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ADMET, Docking studies & binding energy calculations of some Novel ACE - inhibitors for the treatment of Diabetic Nephropathy

Gade Deepak Reddy1*, K N V Pavan Kumar2, N Duganath1, Raavi Divya3, Kancharla Amitha4
  1. Dept. of Pharmaceutical Chemistry, JNTU-OTRI, Anantapur, Andhra Pradesh, India
  2. Dept. of Biology, Arkansas State University, Jonesboro, Arkansas, USA
  3. Dept. of Bioinformatics, Gulbarga University, Karnataka, India
  4. Dept. of Bioinformatics, Sathyabama University, Chennai, TN, India
Corresponding Author: Deepak Reddy Gade,Dept. of Pharmaceutical Chemistry,JNTU-OTRI, Anantapur, AP, India E-mail:
Received:15 July 2012 Accepted: 30 July 2012
Citation: Gade Deepak Reddy*, K N V Pavan Kumar, N Duganath, Raavi Divya, Kancharla Amitha “ADMET, Docking studies & binding energy calculations of some Novel ACE - inhibitors for the treatment of Diabetic Nephropathy” Int. J. Drug Dev. & Res., July-September 2012, 4(3):268-282. doi: doi number
Copyright: © 2010 IJDDR, Gade Deepak Reddy et al. This is an open access paper distributed under the copyright agreement with Serials Publication, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Diabetic Nephropathy (DN) is one of the major complications of diabetes mellitus, representing the leading of cause of chronic renal disease and a major cause of morbidity and mortality in both type 1 and type 2 diabetic patients. The Renin-Angiotensin-Aldosterone System (RAAS) has been implicated in the pathophysiology of DN, and suggests a therapeutic target for blocking this system. Therefore, inhibition of RAAS plays a crucial role in the treatment of DN and therapeutic intervention mostly involves administration of angiotensin converting enzyme (ACE) inhibitors and angiotensin AT1 receptor blockers. In this current study, we have used computational methods to design 37 novel ACE-inhibitors and evaluated them for the interaction with the enzyme ACE through insilico analysis. The obtained results were compared with the standard drug enalapril to find out the potential inhibitors. Here we report that ligand 4 exhibited strongest inhibitory activity among all. All the analogs are also screened for their ADME & Toxicity profiles using insilico tools and ligand 9 is having better binding affinity next to ligand 4, and also having better ADMET profile when compared to that of ligand 4. Post docking calculations were also performed for the docked complexes in order to identify the individual ligand binding energies by employing Multi-Ligand Bimolecular Association with Energetics (Embrace).


Angiotensin converting enzyme, ADMET, Embrace minimization, Enalapril, Molecular docking


Diabetes mellitus (DM) is a chronic disease affecting approximately 220 million people throughout the world. Uncontrolled DM often leads to several severe complications including retinopathy, neuropathy and nephropathy [1].Of these, diabetic nephropathy (DN) is considered to be one of the major complications, characterized by persistent albuminuria, increased arterial blood pressure, and continuous decline in glomerular filtration rate (GFR)[2].Without specific treatment intervention, this condition eventually leads to end-stage renal disease (ESRD). Diabetic nephropathy is the most common cause of ESRD worldwide and affects approximately 30% of patients with type 1 DM and 20% of patients with type 2 DM [3]. Although there is no cure for DN, the rate of deterioration in renal function and therefore progression to ESRD can be delayed with treatment intervention. Several studies from past two decades provide evidence that controlling the levels of glucose in blood and reducing blood pressure are the key factors in the management of DN [4]. The reninangiotensin system (RAS) has always been implicated in the regulatory functions of blood pressure and fluid homeostasis [5]. Hence blocking this RAS system is the first line therapy in the treatment of DN. Accordingly, Angiotensin converting enzyme (ACE) inhibitors and AT1 receptor blockers provide nephroprotective effect and delay the progression of DN[6]. In this current study, using computational methods we have designed 37 novel ACE-inhibitors and evaluated them for interaction with the enzyme ACE through in silico analysis.


Selection & preparation of protein

Angiotensin converting enzyme (ACE) was retrieved from the RCSB Protein Data Bank ( with PDB Id- 1O8A with X-ray diffraction resolution of 2.00Å. ACE is responsible for conversion of Angiotensin - I to Angiotensin – II, which is responsible for increase in blood pressure, and Vascular Endothelium Dysfunction. Preparation of the retrieved protein was performed by using protein Preparation Wizard of Schrodinger suite 2010. Initially all the internal ligands, ions, metal elements, and water molecules were removed and hydrogens were added to satisfy the valances. Refinement of the loops was performed by using PRIME module, and hydrogen bonds were assigned. Energy minimization / geometrical optimization of the preprocessed protein structure were done by employing OPLS 2005 (Optimized Potentials for Liquid Simulations) with RMSD as 0.30.
Binding site characterization of the processed protein was performed by using SITEMAP 2.4 module[7] in which the hydrophilic (hydrogen bond acceptor & donor), hydrophobic, and metal binding regions were mapped which can be very useful in active site identification and also Structure based Drug designing (SBDD). Various regions of the active site of the retrieved protein can be seen in Fig: 1.

Selection of Lead moiety & Designing of ligands

The Lead, 2-(2-oxopropylamino)-4-phenylbutanoic acid is the common pharmacophore of the Carboxylic acid derivatives of Angiotensin Converting Enzyme Inhibitors. Carboxylic acid derivative of ACE inhibitor is selected as Lead moiety because of its optimum potency, higher bioavailability than phosphoric acid derivatives and low toxic profile than sulphonic acid derivative (captopril).
37 ligands were designed from the Lead compound by modifying the non pharmacophoric parts like R1, R2 and R3. Modifications were primarily done at the non-pharmacophoric sites of the ACE inhibitors in order to maintain the original biological therapeutic activity. All the ligands were designed by using Accelrys – Symyx Draw 4.0. These ligands were designed according to the SAR properties of the carboxylic acid derivatives of ACE inhibitors. Structure of the lead scaffold and its sites of modification can be seen in Fig: 2. Newly designed 37 ligands were shown in Table: 1.

Preparation of ligands

Preparation of ligands was performed by using “LigPrep 2.4” module of Schrodinger Suite 2010. The simplest use of LigPrep is to produce a single, lowenergy, 3D structure with correct chirality for each successfully processed input structure. LigPrep can also produce a number of structures from each input structure with various ionization states, tautomers, stereochemistries, and ring conformations, and eliminate molecules using various criteria including molecular weight or specified numbers and types of functional groups present [8].The ionization states in a given pH range of 7±2 (general pH of biological system) were generated by adding or removing protons from the ligand using EPIK 2.1 module. The option to account for metal binding is set by selecting Add metal binding states, can be used by Glide module when docking ligands to metalloproteins. OPLS 2005 Force Field was selected for energy minimization.
Molecular properties like Molecular weight, Hydrophobic component, Hydrophilic component, Total solvent-accessible volume, number of hydrogen bonds that would be donated, number of hydrogen bonds that would be accepted, partition coefficient of all the newly designed 37 ligands were studied by using “QikProp 3.3” module of Schrodinger Suite 2010 [9] and results were listed in Table: 2.

ADME & Toxicity Studies

Insilico ADME studies were performed by using ADME Descriptors algorithm of Accelrys Discovery studio 2.5 in which various pharmacokinetic parameters like Aq. Solubility [10], Human Intestinal Absorption [11],Plasma protein binding (PPB) [12],blood-brain-barrier (BBB) penetration [13], cytochrome P450 inhibition[14] and hepatotoxicity levels[15] were estimated for 37 ligands. Obtained results were cross checked with the standard levels listed in Table: 3
Toxicity profiling of all the 37 ligands were performed by employing Toxicity prediction – extensible protocol of Accelrys discovery studio 2.5. Toxicity profile includes screening for aerobic biodegradability, developmental toxicity potentials, AMES mutagenicity, carcinogenicity, and ocular & skin irritancy[16]. Teratogenicity effects of the ligands were studied by using an online tool, OSIRIS property explorer [17].

Receptor – Ligand Interactions (Docking Studies)

Receptor – Ligand interaction, generally docking studies were performed by using GLIDE 5.6 (Grid- Based Ligand Docking with Energetics) module in Extra Precision (XP) mode of Schrodinger Suite 2010[18,19].Glide docking algorithm consists of two main steps like receptor grid generation and ligand docking. In the first step, a three dimensional grid is generated by selecting a particular protein residue (from data obtained from SiteMap). Grid is constituted by receptor’s shape and properties by sets of fields that provide relatively better accurate scoring of the ligand poses.
In another step, of molecular docking, Extraprecision (XP) docking, a different potential segregating procedure is employed to analyze the protein-ligand interactions. Then, the scoring is identified for the energy-minimized poses and the poses that pass the initial screens enter the final stage of the algorithm, which involves evaluation and minimization of a grid approximation to the OPLSAA nonbonded ligand-receptor interaction energy. Then Emodel combines Glide Score, the nonbonded interaction energy, and the excess internal energy of the generated ligand conformation.
The docking score from Glide (GlideScore)[20] is entirely based on ChemScore.However, it also includes a steric-clash term, adds polar terms featured by Schrodinger to correct electrostatic mismatches, and has modifications to other terms: GScore = 0.065 * Van der Waals energy + 0.130 * Coulomb energy + Lipophilic term (hydrophobic interactions) + H-bonding + Metal binding + BuryP (Penalty for buried polar groups) + RotB (Penalty for freezing rotatable bonds)+ Site (Polar interactions in the active site).

Binding Energy Calculations

Post docking calculations like estimation of binding energies of the ligands with receptor were performed by employing the automated mechanism of Multi- Ligand Bimolecular Association with Energetics (MBAE)[21],using EMBRACE minimization of Macro Model 9.8 module of Schrodinger suite 2010. Embrace minimization was performed by opting energy difference mode. The calculation was performed first on the receptor, then on the ligand, and finally on the complex. The energy difference is then calculated using the equation:
ΔE = Ecomplex – Eligand – Eprotein (QE is the ligand binding energy)


ADME &Toxicity predictions

We have analyzed various phamrmacokinetic and pharmacodynamics properties of enalapril and its 37 newly designed analogs, among which were Aq. Pharmacokinetic properties were Solubility, Human Intestinal Absorption, Plasma protein binding (PPB), blood-brain-barrier (BBB) penetration, cytochrome P450 inhibition, and hepatotoxicity levels. Pharmacodynamics properties (toxicity profile) were Aerobic biodegradability, developmental toxicity potentials, AMES mutagenicity, carcinogenicity, ocular & skin irritancy, Teratogenicity effects. In this study, when the results were compared to the reference Level values and found that no single ligand is having BBB penetration, as ACE inhibitors should not cross BBB, to prevent the CNS adverse effects. ADME descriptor levels of the analogs that were obtained from the ADME Descriptors protocol of Accelrys Discovery studio were listed in Table: 4. Toxicity screening of the ligands along with enalapril was performed by using Toxicity predictionextensible protocol and the results were tabulated in Table: 5. From these toxicity studies it was found that none of the ligands have shown mutagenicity and ligands like 31, 32 have shown carcinogenic characters in male rat models. As in general, ACE inhibitors were administered orally, skin & ocular irritancy characters can be neglected. Teratogenicity an effect of the ligands was studied by using an online tool has shown that none of the ligands were having reproductive effects. Few ligands have shown dose dependent toxicity characters.

Molecular Docking studies

To identify the molecular binding interactions of the analogs with the receptor, all the 37 ligands were docked into the active binding site of the enzyme ACE using Glide docking algorithm and the resulted XP GScore of the ligands were compared with enalapril (marketed potent ACE inhibitor). The docking result of the ligands and enalapril was listed in table: 6. The docking result revealed that the receptor-ligand complex was stabilized by hydrogen bonds, hydrophobic and electrostatic interactions. Among all the ligands, seven hydrogen bonds were formed between receptor active site residues and ligand 4 (Fig: 3) shown the highest dock score of - 10.31 and enalapril (-6.9442) has six hydrogen bonds (Fig: 4). Most of the ligands have shown interactions with protein residues like GLN 281, HIS 383, GLU 384, LYS 511, ARG 522, TYR 523. About 14 ligands have shown better dock score than enalapril. Dock scores for all the 37 ligands and enalapril along with the interacted protein residues and bond distances were listed in Table: 6.
The aromatic hydroxyl group (-OH) in the pharmacophore of ligands 1, 4, 25, 26, 36, and 37 shown hydrogen bonding with protein residues like ASP 415 (ligand 1), GLU 411 (ligands 4, 25, 36, 37) and ASP 358. Common interaction sites in most of the ligands were carboxylic acid group which is a major pharmacophoric feature in carboxylic acid derivatives of ACE inhibitors that acts as Zinc binding site, terminal carboxylic acid attached to the 5 or 6 membered heterocyclics like piperdine and pyrrolidine, and their derived heterocyclic rings at R1 position. The amine linkage (-NH2) and carbonyl group (-C=O) of the ligands have shown binding interactions and plays a key role in the docking through their hydrophilic nature. Ligands showing better dock score than enalapril, have only 5 or 6 membered heterocyclic rings directly attached to carboxylic acid group (-COO-) . Presence of the bulkier rings at R1 decreases the binding affinity of the ligand towards the protein, which may be due to the steric hindrance of the methoxy and carboxylic groups attached to the bulkier rings.
In order to calculate the free energy of binding (FEB) of each ligand, Post docking calculations of the docked complexes were performed by using automated mechanism of Multi-Ligand Bimolecular Association with Energetics (MBAE). Total free energy of binding of each ligand is tabulated in Table: 7. The total free energy of binding is the difference energy of the complex and ligand & protein which includes solvation energy, Vander wall’s energy, electrostatic energy, valence energy, and constraint energy.


In this study we have designed a set of 37 novel molecules and performed docking simulations in order to identify their binding affinity and binding energy towards the protein angiotensin converting enzyme and tested for their ADME & Toxicity profiles using Insilico tools. Among all the 37 molecules and enalapril, (marketed drug) ligand 4 has shown highest dock score (XP GScore) .Ligand 9 has shown the best dock score next to ligand 4 with better ADMET profiles. Binding energies in the protein – ligand interactions explain how fit the ligand binds with target protein.
Examination of the binding interactions of the ligands helps in elucidating the reasonable and appropriate structural features of ligand which increase the binding affinity and therapeutic efficacy. Presence of the bulkier ring structures at R1 position might decrease the overall fitness of the ligand and presence of the aromatic hydroxyl group at R3 has shown better binding fitness by forming an extra hydrogen bond.


Authors are thankful to the support given by Yamini Lingala, Dept. of Chemistry, Nizam College, Hyderabad, and Mr. Subhadip Banerjee, Dept. of Pharmaceutical Sciences, Jadavpur University, Kolkata.

Conflict of Interest


Source of Support


Tables at a glance

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Table 1 Table 2 Table 3 Table 4
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Table 5 Table 6 Table 7

Figures at a glance

Figure 1 Figure 2 Figure 3 Figure 4
Figure 1 Figure 2 Figure 3 Figure 4


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