Discovery of Novel Flavonoid Analogues as Angiotensin Converting Enzyme Inhibitors based on Pharmacophore Modelling and Virtual Screening Techniques

 

Kuppusamy Ashok Kumar¹, Puliyath Jagannath¹*, Mariadas Francis Saleshier²

¹Department of Pharmacology, College of Pharmacy, Sri Ramakrishna Institute of Paramedical Sciences, Coimbatore, Tamil Nadu, India - 641044

(Affiliated to The Tamil Nadu Dr. M.G.R.Medical University, Chennai -600 032)

²Department of Pharmaceutical Chemistry, College of Pharmacy, Sri Ramakrishna Institute of Paramedical Sciences, Coimbatore, Tamil Nadu, India - 641 044

(Affiliated to The Tamil Nadu Dr. M.G.R. Medical University, Chennai -600 032)

 

ABSTRACT:

New drugs for the inhibition of the angiotensin converting enzyme (ACE) are in development and they have to be screened before being considered for preclinical and clinical evaluation. The current study deals with the evaluation of ACE inhibitory activity of flavonoid compounds using in silico docking studies. In this perspective, 18 flavonoids derivatives were selected. The drug likeness, bioactivity score and toxicity profile of the flavonoid compounds were determined using softwares like molinspiration and PreADMET. Captopril, a known ACE inhibitor was used as the standard. All the flavonoid compounds were docked with ACE using the software AutoDock 4.2. Docking results showed that all the selected flavonoids showed binding energy ranging between -10.97  kcal/mol to -9.39 kcal/mol when compared with that of the standard -3.79 kcal/mol). Intermolecular    energy -12.53 (FA8)  kcal/mol to -11.2 (FA11) kcal/mol and the values for captopril was -4.98 kcal/mol. The exhibited  inhibition constant by the flavonoid compound for the ACE was found with varying range of  9.07 nM (FA14) to 208.98 nm (FA11). All the selected compounds had lesser inhibition constant when compared to the standard captopril (-1.67 mM). FA14 contributed better ACE binding energy because of its structural parameters and it can be synthesized and evaluated for its in vitro and in vivo potential and can be used as an effective ACE inhibitor for the prevention and protection against cardiovascular diseases.

 

 

INTRODUCTION:

The power of computer aided rational drug design is coming to fruition: computationally designed pharmaceuticals targeted against various proteins are in advanced stages of clinical testing¹. AutoDock predicts optimal modes of interaction of a flexible small molecule with a rigid macromolecular target². The medicinal chemist, design a new molecule by molecular or chemical manipulation of the lead moiety.

 

 

This generates a compound with better activity and  minimum steric effect^3,4. In identification of  new chemical entitie, the assessment, improvement and extension of the lead is a very important step⁵. Lipinski’s rule of five or Veber’s parameters describes the criteria for a compound to be orally active. This help the pharmaceutical scientists to select the best lead compound for development. These parameters also helps to reject the compounds with a reduced probability of success^6,7.

 

In silico molecular modeling also termed as bioinformatics and cheminformatics are quite useful for the development of new chemical entitie. These molecular modeling methods are extremely fast and economic. Such methods can also be employed for such a compound which is not physically available⁸. The major objective of drug design involves to improve efficacy and potency of an existing lead compound. The scientists also concentrate to reduce or eliminate untoward adverse reactions. During the initial stages of drug discovery, the number of compounds selected for the development will be very high. Among the selected lead compounds only a very few lead candidates enter in to the final step and emerges as a potential drug candidate for new therapy. There are certain factors that results in the failure of a drug molecule are poor pharmacokinetics properties, lack of potency,adverse reactions, commercial reasons etc.⁹. Therefore, to minimize time and resource requirements of chemical synthesis and biological testing significantly, the  in silico molecular modeling may be used¹⁰.

 

The two methods in CADD are structural based and ligand based drug design. Structural based drug design depends on the three dimensional structure of biological target whereas ligand based drug design depends on molecules that bind to biological target¹¹. Drugs usually act on targets, which are either cellular or genetic chemicals in the body, that are believed to be associated with disease. Scientists identify and isolate a target and learn more about its functions and how these influence disease using a variety of techniques. They search for chemical and biological substances that target these biological markers and are likely to have drug-like effects. Various interactions of the compound with drug targets predict the biological response and are helpful in treatment of a specific disease.¹².

 

As advancements occur in the field of drug discovery process, the use of computers to predict the binding of small molecules to known target structures become an an important tool¹³,¹⁴,¹⁵. Molecular docking simulations can be carried out using a wide range of software packages like, AutoDock and DOCK, GOLD, FlexX and ICM¹⁶. Due to its enhanced docking speed AutoDock 4.2 is the most recent version which has been widely used for virtual screening¹⁷. These various computational tools like AutoDock, amber, CHARMM etc are used to study the inhibition activities. The computational tools provide the value of ΔG Kcal/mole as a binding energy between ligand and enzyme. The value of ΔG represents the inhibition strength of ligand with enzyme¹⁸.

 

The other major causes of failure for candidate molecules in drug development are unfavorable ADME and toxicity properties. Due to problems in achieving a desirable pharmacokinetic profile many potent compounds failed to progress into clinical studies. Consequently, there is increasing interest in the early prediction of these properties, with the objective of increasing the success rate of compounds reaching development and market. Through virtual screening of ligand molecules with their targets, the pharmacokinetic, biopharmaceutical and toxicity properties of a lead can be calculated¹⁹.

 

Angiotensin-I-converting enzyme- ACE (PDB ID:108A) plays an important role in the regulation of hypertension. The conversion of decapeptide (angiotensin I) to the potent vasoconstring octapeptide (angiotensin II) is catalyzed by the ACE. ACE inhibition leads to a reduction in blood pressure due to a blockade in the conversion of angiotensin-I to angiotensin-II²⁰. Since hypertension is a major risk factor in the development of cardiovascular diseases, angiotensin I-converting enzyme (ACE) inhibitory  peptides and antihypertensive peptides have been extensively researched among the bioactive peptides. ACE The ACE, which is a zinc metallopeptidase is well distributed in vascular endothelial, absorptive epithelial, neuroepithelial and male germinal cells²¹. The widely used ACE inhibitors for the treatment of cardiovascular diseases are captopril, enalapril, lisinopril, and ramipril. These synthetic ACE inhibitors are known to possess side effects such as coughing, taste disturbances and skin rashes^22,23. They also produce hyperkalemia, proteinurea, angioneuritic edema, neutropenia, glycosuria, and hepatotoxicity²⁴.  Thus, new ACE inhibitors including natural compounds found in edible species and medicinal plants that exhibit better pharmacological and toxicological profiles are searched for²⁵.

 

Basically, biologically active phytochemicals that exist naturally in plants and are known as potent effectors of biological processes are capable of decreasing disease  risk via complementary as well as over­lapping mechanisms. The presence of  phytochemicals in medicinal plants have gained much interest among researchers and the pharmaceutical and food manufacturing industries.  Flavonoids are a group of polyphenolic compounds, which are widely distributed throughout the plant kingdom²⁶. Flavonoids are a large class of phenolic compounds widely present in edible plants. This phytochemical class has been intensely investigated after the early publication of convincing epidemiological studies, which suggested a significant association between the dietary intake of flavonoids and a decreased risk of important cardiovascular diseases^27,28.  Research on flavonoids and other polyphenols their antioxidant properties, and their effects in disease prevention truly began after 1995²⁹. The flavonoids present in plants offer significant pro­tection against the development of chronic illnesses such as cardiovascular diseases^30,31 cancer³² diabetes³³ and tumors³⁴. The reports suggests that flavonoids reduce LDL oxidation³⁵, suppress lipid peroxidation³⁶ and  thereby prevents the progression of atherosclerotic lesions in cardiovascular diseases^37,38.

 

Among phytochemical class of these compounds are endowed with antiplatelet activity, antihypertensive/vasorelaxing effects, anti-atherosclerotic and positive effects against the progression of endothelial dysfunction³⁹. It is well established that some polyphenols, administered as supplements or with food, do improve health status^40,41.

 

Using computer simulation techniques, it is now possible to study the interaction of the ligand and enzyme for elucidating the binding energy. This process provides indepth understanding about the inhibition strength of various ligands. This is generally achieved by designing series of derivatives of lead compound by varying the functional groups at various locations in the lead compound⁴².

 

Therefore, the present study assess whether the polyphenols can reduce the risk of cardiovascular diseases by the inhibition of enzyme ACE esterase.  In this study 18 polyphenolic flavonoid compounds have been selected for investigation. These polyphenolic compounds were screened for their molecular properties  to find their drug likeness score. The bioactivity of these compounds were predicted. The toxicity profile of the selected compound are also evaluated. In silico docking studies was performed to screen the binding site of the ACE and binding energy of flavonoid compounds and to provide a mechanistic insight into the pharmacological effects and therapeutic benefits. Captopril was chosen as the standard to study the in silico ACE inhibitory activity.

 

MATERIALS AND METHODS:

Softwares required:

Chemsketch was downloaded from www.acdlabs.com. The online software cactus.nci.nih.gov/translate was used to carry out online smiles translatory notation. The molinspiration chemoinformatics is an online software available in www.molinspiration.com. Python 2.7- language was downloaded from www.python.com, Cygwin (a data storage) c:\program has downloaded from www.cygwin.com, Molecular graphics laboratory (MGL) tools and AutoDock4.2 was downloaded from www.scripps.edu. From the website www.accelerys.com Discovery studio visualizer 2.5.5 was downloaded. Discovery Studio (DS) is a complete modeling and simulations environment for Life Scientists⁴³ provides tools for visualization, modeling, simulations, docking, pharmacophore analysis and much more.

 

Structure of flavonoid compounds (ligands) used in the study:

The flavone parent structure  used in for the discovery of new flavonoid compounds is given in Fig 1. The structural analogues were prepared using ChemSketch. The structures of all the compounds included in the study are represented in Table 1.

 

 

Fig 1 Flavone skeleton

 

Table1. Compound code and substitution at R and R₁ position of flavone skeleton

Sl.No	Compound Code	Substitution at R	Substitution at R1
1.	FA1	-OH	- Cl
2.	FA2	-OH	- Br
3.	FA3	-OH	- F
4.	FA4	-OH	- CH3
5.	FA5	-OH	- NO­2
6.	FA6	-OH	- OCH3
7.	FA7	-OCH3	- Cl
8.	FA8	-OCH3	- Br
9.	FA9	-OCH3	- F
10.	FA10	-OCH3	- CH3
11.	FA11	-OCH3	- NO­2
12.	FA12	-OCH3	- OCH3
13.	FA13	-CH3	- Cl
14.	FA14	-CH3	- Br
15.	FA15	-CH3	- F
16.	FA16	-CH3	- CH3
17.	FA17	-CH3	- NO­2
18.	FA18	-CH3	- OCH3

 

Calculation of molecular properties:

Molinspiration supports internet chemistry community by offering free on-line services for calculation of important molecular properties like Log P (miLog P), polar surface area, number of hydrogen bond donors (HBD) and acceptors (HBA) and others. We can also predict the bioactivity score for the most important drug targets like GPCR ligands, kinase inhibitors, ion channel modulators, enzymes and nuclear receptors using this software^44,45.

 

Preparation of ligand data set and rule of five screening:

A subset of 18 compounds were drawn using ACD labs Chemsketch v 12.0. The SMILES notations of these flavonoid compounds were generated. The lead optimization was carried out for the selected compounds by calculating their druglikeness and bioactivity score using Lipinski filter^46,47 with the help of software Molinspiration. Online molinspiration software version 2011.06 (www.molinspiration.com) was used to calculate the molecular properties (Log P, number of hydrogen bond donors and acceptors, molecular weight, etc.) by generating the smile notations of the selected lead moieties. Also predicted the bioactivity score for drug targets (GPCR ligands, kinase inhibitors, ion channel modulators, enzymes and nuclear receptors). PreADMET ( www.preadmet.bmdrc.kr) is a web-based application for predicting ADME data and building drug-like library using in silico method. The web page of PreADMET was opened and the structure of the compound is drawn in the space provided in the window and was submitted to study the toxic profile of the flavonoid compounds and was compared with standard drug.

 

Coordinate file preparation:

The software Autodock 4.2 was used for molecular docking studies^2,16. AutoDock considers the best binding mode by calculating the binding free energy evaluations. Number of physical interactions was also taken into account. Autodock calculates the energy values by the characterization of internal energy of ligand, torsional free energy and intermolecular energy. An extended PDB format from traditional PDB file as was generated termed as PDBQT file using the software AutoDock4.2. This file was used to coordinate files which includes atomic partial charges⁴⁸. From the RCSB protein data bank, crystal structure of ACE (PDB ID: 108A) was downloaded.

 

Docking analysis:

The Lamarckian genetic algorithm(LGA) was employed for ligand conformational searching. It is a hybrid of a local search algorithm and genetic algorithm. This algorithm first builds a population of individuals  can be termed as genes. The individuals with the low resulting energy are transferred to the next generation and the process is then repeated. Every new generation of individuals is allowed to inherit the local search adaptations of their parents and therefore the algorithm is called LGA.

 

The flavonoid derivatives and the standard captopril were built using ChemSketch. (Table.1). The optimized ligand molecules were docked into refined angiotensin converting enzyme model using“LigandFit” in the AutoDock 4.2^49,50.

 

AutoGrid calculation:

Precalculation of atomic affinity potentials for each atom in the ligand molecule was done and rapid energy evaluation was achieved. The target enzyme was embedded on a three dimensional grid point using the AutoGrid option in the AutoDock. The energy of interaction of each atom in the ligand was encountered⁵⁰.

 

Autodock Calculation:

The most efficient method among the various docking methods is LGA.To get various docked conformations AutoDock was run several times. This is used to analyze the predicted docking energy. Based on the ligand-binding pocket of the templates the binding sites for these molecules were selected⁴⁹.

 

Analysis using AutoDock Tools

AutoDock Tools provides the methods to analyze the docking simulations. The binding site and binding energy was also obtained. The other parameters like intermolecular energy and inhibition constant were also calculated and received. Using AutoDock 4.2 scoring functions ten best poses were generated and scored for each ligand^49,50.

 

RESULTS AND DISCUSSION:

In silico docking study was carried out to identify the inhibiting potential of selected flavonoids against angiotensin converting enzyme. In the present study, we have evaluated a total of 18 different flavonoids for their drug likeness and bioactivity scores. The druglikeness scores of the compounds were evaluated with the help of Lipinski's rule. Using the AutoDock 4.2, the docking studies were performed.

 

Calculation of molecular properties:

The complex balance of various molecular properties such as hydrophobicity, electronic distribution, hydrogen bonding characteristics, molecule size and flexibility and structure features like various pharmacophoric features of a compound is called as druglikeness. These properties influence the behavior of molecule in a living organism.

 

The drug likeness properties of the flavonoid derivatives were studied. The drug relevant properties of the flavonoid compounds for their various drug likeness are presented in the Table 2. To determine molecular properties that are important for drug’s pharmacokinetic properties in vivo, Lipinski’s rule is widely used. Lipinski’s rule of five states that a candidate molecule is more likely to be orally active if: (1) the molecular weight of a compound is under 500, (2) octanol/water partition coefficient (log P) is less than 5, (3) the compounds having not more than 5 hydrogen bond donors (OH and NH groups), (4) the compounds have not more than 10 hydrogen bond acceptors (notably N and O). Number of hydrogen bond donors and acceptors were within the limit.

 

 

 

 

 

 

Table 2. Drug likeness Properties(Drug relevant properties)

COMPOUNDS	Drug relevant properties
	mi Log P	Mol .Wt	nON (H-Acceptors)	nOHNH (H-Donors)	Violations
FA1	4.54	449.85	7	2	0
FA2	4.67	494.30	7	2	0
FA3	4.03	433.39	7	2	0
FA4	4.31	429.43	7	2	0
FA5	3.42	474.43	10	2	0
FA6	3.92	445.43	8	2	0
FA7	4.82	463.87	7	1	0
FA8	4.95	508.32	7	1	1
FA9	4.30	447.42	7	1	0
FA10	4.59	443.45	7	1	0
FA11	3.70	488.45	10	1	0
FA12	4.20	459.45	8	1	0
FA13	5.21	447.87	6	1	1
FA14	4.84	492.32	6	1	0
FA15	4.70	431.42	6	1	0
FA16	4.98	427.46	6	1	0
FA17	4.09	472.45	9	1	0
FA18	4.59	443.45	7	1	0

 

Rule of five (Ro5) analysis:

The selected candidates satisfy the criteria Ro5 was predicted and the results are reported in Table 2. It also helps to identify the toxic molecules. Among the compound screened sixteen compounds showed no violations and satisfied all Ro5. The flavonoid compounds FA8 and FA13 showed one violation. It should also be noted that among the four properties compared, the numbers of hydrogen bond donor and acceptor seem to be more robust properties, as over 84% of the molecules in all the datasets satisfy Ro5 requirements.

 

Bioactivity score for optimized compounds:

Molinspiration was also used to predict the bioactivity scores of each derivative. The predicted bioactivity scores of screened compounds as well as their comparison with the standard drug for the criteria of GPCR ligand activity, ion channel modulation, kinase inhibition, nuclear receptor ligand, protease inhibitor and enzyme inhibition was calculated and results presented in Table 3. As a general rule, larger is the bioactivity score, higher is the probability that investigated compound will be active. Bioactivity activity of a compound was decided based on the bioactivity score. If bioactivity score is >0, it is an active compound while <-5.0 is an inactive compound and range between -5.0 to 0.0 is moderately active compounds⁵¹. The drug likeliness score as calculated through Molinspiration reveals that they satisfy maximum parameters. The GPCR ligand activity of the selected compounds were within the range -0.14 and -0.28 and found to be moderately active. The compounds are showing moderately active ion channel modulatory compounds and protease inhibitors. Their GPCR action were moderately active and poor kinase inhibitory property was varying as active and moderately active. The ion channel modulatory action was found to be moderate for all the selected compounds. The compounds FA2, FA3, FA6 and FA15 are biologically active for their kinase inhibitory action. FA1 to FA6 and FA15 are biologically active for nuclear receptor ligand and rest of the compounds are with moderate activity. All the compounds showed moderate enzyme inhibitory actions. The Compound FA6 showed better Compound FA14 showed a better GPCR ligand activity when compared to the other flavonoid derivatives. The ion channel modulatory effects of the compounds ranges between -0.33 (FA17) and -0.44 (FA2).

 

Table 3: Predicted Bioactivity score for optimized compounds

Compounds	GPCR ligand	Ion channel modulator	Kinase inhibitor	Nuclear receptor ligand	Protease inhibitor	Enzyme inhibitor
FA1	-0.14	-0.38	-0.01	0.02	-0.25	-0.02
FA2	-0.22	-0.44	0.03	0.05	-0.31	-0.05
FA3	-0.14	-0.39	0.03	0.05	-0.24	-0.01
FA4	-0.17	-0.43	-0.02	0.01	-0.26	-0.04
FA5	-0.18	-0.36	-0.03	0.01	-0.24	-0.01
FA6	-0.16	-0.40	0.00	0.00	-0.24	-0.02
FA7	-0.19	-0.36	-0.03	-0.08	-0.27	-0.09
FA8	-0.26	-0.42	-0.05	-0.14	-0.33	-0.12
FA9	-0.18	-0.37	0.01	-0.04	-0.26	-0.08
FA10	-0.22	-0.41	-0.04	-0.08	-0.28	-0.11
FA11	-0.22	-0.36	-0.04	-0.07	-0.26	-0.07
FA12	-0.18	-0.35	-0.01	-0.06	-0.23	-0.06
FA13	-0.20	-0.35	-0.04	-0.03	-0.26	-0.09
FA14	-0.28	-0.40	-0.06	-0.10	-0.32	-0.12
FA15	-0.19	-0.36	0.00	0.01	-0.25	-0.07
FA16	-0.21	-0.37	-0.04	-0.02	-0.25	-0.08
FA17	-0.23	-0.33	-0.05	-0.03	-0.25	-0.07
FA18	-0.21	-0.36	-0.03	-0.04	-0.25	-0.08
Captopril	-0.55	-0.03	-1.04	-0.45	0.69	0.37

 

Toxicity profile of the compounds:

The compounds FA1 to FA4 and FA12 to FA14 are non-mutagenic with medium risk. The compounds FA6 to FA 11 and FA15 to FA18 are mutagenic with medum risk. The compound FA5 is mutagenic with high risk. The carcinogenicity in mice and rat are also presented in the same table. "P" (positive) represent that the flavonoid compound is non-carcinogenic and "N"(negative) indicate the carcinogenic nature of the compounds (Table 4).

 

Table .4: Toxicity profile of compounds

COMPOUNDS	Mutagenecity	Carcinogenecity		hERG_inhibition
		Mouse	Rat
FA1	non-mutagen	P	N	medium_risk
FA2	non-mutagen	P	N	medium_risk
FA3	non-mutagen	P	P	medium_risk
FA4	non-mutagen	P	P	medium_risk
FA5	Mutagen	P	P	high_risk
FA6	Mutagen	N	P	medium_risk
FA7	Mutagen	N	N	medium_risk
FA8	Mutagen	P	P	medium_risk
FA9	Mutagen	N	P	medium_risk
FA10	Mutagen	P	P	medium_risk
FA11	Mutagen	N	P	medium_risk
FA12	non-mutagen	N	N	medium_risk
FA13	non-mutagen	N	N	medium_risk
FA14	non-mutagen	P	P	medium_risk
FA15	Mutagen	N	P	medium_risk
FA16	Mutagen	P	P	medium_risk
FA17	Mutagen	P	P	medium_risk
FA18	Mutagen	N	P	medium_risk

*P-positive:Non-toxic and **N-negative:toxic

 

Docking parameters of the flavonoid derivatives using AutoDock 4.2:

Determination of binding energy:

Binding energy is an important problem for structure based drug design. The correlation of tentative and predicted protein-ligand binding energies were calculated by AutoDock and the results were provided by the software⁵².

 

After successful docking of the polyphenolic compounds with ACE, receptor/ligand complex models generated. It was generated based on the parameters such as, hydrogen bond interactions, п – п interactions, binding energy, RMSD of active site residues and orientation of the docked compound within the active site⁵³.

 

Table 5: Binding energy, intermolecular energy and inhibition constant of flavonoid derivatives

Compounds	ACE Binding energy ( kcal/mol)	Intermolecular energy ( kcal/mol)	Inhibition constant kI (nM)
FA1	-10.11	-11.9	38.95
FA2	-10.43	-12.22	22.64
FA3	-9.72	-11.51	75.21
FA4	-10.11	-11.9	38.79
FA5	-9.22	-11.31	173.24
FA6	-10.08	-12.17	41.05
FA7	-10.64	-12.43	15.89
FA8	-10.74	-12.53	13.45
FA9	-10.0	-11.79	46.64
FA10	-10.5	-12.29	20.01
FA11	-9.11	-11.2	208.98
FA12	-10.38	-12.46	24.78
FA13	-10.79	-12.29	12.23
FA14	-10.97	-12.46	9.07
FA15	-10.3	-11.8	27.96
FA16	-10.77	-12.26	12.75
FA17	-9.39	-11.18	131.86
FA18	-9.44	-11.23	121.15
Captopril	-3.79	-4.98	1.67mM

 

Polyphenolic flavonoid derivatives showed binding energy for ACE ranging between -10.97 kcal/mol to -9.39 kcal/mol (Table 5).The compound FA14 showed lowest binding energy and FA11 showed the highest (-9.11) with ACE among the population of flavonoid analogues. All the selected polyphenolic compounds had better binding energy when compared to the standard captopril (-3.79 kcal/mol). This proves that polyphenolic compounds consist of potential ACE inhibitory binding sites when compared to the standard captopril.

 

It was also observed that intermolecular energy was also directly proportional to binding energy. It was observed that a decrease in intermolecular energy of all the selected compounds with a simultaneous decrease in the binding energy. The selected lead compounds, showed intermolecular energy for the enzyme ACE, ranging between -12.53(FA8) kcal/mol to -11.2(FA11) kcal/mol whereas the standard showed between -4.98 kcal/mol.

 

In addition, like inhibition constant (kI) of all the eighteen compounds were determined. The inhbition constant (kI) of the compounds are given in table no.6 Flavonoid derivatives showed inhibition constant for the ACE was found with varying range of 9.07nM (FA14) to 208.98 nm (FA11). All the selected flavonoid ligand molecules had exhibited lesser inhibition constant when compared to the standard captopril (9.27 μM).

 

Interaction of Captopril and FA14 with ACE binding site:

The docking studies proves that if a compound shows lesser binding energy compared to the standard, that compound is highly active. The docking poses were ranked according to their docking scores and both the ranked list of docked ligands and their corresponding binding poses⁵². In Fig. 3, docked pose of ACE with flavonoid derivative ligand (FA14) clearly demonstrated the binding positions of the ligand with the enzyme. The interaction site of ACE contains 19 amino acid residues: His353, Ala354, Ser355, Ala356, His383, Glu384, His387, Phe391, Pro407, His410, Glu411, Phe512, His513, Ser516, Ser517, Val518, Pro519, Arg522, and Tyr523. The zinc(II) ion is also an important component in ACE catalysis⁵⁴.

 

According to ACE’s catalytic mechanism and relevant experimental reports, ACE’s active site was identified. Based on the docking studies, the ACE inhibitory activity of the selected compounds were found to be decreased in the order of FA14>FA13>FA16> FA8> FA7> FA10>FA2>FA12> FA15> FA1 = FA4> FA6> FA9>FA3>FA18> FA17> FA5> FA11. All the flavonoids possess binding sites with ACE. But, only the FA14 showed better binding interactions and binding energy and intermolecular energy among the other selected flavonoids and the standard. On the basis of the above study, FA14 possess potential ACE inhibitory binding sites and docking parameters compared to that of the standard. This may be attributed due to the presence of -CH₃ and -OH groups and differences in the position of the functional groups in that compound.

 

ACE interacts with the standard captopril at the sites Gln266, Phe441,Lys495, His497, Tyr504, Tyr507, Phe511and its binding energy was found to be -3.79kcal/mol (Fig 2).

 

 

Fig 2 Docked pose of ACE with captopril

 

The interaction site of ACE with FA14 was found to be His367, Ser339, Asp399, Asp437,Lys438,, Phe441, Phe507, Phe511 which reveals that there are effective binding sites are present in the flavonoid compound FA14. The binding energy obtained from docking was -10.97kcal/mol. This indicate that among the selected flavonoids, FA14 have better binding sites and interactions with ACE ( Fig 3).

 

 

 

Fig 3 Docked pose of ACE with FA14

 

Quantitative structure-activity relationships (QSAR) play a vital role in modern drug design. QSAR studies represent a much cheaper and rapid alternative to the medium throughput in vitro and low throughput in vivo assays. Thus, when the study of the 3D molecular structure became practical routine with the parallel development of several computational molecular modeling techniques in the 1980s, the new era of the drug design process, named Computer-Aided/Assisted Drug Design (CADD) or Computer-Aided/Assisted Molecular Design (CAMD) which represents more recent applications of computers as tools in the drug design process came into and being QSAR methodology has became in a broad subfield of CADD/CAMD^55,56. Design and development of new drugs is simplified and made more cost-effective because of the advances in the concepts of QSAR studies. A methodology of QSAR studies is one of the approaches to the rational drug design⁵⁷.

 

Cheminformatics is the use of computer software to assist in the acquisition, analysis and management of data and information relating to chemical compounds and their properties. The cheminformatics groups offer programs and databases (mainly for organic and sometimes for inorganic application) related to small molecules, complementing the activities of the bioinformatics groups, who concentrate on biological macromolecules⁵⁸.

 

Computational biology and bioinformatics speed up the drug discovery process and also reduce the costs of drug development. It is also different from the way that are designed by conventional methods. Rational Drug Design (RDD) facilitate and speedup the drug designing process. RDD involves variety of methods to identify novel compounds. One of such method is the docking of the drug molecule with the receptor (target). The site of drug action, which is ultimately responsible for the pharmaceutical effect, is a receptor. In the process of docking, two molecules fit together in 3D space⁵⁹.

 

Drug discovery and development are expensive undertakings. The application of computational technology during drug discovery and development offers considerable potential for reducing the number of experimental studies required for compound selection and development and for improving the success rate. The quantitative structure activity relationships (QSAR) are certainly a major factor in contemporary drug design⁶⁰. Using the structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be designed using interactive graphics and the intuition of a medicinal chemist⁶¹.In structure based drug design, slight modification of lead molecules to obtain or improve certain therapeutic properties is one of the central strategy. The rationale behind this approach is that similar molecules bind in a similar fashion to a target receptor, thus possibly inducing the same effect. Nevertheless, the new compound may adopt a different binding mode, due to the presence of internal water molecules⁶⁰.

 

Flavonoids have gained a great amount of interest with regards to their potential for cardiovascular protection in recent years. In fact, many epidemiological studies associate an increased consumption of foods and beverages rich in flavonoids with a reduced risk of CVD and associated death^30,62,63. Additionally, several of these flavonoids or their derivatives (i.e., diosmin, rutin and quercetin) are widely used as pharmaceutical agents for their vasoprotective properties (i.e., Daflon 50 and Venorutom)⁶⁴.

 

ACE, angiotensin I and angiotensin II are part of the RAS, which controls blood pressure by regulating the volume of fluids in the body. ACE is secreted in the lungs and kidneys by cells in the endothelium (inner layer) of blood vessels⁶⁵. ACE is a zinc-containing peptidyl dipeptide hydrolase. The active site of ACE is known to consist of three parts; a carboxylate binding functionality such as the guanidinium group of Arg, a pocket that accommodates a hydrophobic side chain of C-terminal amino acid residues, and a zinc ion. Some authors suggest that the activity of flavonoids and other polyphenols is due to the formation of chelate complexes with the zinc atom within the active centre of zinc-dependent metallopeptidases⁶⁶. Possibly it also results from the formation of hydrogen bridges between the inhibitor and amino acids near at the active site^67,68.

 

CONCLUSION:

In conclusion, novel therapeutic molecules can be designed using computer aided drug design. Computer Aided Drug Design is an excellent approach of drug discovery saving time and cost of experiments in vitro. Binding affinity optimization was done in silico by designing structural analogues of flavone and predicted their interactions with ACE target by docking studies. These results indicate that the ACE has better binding sites and interactions with the pharmacophores of the selected flavonoid compounds. The flavonoid compound FA14 showed the better binding energy among all the compounds. Further, these compounds can be synthesized and screened for their in vitro and in vivo ACE inhibitory potential and their protection and prevention against cardiovascular diseases.

 

CONFLICT OF INTEREST:

The authors confirm that this article content has no conflicts of interest.

 

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Accepted on 15.07.2018           © RJPT All right reserved

Research J. Pharm. and Tech 2018; 11(10): 4370-4378.