. Also, among the two the contribution of electrostatic interaction is more than the Vander waals contribution.

Discussion
Compound 6acetyl β-d mannose has exhibited potential docking against the target protein with binding energy -4.29 kcal/mol. The compound is also reported from extracts of Aspergillus fumigatus and is also proved potent against various microorganisms (Al-Jassaini et al, 2016).
The compound 2 Furancarboxyldehyde-5-methyl is also found in Citrum annum, brown algae and other plant sources but is not reported from fungi. Hence it can be considered that alteration in supplement media with Lactobacillus has resulted in extraction of production of some compounds that were previously not reported from fungi (source PubChem database). Similarly, the compound Tetracetyl-d-xylonic nitrile have also exhibited considerable docking against the target protein but has not yet been isolated from any fungi.
The present structure based screening and molecular docking of fungal secondary metabolites have proved them to be effective inhibitors of proteins causing COVID-19. Some of the metabolites can also be isolated from various other sources and few have been isolated only in this study. This con rms that altering the growth conditions can result in production of some bizzare compounds. These compounds also showed a stable conformation during the MD simulations run. The values obtained were better than that obtained through the native N3 ligand complex as observed by Das et al (Das et al, 2020