Exploring active compounds of kelor ( Moringa oleifera lam. ) leaves as an alternative medicine to improve immunity in facing covid-19 via in silico study

SARS-CoV-2 is a new strain of coronavirus (CoV) that was identified in Wuhan in 2019. This virus is known to have the ability to reduce human immunity. Kelor ( Moringa oleifera ) is a potential natural resource in Indonesia, which is very abundant and contains several metabolic compounds such as phenolics, flavonoids, saponins, cytokines, and caffeoylquinic acid, which was reported to show antioxidants, antibacterial and antiviral. This study aims to predict the biological activity, physicochemical properties, toxicity, and affinity-interactions of the active compounds of M. oleifera leave. The active compounds of M. oleifera were obtained from the KNApSAcK and PubChem. Analysis of the bioactivity of the compounds using the Way2Drug Pass Online. Analysis of drug-likeness and toxicity using the Lipinski web server and pkCSM. Docking is done using Autodock vina software to analyze the interaction of the compounds with M pro . The results indicate that the compound astragalin is the compound with the highest affinity value, namely -8.7 (kcal/mol), compared to lopinavir as a control compound with an affinity value -6.6 (kcal/mol). The types of bonds in astragalin compounds are hydrogen bonds with amino acids Glutamine 127 and Arginine 298. From these results, it is predicted that astragalin compounds have the highest potential as alternative drugs to increase body immunity against the COVID-19.


Introduction
COVID-19 is a new viral infection first reported in China in late December 2019, causing global health problems. WHO publicly declared the SARS-CoV-2 outbreak a pandemic on March 11, 2020 1 . The disease caused by SARS-CoV-2 is called COVID-19. Coronaviruses infect humans and other animals and cause various highly prevalent and severe diseases, including severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS). The SARS-CoV-2 genome comprises about 30,000 nucleotides: the replicase gene of SARS-CoV-2 encodes two overlapping polyproteins pp1a and pp1ab that are required for viral replication and transcription. The functional polypeptides are released from the polyproteins by extensive proteolytic processing, predominantly by the 33.8-kDa M pro (also known as 3Clike protease). M pro digests the polyprotein at least 11 conserved sites, starting with the autolytic cleavage from pp1a and pp1ab. The functional importance of M pro in the viral life cycle, combined with the absence of closely related homologs in humans, identify M pro as an attractive target for the design of antiviral drugs. To facilitate the rapid discovery of antiviral compounds with clinical potential, we developed a strategy that combines structure-assisted drug design, virtual drug screening, and high-throughput screening to repurpose existing drugs to target SARS-CoV-2 M pro . This program focused on identifying drug leads that target the main protease (M pro ) of SARS-CoV-2: M pro is a key enzyme of coronaviruses 2 , which can spread from person to person. There are no specific antiviral drugs approved for the treatment of COVID-19. Currently, several clinical trials are being conducted to identify the drug. In this scenario, there is a need to identify new medicinal lead compounds to treat COVID-19 3 .
Plants from genus Moringa were reported for many activities such as circulatory stimulant, antitumor, antipyretic, antiepileptic, anti-inflammatory, antiulcer, diuretic, antihypertensive, lowers cholesterol, antioxidants, antidiabetic, antibacterial, antifungal, and antiviral. The combination of the use of active components can contribute optimally to prevention to build the body's immune system and affect the treatment of certain diseases 4 . M. oleifera possesses remarkable inhibitory activities against viruses, such as HIV 5 , HSV 6 , HBV 7 , FMDV 8 and NDV 9 and has a role as an immunostimulant because it can increase macrophages activity 10 .
The explanation regarding the content of M. oleifera compounds which are very good for health and the results of phytochemical screening show that M. oleifera leaves have a protective effect against many infectious diseases (bacteria and viruses). Therefore, the researchers aimed to explore the active compound of M. oleifera as an alternative medicine to increase immunity against COVID-19 in silico.

Data collection of M. oleifera bioactive compounds
Information about bioactive compounds of M. oleifera was obtained in two ways. The first is through the KNApSAcK webserver (http://www.knapsackfamily.com/KNApSAcK/) and the second form published research journal references. Bioactive compounds were downloaded from PubChem database (https://pubchem.ncbi.nlm.nih.gov/).

Bioactivity prediction
Bioactivity prediction of each compound was searched through the Way2Drug PASS Online web server (http://www.pharmaexpert.ru/passonline/) using SMILE code of each compound. The Pa (Probability Activity) value must be above 0.3, while the Pi (Probability Inhibition) value must not exceed the Pa value. The bioactivity taken is the potential of the compound as an antiviral. If Pa >0.7, the substance is very likely to exhibit the activity in the experiment, but the chance of the substance is the analog of a known pharmaceutical agent is also high. If 0.5<Pa<0.7, the substance is likely to exhibit the activity in an experiment, but the probability is less, and the substance is unlike known pharmaceutical agents. If Pa <0.5, the substance is unlikely to exhibit the activity in the experiment. However, if the presence of this activity is confirmed in the experiment, the substance might be a new chemical entity 11 .

Lipinski test and toxicity
The Lipinski test is obtained through the Lipinski webserver (http://www.scfbioiitd.res.in/software/drugdesign/lipinski.jsp) by entering the target compound file into it in pdb form. The parameters include molecular mass, hydrogen bonding, hydrogen bond acceptors, lipophilicity, and molar resistance. Both simple and complex filters have a role in combinatorial library design. Simple properties, for example, privileged building blocks and counting of structural properties (e.g. number of H-bond parameters) to complex calculations (e.g. regression or neural network-based models) explain the relationship of structural features to ADME properties. Drugs must contain adequate functionality to achieve acceptable receptor interactions. A single filter for under functionalization separates drug-like from non-drug-like compounds. Using retrospective analyses of known drugs, including simple property counting schemes, machine learning methods, regression models, and clustering methods, have all been employed to distinguish between drugs and non-drugs 12 . Meanwhile, the toxicity test is obtained through the pkCSM webserver (http://biosig.unimelb.edu.au/pkcsm/prediction), a novel method for predicting and optimizing small-molecule pharmacokinetic and toxicity properties which rely on distance-based graph signatures. We adapted the Cutoff Scanning concept to represent small molecule structure and chemistry (expressed as atomic pharmacophores−node labels) in order to represent and predict their pharmacokinetic and toxicity properties, building 30 predictors divided into five major classes: absorption (seven predictors), distribution (four predictors), metabolism (seven predictors), excretion (two predictors), and toxicity (10 predictors) by entering the SMILE code or compound .ile in the form of pdb. The parameters include the LD50 value test 13 .

Molecular docking
The target protein in this study is M pro SARS-CoV-2 obtained from NCBI database (https://www.ncbi.nlm.nih.gov/). Three-dimensional structure of M pro (PDB ID: 7BQY) obtained from RCSB PDB database (https://www.rcsb.org/). Three-dimensional structure of bioactive compounds obtained from PubChem database. Proteins are prepared by removing contaminant molecules. Bioactive compounds are prepared by minimizing conformational energy. The purpose of molecular docking is to predict the interactions between proteins and ligands so that the effect of ligands on protein can be predicted. Docking between M pro and bioactive compounds of M. oleifera performed using AutoDock Vina 14 integrated into PyRx. Visualization of docking results was conducted using the user-sponsored, open-source molecular visualization system PyMol 2.3.4.0 (Python). PyMOL supports most of the common representations for macromolecular structures: wire bonds, cylinders, spheres, ball-and-stick, dot surfaces, solid surfaces, wire mesh surfaces, backbone ribbons, and cartoon ribbons which are comparable to those generated by Molscript 15 .
As a scientific discipline, structural biology drives the need for interactive molecular visualization and has dramatically developed over the last decades. Currently, small molecules with only thousands of atoms or short molecular dynamics simulations with only thousands of frames are rarely interesting for researchers anymore. The analysis focuses on very long simulations of structural models, where several molecules can mutually interact with a macromolecular structure. A ligand, an interacting chemical compound, is often stimulated to interact with the studied macromolecule. Furthermore, the solvent molecules are also present in the simulation, raising new challenges for the visualization. Nowadays, it is no longer an issue to render several thousands of atoms interactively, even if they change over time. Now the challenge is to understand the dynamic behavior captured in several millions of timesteps. Direct playback of such a long molecular dynamics sequence is unsuitable for a visual analysis and more advanced techniques are required that convey several scales of dynamics 16 . Chemical compounds with the lowest binding energy were analyzed for the position of molecular interactions and the types of bonds formed at webserver protein plus (https://proteins.plus/) 17 . The LD50 is used to assess the potential short-term toxicity of a substance (Table 2).

Discussion
From the KNApSAcK analysis results, it was found that the active compounds of M. oleifera leaves were Rhamnetin and Glucoputranjivin. In comparison, from research journals 18 it was stated that M. oleifera leaves contain Apigenin, Astragalin, Aurantiamide Acetate, Beta-Sitosterol, Chlorogenic Acid, Chrysin, Dibutyl Phtalate, Ellagic Acid, Ferulic Acid, Gallic Acid, Kaempferol, Linalool Oxide, Myricetin, Niaziminin, Quercetin, and Vanillin. Furthermore, to determine the physicochemical properties of each compound, the Lipinski test was conducted. The Lipinski Rule helps in distinguishing between drug-like and non-drug-like molecules. The test predicts a high likelihood of success or failure due to drug resemblance for molecules obeying 2 or more rules. The 5 Lipinski rules include molecular mass less than 500 Daltons, high lipophilicity (expressed as LogP less than 5), less than 5 hydrogen bond donors, less than 10 hydrogen bond acceptors and the molar resistance must be between 40-130 19 .
From the PASS Online, it was found that all compounds have bioactivity as antiviral with a Pa value above 0.3. The Pa score of a compound must be higher than Pi because it will clarify the positive prediction of the potential of the query compound ( Table 1). The results of the analysis show that compounds that have a probability activity (Pa) score >0.3 are less close to the fact because their potential is computationally proven, but this score suitable for screening, whereas if Pa >0.7 is seen as a positive predictor because its potential has been proven through previous research 20 .
LD50 is defined as a statistical sign when giving a substance as a single dose that can cause the death of 50% of the tested animals. The classification of the toxicity class of compounds is based on the Globally Harmonized System (GHS), the toxicity class includes class I: fatal if ingested (LD50<5 mg/kg), class II: fatal if swallowed (5<LD50<50 mg/kg), class III: toxic if swallowed (50<LD50<300), class IV: harmful if swallowed (300<LD50<2000), Class V: may be harmful if swallowed (2000<LD50<5000), Class VI: nontoxic (LD50<5000) 21 .
Molecular docking is the process of binding a ligand with a target protein and determining the binding energy formed in the stable molecular complex 22,23 . Ligands with lowest binding energy can affect the biological activity of a target protein. The lowest binding energy allows molecular complex formation in constant temperature and pressure 24 . Among the bioactive compounds of M. oleifera leaves, astragalin is the compound with the highest affinity value, which is -8.7 (kcal/mol), which is greater than the affinity value of Lopinavir as control compound with an affinity value of -6.6 (kcal/mol) ( Table 3).
The position of astragalin interaction on the amino acid residues Arg298 and Gln127 with hydrogen bonds when Astragalin forms a molecular complex with M pro . (Figure 1) The leaves of M. oleifera are predicted to act as antiviral agents because they have the bioactive compound astragalin capable of reaching targets by passing through cell membranes because they have high bioavailability refer to Lipinski Five Rule. In addition, astragalin has a low level of toxicity compared to other compounds. The binding energy produced by astragalin is more negative when it binds to the specific Mpro domain, allowing the initiation of a direct inhibitory response to Mpro activity in SARS-CoV-2.

Conclusions
Our research demonstrated that all of these compounds have physicochemical properties that have met at least 2 out of 5 Lipinski test rules, also have biological activity that has potential as antiviral, aurantiamide acetate, chlorogenic acid, dibutyl phthalate, glucoputranjivin, linalool oxide, and vanillin have class IV toxicity properties. It means dangerous if swallowed. Apigenin, astragalin, beta-sitosterol, chrysin, ellagic acid, ferulic acid, gallic acid, kaempferol, myricetin, niaziminin, quercetin, and rhamnetin have Class V toxicity properties, which means they may be harmful if ingested. The docking results show that Astragalin CID 5282102 compound received the highest affinity value of -8.7 kcal/mol. From these results, it was predicted that astragalin compounds had the highest potential as an alternative medicine to increase body immunity against the SARS-CoV-2.