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ORIGINAL ARTICLE
Year : 2020  |  Volume : 10  |  Issue : 4  |  Page : 194-202

Protective Effects of Flavonols From Crataegus Oxycantha Against Inflammatory Markers of Atherosclerosis: A Structure Based, Molecular Docking And Dynamics Studies


1 Drug discovery and Molecular Cardiology Lab, Department of Bioinformatics, School of Life sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India
2 Molecular Gerontology Lab, Department of Biochemistry, School of Life sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India

Date of Submission10-Jul-2020
Date of Decision18-Jul-2020
Date of Acceptance27-Jul-2020
Date of Web Publication27-Oct-2020

Correspondence Address:
Swaminathan K Jayachandran
Assistant Professor, Drug Discovery and Molecular Cardiology Lab, Department of Bioinformatics, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijnpnd.ijnpnd_73_20

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   Abstract 


Coronary atherosclerosis is a life-threatening chronic disease that occurs in the arteries of the heart. The disease builds-up by various processes in a series that are hyperlipidemia, endothelial dysfunction, inflammation, LDL deposition and modification to OxLDL, macrophage infiltration all together known as plaque deposition, followed by foam cell formation, leading to plaque rupture and finally thrombosis which results in a complete blockage of blood flow. Among all the above, inflammation is the key event in the pathogenesis of atherosclerosis. TNF-α and IL-6, the known inflammatory markers are reported to be the key initiators and developers of plaque. Therefore this study aimed to find out the best blockers for these inflammatory markers by in-silico molecular docking and dynamic studies, using Maestro (Schrödinger). The blockers used in this study are the active secondary metabolites from Crataegus oxycantha, a well-known cardioprotective plant with numerous medicinal properties. The overall in-silico outcomes reported that a secondary metabolite Epicatechin gallate can be one of the best agents to minimize inflammation by inhibiting these inflammatory markers. Therefore these results should be further taken for experimental studies to prove the efficacy of Epicatechin gallate as a drug that can minimize inflammation and reduce Myocardial infarction complications.

Keywords: Flavonols, inflammatory markers, ischemic heart disease, plaque


How to cite this article:
Ravindran AS, Anusuyadevi M, Jayachandran SK. Protective Effects of Flavonols From Crataegus Oxycantha Against Inflammatory Markers of Atherosclerosis: A Structure Based, Molecular Docking And Dynamics Studies. Int J Nutr Pharmacol Neurol Dis 2020;10:194-202

How to cite this URL:
Ravindran AS, Anusuyadevi M, Jayachandran SK. Protective Effects of Flavonols From Crataegus Oxycantha Against Inflammatory Markers of Atherosclerosis: A Structure Based, Molecular Docking And Dynamics Studies. Int J Nutr Pharmacol Neurol Dis [serial online] 2020 [cited 2020 Dec 3];10:194-202. Available from: https://www.ijnpnd.com/text.asp?2020/10/4/194/299268




   Introduction Top


Coronary atherosclerosis is a chronic disease affecting arteries that supply blood to the heart.[1],[2] Development of atherosclerosis is anatomically facilitated by the branches and curvatures of the coronary arteries, in which the deposition of lipoproteins, cholesterol, calcium and macrophages initiate plaque formation.[3],[4],[5] Hyperlipidemia is one of the major causes for the development of coronary atherosclerosis, which in turn leads to the occurrence of Ischemic heart disease (IHD). Atherosclerosis based IHD is the leading global cause of death, accounting for >9 million deaths in 2016 (WHO report).[6],[7]

Until now, atherosclerosis was not clearly defined under any of the following specific categories, whether it is related to vascular, inflammatory, immune or cardiac complications. Even though several reports strongly suggest that it is a cardiac complication, the plaque formation begins with endothelial cell (EC) dysfunction mediated inflammation.[6],[8] Nitric oxide (NO), a well-known vasodilator, produced by the EC, is reduced at a certain point of time during the process of aging. This impairs the flexible nature of the EC that leads to increased permeability and irregular arrangement.[9] Further, the low-density lipoprotein (LDL) flows from the extracellular fluid to the sub-endothelial space, where the positively charged amino acids of the ApoB100 binds with a negatively charged proteoglycan extracellular matrix (ECM), where the LDL becomes oxidized to form OxLDL (oxidized low-density lipoprotein).[6] Endothelial dysfunction and the formation of OxLDL are the classical indicators of initiation of foam cell formation and plaque development, which may lead to the prognosis of atherosclerosis.[10] Despite several factors discussed above, a major stimulus reported is the excessive production and release of inflammatory markers, especially Tumor Necrosis Factor-Alpha (TNF-α) and Interleukin 6 (IL-6), which are believed to be the key initiators of endothelial dysfunction [Figure 1].[11] 11.2 ± 7.31pg/mL and 4.2 ± 5.86pg/mL are the reported reference values for TNF-α and IL-6 respectively.[12] However, these values increase several folds during any kind of viral and bacterial infections and could be brought back to normal levels upon treatments. Whereas in the condition like atherosclerosis, the levels of TNF-α and IL-6 were constantly up-regulated, which results in promoting plaque formation.[13] Several reports suggest that initiation of EC dysfunction is triggered by TNF-α, which reduces the NO production and release by EC through several mechanisms.[14] Elevated levels of TNF-α activates IL-6 which in turn activates TNF-α through feedback activation, and hence the bioavailability of NO would be constantly reduced.[15] Several research findings suggest that the therapeutic control of TNF-α and IL-6 resulted in the reduction of inflammation at a significant level.[16],[17]
Figure 1 Schematic representation of atherosclerosis events

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Several anti-inflammatory drugs are available in the market to reduce inflammatory cytokines. But these medications are not recommended for atherosclerosis. Statins and aspirin are the available major primary and secondary source of medication for atherosclerosis. The drugs work better in reducing the levels of LDL, increasing the levels of High density lipoprotein, removing the blood clot and also in decreasing inflammation. These medications cannot be a complete and permanent cure for atherosclerosis; rather it slightly lowers the level of plaque. Moreover, these drugs are widely reported for its adverse side effects.[18],[19],[20] Despite the use of these drugs, atherosclerosis mediated myocardial infarction still remains the leading cause of mortality worldwide, which necessitates the need for novel anti-atherosclerotic therapeutics.

Plant-based medications have become a recent trend worldwide, because of its numerous beneficial effects.[21],[22] One such herbal preparation that has a profound history as one of the best known cardio tonic is Crataegus oxycantha (COC) which was reported for the presence of several medicinally important secondary metabolites. Many research groups, including ours, have identified the beneficial effect of COC against atherosclerotic progression and prevention of MI complications.[23],[24] Interestingly, in the in-vivo studies using high fat diet induced atherosclerotic plaque development and formation of MI in guinea pig as a model system, it was observed that COC extract could able to significantly reduce several inflammatory markers (data not shown). Even though there are several pieces of evidence made available to support the anti-inflammatory property of COC, there is no report available till-date, to understand the mechanism of action of secondary metabolites present in the COC against inflammatory cytokines. While using the crude extract of COC as an anti-inflammatory drug, it will be difficult to decipher the specific active molecule and its mechanism that is involved in reducing inflammation. Therefore by identifying the active molecule from the COC, their mechanism of action can be identified and also there is a potential for using the molecule as a pure anti-inflammatory drug. Hence, the present study intends to investigate the effect of major bioactive compounds of COC, against the key inflammatory (TNF-α, IL-6) markers through in-silico analysis.

Molecular docking is widely used in-silico based approach to identify the best interacting small organic compound with target proteins. The small organic molecule, further, may help in the development of a new drug.[25] The in-silico resulted compounds may or may not work effectively in the in-vitro and in-vivo system, but when a compound not capable of providing the best result in the in-silico, absolutely it will not work successfully in in-vitro and in-vivo. Therefore the in-silico based experiments are more valuable in selecting a better drug-like molecule without much expense and time for any further analysis. In the present study, four leading molecules from COC extract were studied to understand their effect on key inflammatory proteins involved in atherosclerosis.


   Methodology Top


Sequence and structural analysis

The structure-based drug discovery approach completely relies on the three-dimensional structure of the target proteins involved in a particular biological activity. Since the study focuses on the inflammation that occurs during the event of atherosclerosis, Tumor necrosis factor-alpha (TNF-α) and Interleukin 6 (IL-6) were used as the target protein. The sequence and structural information of the targets are retrieved from UNIPROT (https://www.uniprot.org/) and PDB (https://www.rcsb.org/pdb/) database.

Selection of compounds

The major bioactive compounds of COC were obtained through literature search and are used as principal drug components against the inflammatory and apoptotic targets. Five compounds namely Epicatechin, Epicatechin gallate, Rutin, Vitexin (Flavonoid derivatives) and Tyramine (amine derivative) were used for the present study.[24],[26] The structures of these compounds were retrieved from the PubChem Compound (https://pubchem.ncbi.nlm.nih.gov/) database.

Protein preparation and active site prediction

The 3D structure of target proteins obtained from PDB database was submitted to Protein Preparation Wizard of Maestro of Schrodinger (Schrödinger, LLC, New York, NY, 2015) for their preliminary stabilization. This wizard initially removes all the water molecules and adds the missing hydrogen bonds to the proteins. In the next step, optimization and energy minimization was done. After energy minimization, information about the binding region of the target was analyzed. IL-6 has active site information from their solved structure. For TNF-α, the binding site information was obtained through the SiteMap protocol of Maestro (Schrödinger, LLC, New York, NY, 2015). This predicts one or more binding regions for the proteins. This SiteMap algorithm predicts the binding site information with a high degree of confidence and also analyzes the druggability nature of the site. The scoring function (site score) of SiteMap also ranks the possible binding sites more accurately. These binding site results are essential for the prediction of protein-ligand interaction.

Ligand preparation and ADME toxicity prediction

The principal compounds of COC retrieved are imported to the Maestro window and prepared using the LigPrep module of Maestro (Schrödinger, LLC, New York, NY, 2015). The ligands are converted to 3D models and then energy minimized with the OPLS-2005 force field. About 40% of the drugs fail in their clinical trials due to their toxicity.[27] This failure in the final stage of evaluation will ultimately push back the drug development process. Analyzing the ADME (Absorption, distribution, metabolism, excretion) properties helps to eliminate the compounds that fail to be a drug. This prediction was achieved through QikProp of Maestro (Schrödinger, LLC, New York, NY, 2015), which predicts the wide variety of pharmacologically relevant properties of the drug.

Molecular docking simulation

Predictions of binding efficiency of COC compounds in the active site of proteins were achieved through Glide XP (extra precision) docking (Schrödinger, LLC, New York, NY, 2015). The prepared target and ligand were taken as input for glide. The docking area in the protein was defined by a grid box generated using Receptor Grid Generation protocol (Schrödinger, LLC, New York, NY, 2015), which provides a more accurate scoring for the ligand binding. The molecular docking simulations are carried out with default parameters. The simulation results were validated based on the docking score, glide energy, and the number of hydrogen bond (HB) interactions between the target and the ligand.

Molecular dynamic simulation

The conformational stability of the protein-ligand complex in motion was obtained through molecular dynamic simulation using the Desmond module (Schrödinger, LLC, New York, NY, 2015). The protein-ligand complex was bound by a predefined TIP4P water model, by an orthorhombic box with 10Å distance and the box volume was minimized. The complete charge of the system was neutralized by adding Na+ and Cl- ions. OPLS 2005 force field was used for energy calculation. Temperature and pressure were kept default at 300 K and 1.01325 bar using the Nose-Hoover thermostat algorithm and Martyna-Tobias-Klein Barostat algorithm. The complex structure dynamic simulation for best complex from docking simulation was carried out with NPT ensemble, for 20ns and the trajectory was set at an interval of 1.2ps.


   Results Top


Sequence and structural information

The basic information and crystal structures of the target proteins TNF-α and IL-6 were retrieved from UNIPROT and PDB databases with ID P01375 and 1A8M (TNF-α), P05231 and 1ALU (IL-6). This crystal structure information is vital for the drug discovery process in assessing the activity of a protein as a target for a drug. The active site region of TNF-α was specified in [Figure 2].
Figure 2 Active site region of TNF-α predicted using SiteMap

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Compound selection and ADME prediction

The five bioactive compounds of COC obtained were taken for the pharmacological analysis which was done using QikProp. This module will analyse various pharmacological properties of the compounds like Molecular weight, Rule of five (number of violations of Lipinski’s rule of five), Rule of three (Number of violations of Jorgensen’s rule of three), donor hydrogen bond (Estimated number of hydrogen bonds that would be donated by the solute to water molecules in an aqueous solution), acceptor hydrogen bonds (Estimated number of hydrogen bonds that would be accepted by the solute from water molecules in an aqueous solution), human oral absorption (Predicted qualitative human oral absorption) and stars (Number of property or descriptor values that fall outside the 95% range of similar values for known drugs). The values of these properties were presented in [Table 1]. The predicted pharmacological properties of compound Rutin is not in the acceptable range of ADME prediction, hence the compound was excluded for further studies. The remaining four compounds were in the acceptable range and are taken for further docking analysis.
Table 1 QikProp results of the predicted toxicity of COC compounds

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Molecular docking simulation

Molecular docking is a structure-based tool that predicts the intermolecular contact between any two molecules. In the field of drug discovery, molecular docking remains as an important tool in identifying the interaction between a ligand (drug) and a protein (target). By this way this tool will ease the work and save the time of the researchers by eliminating the in-active complexes. Molecular docking analysis of four compounds (Epicatechin, Epicatechin gallate, Tyramine and Vitexin) with target proteins (TNF-α, IL-6) performed were recorded and presented in [Table 2]. The results obtained were validated based on the docking score, glide energy and hydrogen bond interaction. All the compounds have interactions with all the four targets. Of this Epicatechin gallate showed best binding competence for all the targets with docking score −7.272 kcal/mol, glide energy −68.147 kcal/mol and 7 HB interactions for TNF-α, docking score −7.985 kcal/mol, glide energy −67.762 kcal/mol and 7 HB interactions for IL-6. The docked image of Epicatechin gallate with both the target proteins are given in [Figure 3]A and 3B. The docking simulation results clearly predict that Epicatechin gallate has a potential application towards the event of inflammation that occurs during atherosclerosis. Even though vitexin has showed better binding efficiency than Epicatechin gallate in TNF-α its binding efficiency with IL-6 is very poor, but Epicatechin gallatae has the best binding efficiency with both the targets. Therefore while considering inflammatory pathway, Epicatechin gallate can act as an effective anti-inflammatory agent. The docking results were also compared with the existing anti-inflammatory drug Levocetirizine, and its results are also shown in [Table 2]. The histogram plot showed in [Figure 4] represents the difference in the docking score between Levocetirizine and Epicatechin gallate.
Table 2 Docking score, Glide energy, interacting residues and number of hydrogen bonds of the docked molecules

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Figure 3 2D ligplot image showing the binding interactions of (A) TNF-α – Epicatechin gallate and (B) IL-6 – Epicatechin gallate

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Figure 4 The above histogram plot shows the difference in the docking score between the standard drug (Levocetirizine) and the test compound (Epicatechin gallate) against the major anti-inflammatory markers TNF-α and IL-6

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Molecular dynamic simulation:

Molecular dynamics simulation method further supports molecular docking by verifying the stability of the interacted complex, by analyzing the physical movement of the molecules for a particular time period. The complex dynamic simulation was done for TNF-α − Epicatechin gallate and IL-6–Epicatechin gallate. The complex was immersed in a TIP4P water solvent surrounded by an orthorhombic box. The Protein-ligand RMSD (root mean square deviation) plot was given in the [Figure 5]A&B, the results showed that TNF-α − Epicatechin gallate and IL-6–Epicatechin gallate complexes were stable around 10 to 15 ns. The histogram chart and 2D interaction poses are also given in the [Figure 6]A-D. The interacting residues are similar to the docking results.
Figure 5 RMSD plot of Protein-ligand Complex. (A) TNF-α – Epicatechin gallate, (B) IL-6 – Epicatechin gallate

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Figure 6 Histogram and percentage of interaction in molecular dynamics simulation of (A&B) TNF-α – Epicatechin gallate, (C&D) IL-6 – Epicatechin gallate

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   Discussion Top


The Present study fully focused on the inflammatory response that occurs during atherosclerosis induced IHD. In conditions like IHD, the myocardium becomes more vulnerable to this inflammatory sequence resulting in cardiac dysfunction and death.[28] Considering the importance of inflammatory markers as therapeutic targets in preventing Myocardial infarction (MI) injury-induced progression, this study focused on preventing the activation of key inflammatory signals. Based on various literature evidence,[29],[30], the key atherosclerotic initiator viz., TNF-α and IL-6 were chosen as therapeutic targets to extend in-silico approach against selected secondary metabolites present in the COC.

As an insight into these targets, it was reported that TNF-α as the key inflammatory cytokine produced by many cells including, immune cells, epithelial cells, endothelial cells, smooth muscle cells, etc.[31] It is one of the tumor necrosis factor super family proteins (TNFSP), largely involved in the T cell activation, inflammation, differentiation and apoptosis. The TNF-α binds with its receptor (Tumor necrosis factor receptor 2) TNFR2, leads to TCR-mediated T-cell activation. This, in turn, activates many inflammatory signals including NF-kB, IL-2 and IL-6.[32],[33] It was confirmed that TNF-α and its receptors are produced in large amounts during MI and also there are several evidences that cardiocytes itself will produce and release TNF-α upon abnormal conditions.[34] Activated TNF-α alters the endothelial and vascular smooth muscle cell (VSMC) functions by reducing the availability of NO.[35] Endothelial and VSMC abnormalities are the initial steps of recruitment of Macrophages to the site of foam cell formation during the atherosclerosis development.[36]

Upon prolonged inflammation, TNF-α found to activate IL-6, another important inflammatory cytokine involved in the progression of MI along with several other pro-inflammatory proteins. IL-6 leads to the activation and aggregation of platelets to the region of inflammation, which leads to clot formation and occlusion by thrombosis.[37] From the initial to the final stage of plaque development, the circulation of these inflammatory cytokines, around the regions of inflammation is well reported.[38] The expression levels of these inflammatory markers and prognosis of MI are directly proportional. These increased inflammatory markers with some abnormal conditions like hypoxia, activates the death signals through caspase activation, resulting in programmed cell death.[39] A number of experimental studies reported that inhibition of TNF-α and IL-6 could reduce the complication of inflammatory disorders like Rheumatoid arthritis.[40],[41] In the present study, the same was investigated using plant-derived secondary metabolites in an in-silico approach.

Docking between key active constituent of COC such as Epicatechin gallate to that of inflammatory markers was performed and the results revealed that Epicatechin gallate of COC possess the best binding efficiency with both targets. We also compared our docking results with levocetirizine, a known anti-inflammatory drug. Levocetirizine showed poor binding efficiency for TNF-α (−2.384 kcal/mol docking score, −36.795 kcal/mol glide energy and two hydrogen bond interaction) and IL-6 (−4.642 kcal/mol docking score, −49.098 kcal/mol glide energy and two hydrogen bond interaction) compared to Epicatechin gallate, which shows that Epicatechin gallate can be a more effective anti-inflammatory agent compared to levocetirizine.

Further, the docked complexes were brought in for Molecular dynamic simulation studies to confirm the stability of the protein-ligand complex and the results are presented in [Figure 5]. Even though the TNF-α complex was not stable in the initial stages, it becomes strongly associated after 8 nanoseconds (NS) till 20th NS, supporting the hypothesis that it could form the stable complex with Epicatechin gallate. Whereas in IL-6 the complex is stable between 10 to 14 NS. Since the IL-6 protein was so complex, the ligand might not have exhibited a stable complex as compared to TNF-α. As supported by several research evidences, inhibition of these two inflammatory markers may provide a strong barrier against the development of atherosclerotic plaque. Based on the results obtained from docking and molecular dynamic studies, it is clearly evident that Epicatechin gallate is capable of inhibiting TNF-α and IL-6. Future studies are to focus the same finding from in-silico approach to the in-vivo system that mimics MI.


   Conclusion Top


From the docking and dynamics prediction of analysis of flavonols from COC clearly revealed the potential benefits of Epicatechin gallate against the inflammatory cytokines. This study provides initial clues in understanding the intricate complex behind myocardial infarction, therefore further investigation of these results in in-vitro and in-vivo experimental studies may lead to ascertain the effect of Epicatechin gallate as a drug to reduce MI complications, thereby potentially translating it to a therapeutic approach.

Financial support and sponsorship

We acknowledge the financial support from DST-SERB under grant number SB/EMEQ-175/2014 and software support from DST-PURSE under grant number SR/FT/LS-113/2009.

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
 
 
    Tables

  [Table 1], [Table 2]



 

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