key: cord-0848816-e46if07y
authors: Aditya Rao, S.J.; Shetty, Nandini P.
title: Structure-based screening of natural product libraries in search of potential antiviral drug-leads as first-line treatment to COVID-19 infection
date: 2022-03-22
journal: Microb Pathog
DOI: 10.1016/j.micpath.2022.105497
sha: 702fa2c317c95267a18db4fa12146cc498171a2b
doc_id: 848816
cord_uid: e46if07y

The study focuses on identifying and screening natural products (NPs) based on their structural similarities with chemical drugs followed by their possible use in first-line treatment to COVID-19 infection. In the present study, the in-house natural product libraries, consisting of 26,311 structures, were screened against potential targets of SARS-CoV-2 based on their structural similarities with the prescribed chemical drugs. The comparison was based on molecular properties, 2 and 3-dimensional structural similarities, activity cliffs, and core fragments of NPs with chemical drugs. The screened NPs were evaluated for their therapeutic effects based on their predicted in-silico pharmacokinetic and pharmacodynamics properties, binding interactions with the appropriate targets, and structural stability of the bound complex using molecular dynamics simulations. The study yielded NPs with significant structural similarities to synthetic drugs currently used to treat COVID-19 infections. The study proposes the probable biological action of the selected NPs as Anti-retroviral protease inhibitors, RNA-dependent RNA polymerase inhibitors, and viral entry inhibitors.

Source: https://drugvirus.info/ Natural products and traditional medicines have been serving as the greatest source for modern drug discovery. Their derivatives have been recognized for many years as the source of therapeutic potential and structural diversity. There are over 200,000 compounds reported in the scientific literature. NPs are more often structurally complex, with well-organized structure and steric properties offering efficacy, efficiency, and selectivity of molecular targets [10] . However, their utilization on many health conditions is well documented; it is in the hands of existing traditional practitioners and herbologists to define their applications for newly emerging diseases. The biological activities reported from different plant extracts often narrow down to pre-reported molecules rather than novel compounds [11] , creating a real challenge to medicinal chemists. In this avenue, the search for new therapeutic molecules is the need of the hour to combat new health challenges.

The biological activity of any molecule is attributed to its structural arrangements. If two molecules have a similar structure, they will probably have a similar biological effect [12] [13] [14] ( Figure 3 ). The computational chemists successfully exploit this principle for the construction of diverse compound libraries and select compounds for high-throughput screening experiments [12] . Computational advancements with the introduction of parallel processing clusters, cloud-based computing, and highly effective graphical processing units (GPUs), tremendous success has been achieved in the field of modern drug discovery [15] .

The knowledge of natural products and ligands, earlier used as starting points for drug discovery, has greatly influenced computational biology techniques [16] . These advancements have been speeded up by the creation of new algorithms for more accurate predictions, simulations, and interpretations [17] [18] [19] [20] [21] [22] . The extensive molecular dynamics (MD) simulations can provide insights into the host-virus interactions, disease spread, and J o u r n a l P r e -p r o o f possible regulative/preventive mechanisms [23] . In this avenue, the present study proceeds to exploit these developments to identify natural products as potential drug leads targeting some of the most widely used antiviral drugs currently being used against SARS-CoV-2. The identified molecules envisage to be potential drug leads and, with critical screening and trials, could be used in the first-line treatment for COVID-19 infections.

Material and Method:

An in-house natural product library consisting of 26,311 natural product structures was constructed using natural products information from different databases like Dr. Duke's database (https://phytochem.nal.usda.gov/phytochem/search) [24] , Phytochemical

Interactions Database (http://www.genome.jp/db/pcidb), and Natural product activity and species source database (NPASS) (http://bidd.group/NPASS/index.php) [25] . The plant's secondary metabolites are classified into various classes according to their chemical structures [26] . Their classification as phenolics, alkaloids, Flavonoids, Tannins, Coumarins, terpenes, Lignans, etc., with polyphenols further classified into flavans, flavones, and isoflavonoids are of particular interest to medicinal chemists due to their diverse therapeutic effects [27, 28] . Therefore, the polyphenols from the natural product library were further categorized as flavans (339) To find structurally similar compounds rather than compounds sharing a common substructure, core fragment-based SAR analysis was performed by considering the most central ring structure. The similarities between the fragments were assessed based on the number of fragments that both molecules have in common, divided by the number of fragments being found in any of the two structures [31] [32] [33] . The structures were further analyzed for structural scaffolds based on plane ring system to determine the substructures and to define the similarity cut-off during the structure comparison. The molecular properties, activity cliff, core fragments, and structural scaffolds were predicted using Osiris DataWarrior V.4.4.3 software [31, 34] .

b. Similarity score cut-off limit:

J o u r n a l P r e -p r o o f Natural products exist in many of their stable analogs forms in nature. Even with minor structural variations, the analogous forms of natural products can exert unique biological effects [35] . Therefore, there was a need to set an appropriate limit to filter natural products and their analogous structures. Due to their complex structures, at higher cut-off limits, such derivatives are expected to exclude, while at lower cut-off limits, the analogous structures become inclusive (Table S2) . By considering such variations, a similarity cut-off limit of 60% was fixed for the comparison [34] .

Natural products are the major source of oral drugs 'beyond Lipinski's rule of five' [36] [37] [38] .

The drug-likeness assessment, pharmacokinetic (PK), and pharmacodynamics (PD) of NPs were determined based on their molecular properties like molecular weight, cLogP, hydrogen atom donors, hydrogen atom acceptors, and rotatable hydrogen bonds. These properties are used as filtering parameters to estimate the oral bioavailability, solubility, and permeability of new drug candidates [36, 38, 39] . The natural products obtained from structural comparison were considered as hits for in-silico PK/PD assessment to analyze mutagenicity, tumorigenicity, reproductive effectiveness, irritant properties, and drug likeliness. Molecular properties were predicted using Osiris Data warrior V.4.4.3 software [31] . The admetSAR server [40] was used to predict solubility, permeability, GPCR ligand, ion channel modulator, protease inhibitor, kinase inhibitor, enzyme inhibitor, nuclear receptor inhibitor, aqueous solubility, TPSA (Topological polar surface area), blood-brain barrier penetration, human intestinal absorption, Caco-2 permeability, AMES toxicity, carcinogenicity and acute oral toxicity of the selected molecules [38, 41] .

J o u r n a l P r e -p r o o f Automated docking was performed to deduce the binding interactions of selected natural products with appropriate target proteins. Broyden-Fletcher-Goldfarb-Shanno algorithm implemented in the AutoDockVina was employed to study proper binding modes of the selected natural products in different conformations [42] . The antiviral drugs currently being prescribed for COVID-19 first-line treatment were retrieved from the drugvirus.info server, and their action mechanisms were studied using the Inxight: Drugs database (https://drugs.ncats.io/) (Table S1 ).

Based on the action mechanism of the standard drugs, HIV-1 protease I50V isolate, influenza virus hemagglutinin, SARS-CoV NSP12 polymerase, and HIV-1 protease A02 isolate were selected for the docking studies. The protein structures were retrieved from protein databank (https://www.rcsb.org/) and were prepared for docking studies. For each target, residues forming the binding site were retrieved using the PDBsum server. The antiviral drug;

Lopinavir and its related natural products were docked against anti-retroviral protease inhibitor (I50V isolate) (PDB ID 3OXV), Ritonavir and its related natural products were docked against anti-retroviral protease inhibitor (A02 isolate) (PDB ID 4NJV), Remdesivir, and its related natural products were docked against anti-retroviral protease inhibitor (PDB ID 7BV2), and Arbidol and its related natural products were docked against anti-retroviral protease inhibitor (PDB ID 5T6S). During molecular docking, the energy range was set to 3, exhaustiveness to 8, and the number of modes to 9. For the ligand molecules, all the torsions were allowed to rotate during docking. The in-silico studies were performed on a local machine equipped with AMD Ryzen 5 six-core 3.4 GHz processor, 8GB graphics, and 16 GB RAM with Microsoft Windows 10 and Ubuntu 16.04 LTS dual boot operating systems.

the structural stability of the free and bound targets was assessed using MD simulations run for a time scale of 20 ns [43, 44] by employing the GROMOS96 54a7 [45] force field J o u r n a l P r e -p r o o f implemented in the GROMACS-2018 package [46] . A periodic cubic solvated box was created around the target proteins with at least 10 Å distance from the edge of the box and solvated using the simple point charge (SPC) model [47] and neutralized using sodium or chloride ions. The energy minimization was done using the steepest descent method. The system was equilibrated with a temperature coupling at 300K using V-rescale thermostat [48] in NVT ensemble and pressure coupling at 10 5 Pa using Parrinello-Rahman barostat [49] in NPT ensemble for a period of 500 ps. Bond parameters were adjusted using the LINCS algorithm [50] , and the particle mesh Ewald method (PME) [51] was used to evaluate electrostatic interactions. A cut-off at 1.0 nm was set for the long-distance interactions as MD simulation accurately predicts properties of the system for a larger cut-off distance [52] . The final MD trajectories were prepared for a time scale of 20ns at a time step of 2fs with trajectory coordinates updated at 10ps intervals. The final trajectories were analyzed using gmx energy, gmx rms, gmx rmsf, gmx gyrate, gmx do_dssp, and gmx sasa modules of GROMACS along with interaction energies in terms of electrostatic and van der Waals energy between the ligand and the macromolecule.

For Molecular mechanics/Poisson-Boltzmann surface area (MMPBSA) calculations, trajectory files were created from the final production MD trajectory with coordinates updated every 200ps. The g_mmpbsa package was used for binding energy calculations [53] .

The g_mmpbsa package uses the following equation to calculate the binding energy of the protein-ligand complex;

The 'G' term can be further decomposed into the following components-J o u r n a l P r e -p r o o f

Where, G Complex = total free energy of the binding complex, G Protein and G Ligand = total free energies of protein and ligand, respectively. EMM = vacuum potential energy; G Solvation = free energy of solvation

Structure-based screening of Natural products:

The natural product library consisting of 26 (Table   2A) . Gastrointestinal (GI) absorption is an important parameter to screen orally administered drugs. A positive value shown in Table 2A for gastrointestinal (GI) absorption suggests a high probability of success for absorption into the intestinal tract [54] . While the blood-brain barrier (BBB) penetration indicates the potential of a drug to cross into the brain, it can bind J o u r n a l P r e -p r o o f to specific receptors and activate specific signaling pathways. Therefore, the prediction of BBB penetration is crucial in the drug development pipeline [55] . In the present study, 33 molecules were found to penetrate the human intestine barrier, 17 molecules penetrating the blood-brain barrier. None of them was the substrate for Cytochromes P450 group of isozymes that regulates drug metabolism, indicating a high possibility of their bioavailability (Table   2A ). Further, out of 35 molecules, 34 were predicted to be non-mutagenic, non-tumorigenic, and non-irritant, with 10 molecules predicted to have reproductive effects (Table 2B) 

The in-silico molecular interaction studies were used to predict the most effective natural product drug to bind to the appropriate target involved in the regulation of virus entry, replication, assembly, and release, as well as host-specific interactions. In the present study, the docking studies were carried out for synthetic antiviral agents as well as their structurally similar natural products against different targets proteins of SARS-CoV-2 to deduce the structural insight of molecular interactions. The study yielded natural products being effectively bound to their respective targets (Table 3) . The results were expressed in terms of docking energy (kcal/mol). Many selected natural products have displayed docking energies lower than their structurally similar standard drug counterparts. The natural products similar to Remdesivir interact with SARS-CoV NSP12 polymerase with docking energies comparably lower than the standard drug. The natural products tested as influenza virus hemagglutinin inhibitors are also bound to the target with docking energies lower than the standard drug arbidol. The binding interactions of natural products tested as viral protease J o u r n a l P r e -p r o o f inhibitors were compared with standard drugs Lopinavir and Ritonavir. The effectiveness of these binding of natural products with the highest interaction energy in each group was selected for protein stability assessment using molecular dynamics simulations (Figure 4 ). 

In the present study, united-atom MD simulations were performed to confirm the accuracy of binding resulting from docking studies [56] . The result of the MD simulation displayed the conformational changes acquired by different target proteins of SARS-CoV-2 upon binding and inferred the structural insight on molecular stability (figure 5).

The RMSD analysis was done to understand the deviation of Cα atoms of the protein from its backbone, and RMSF analysis was done to study the fluctuations associated with the amino acid residues of the protein during the simulation. The average RMS deviations and RMS fluctuations were calculated from the MD trajectories of natural product, and synthetic drug bound HIV-1 protease (I50V isolate), Influenza virus haemagglutinin, SARS-CoV NSP 12 polymerase, and HIV-1 protease (A02 isolate) and were compared with their respective unbound structures ( Table 4 ). The RMSD plots indicate that Remdesivir bound SARS CoV NSP 12 polymerase (Fig. 4a) and Arbidol bound Influenza virus haemagglutinin (Fig.4b) displayed lesser deviations than their structurally similar natural products, Hydroxy Manzamin A and Phellibaurin_A bound structures. However, the macromolecular structures HIV-1 protease (I50V isolate) (Fig. 4c) and HIV-1 protease (A02 isolate) (Fig.4d ) displayed lesser RMS deviations upon binding with respective natural products compared to their synthetic drug counterparts. It can also be seen from these plots that the deviations in the macromolecular structures are relatively low after 5ns in both conditions. From the RMSF plots ( fig.4e-h) , it can be inferred that, though the residues displayed minor fluctuations at certain positions, all proteins were able to retain their secondary structure's packability. This was inferred based on the Rg plots (Figure 5i-l) , where the structures were found to be very tightly packed, as the secondary structure elements like α-helix, β-sheet, and turn were have been an excellent source for modern drug research programs for a long time [58] . Their origin, availability, safety, and cost-effectiveness make them a reliable choice alongside modern medicine [59] .

With the advancements in computational techniques, new strategies have been designed to predict the interactions of potential human target proteins with specific viral strains [60] .

These models rely on the available interaction information to predict the novel host-virus interactions. These predictions have been reliable in the past in understanding the infection mechanism of SARS-CoV [61] , MERS-CoV [61] , Ebola virus [62] , and Zika virus [63] .

However, these computational methods play a significant role in modern drug research; the experimental verifications of virus-host interactions are needed to substantiate the potential interactions [64] [65] [66] . Along with this, the availability of verified interactions and relevant information is a prerequisite for computational drug discovery methods. Most of the chemical structure search and identification methods today rely on the conventional string search to modern data structure module-based approaches [67] [68] [69] [70] . The structural comparison and screening based on physiochemical properties, topological indices, molecular graphs, pharmacophore features, molecular shapes, molecular fields, and quantitative measures are expected to reduce false-positive results and yield more effective structures [13, 14] . In the current investigation, we were able to shortlist some of the natural products as potential drug- isolate and A02 isolate.

List of tables: Table 1 : Natural products structurally similar to prescribed COVID-19 drugs and their similarity score. Table 3 : Molecular interactions between the selected natural products with targets of their structurally similar chemical drugs expressed as docking energies along with their structure similarity score. The table also details the number of hydrogen bonds formed between the Natural product and the amino acid residues from the target molecule. 

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