Molecular Docking Studies on Oxidosqualene Cyclase with 4-Piperidinopyridine and 4-Piperidinopyrimidine As Its Inhibitors

Full Length Research Paper

Molecular docking studies on oxidosqualene cyclase with 4-piperidinopyridine and 4-piperidinopyrimidine as its inhibitors

G. Jhansi Rani*, M. Vinoth and P. Anitha

Department of Biochemistry and Bioinformatics, Hindustan College of Arts and Science, Chennai, India.

Accepted 22 February, 2011

Oxidosqualene cyclase (OSC) or Lanostrol Synthase is one of the major enzyme in cholesterol biosynthesis. OSC is involved in conversion of squalene to lanosterol. Increased level of cholesterol in blood leads to hypercholesterolemia and major risk factor for cardiovascular diseases. Statin drug molecule is developed to inhibit HMGCoA, which is the first enzyme of cholesterol biosynthesis showed major side effects. Insilico predictions based on the crystal structure of OSC are performed. Active molecular docking studies using GOLD software reveals 4-piperidinopyridine and 4- piperidinopyrimidine (piperidino derivatives) are potent inhibitors of OSC and satisfy ADME properties. The PASS (Prediction of Activity Spectra for Substances) prediction results show the inhibiting activity of OSC enzyme.

Key words: Oxidosqualene cyclase (OSC), piperidino derivatives, molecular docking, toxicity testing, prediction of activity spectra for substances (PASS).

INTRODUCTION

Hypercholesterolemia is a condition characterized by toxic drug regimes (Jean-Didier, 2005; Michael et al. very high levels of cholesterol in blood, have a high risk 1996). of developing heart disease and also cause angina, Insilico models have potential use in the discovery and tendon xanthomas, xanthalasmata, achilles tendons optimization of novel molecules, with affinity to the target, (Genetic Home, 2007). A large scale trials therapy clarification of absorption, distribution, metabolism, demonstrated that cholesterol level lowered by 3-hydroxy excretion and toxicity properties as well as 3-methyl coenzyme A (HMGCoA) reductase inhibitors physiochemical characterization (Ekins et al., 2007). (Gavin, 2000). Statins, a class of hypercholesterolemia Docking is an important in the study of protein ligand agents are well established agents for lowering plasma interaction properties such as binding energy, geometry cholesterol levels and have wide spread agent for complementarity, electron distribution, hydrogen bond lowering plasma cholesterol levels, have wide spread donor acceptor, hydrophobicity and polarizability thus therapeutic used in the treatment of cardiovascular molecular docking contribute a major role in the drug disease that led to search for other novel inhibitors of discovery in the identification of innovative small cholesterol biosynthesis pathway (David, 1987; John et molecular scaffold, exhibiting the important properties al., 2006) with selectivity for the target together with reasonable Oxidosqualene cyclase (OSC) is involved in conversion ADME profile, lead and or drug likeness (Krvoat et al., of squalene to lanosterol in cholesterol biosynthesis 2005) pathway, identifying drug-drug interactions involving OSC GOLD, the first algorithm to be evaluated on a large is vital for improving therapy of hypercholesterolemia, data set of complex posses, an empirical free energy thus would lead to the development of more effective less scoring function that estimates the free energy of binding permitting inhibition constant for protein ligand complex. It is a package of program for structure visualization and manipulation for docking, the post processing and *Corresponding author. E-mail: [email protected]. visualization of the results (Jones, 1997). 32 J. Bioinform. Seq. Anal.

MATERIALS AND METHODS conformer with highest binding score was used for further analysis (Girija et al., 2010) Preparation of protein molecule

The experimental structure of human oxidosqualene cyclase (OSC) ADME/ toxicity testing (PDB ID: 1GSZ) as shown in Figure 2 was retrieved from the RCSB protein data bank as a PDB file. The protein molecules were prepared mainly by using the software Swiss PDB viewer. Active \ADME (absorption, distribution, metabolism, and excretion) site residues within a range of 4.0 A0 were selected and saved in determines drug like activity of the ligand molecules based on PDB format. Later, the active site residues were minimized in Argus Lipinski rule of 5 (Konstantin, 2005). Increasing clinical failures of Lab (4.0.1) after adding hydrogen bonds (Akifumi, 2009). new drugs call for a more effective use of ADME/TOX technologies, becoming more advanced and reliable in terms of accuracy and predictiveness, an increase in their usage is expected during the Preparation of ligand initial development and screening phase of innovative drugs. New computational methods including consensus modeling show The ligand compounds 4- piperidino pyridine and 4- piperidino promise for increase in the accuracy of Insilco ADME-TOX pyrimidine were drawn using ACD/ Chemsketch (12.0) (Alex, 2009) prediction used for virtual screening in lead optimization (Gregory, and saved in mol 2 format. The saved ligand compounds were later 2004). imported and minimized in Argus Lab after adding hydrogen bonds. The molecules thus obtained were saved in PDB format. PASS prediction

Argus Lab The prediction of activity spectra for substances predicts the biological activity spectrum (BAS) (Sohelia et al., 2001) of a Argus Lab is an electronic structure program that is based on the compound represents the complex of pharmacological and quantum mechanics; it predicts the potential energies, molecular physiological effects and biochemical mechanisms of actions, structures, geometry optimization of structure, vibration frequencies specific toxicity (mutagenecity, carcinogenecity, teratogenecity and of co-ordinates of atoms, bond length, bond angle and reaction embryotoxicity) for a compound on the basis of its structural formula pathway (Cheng, 2003). (Poroikov, 2000). The energy (E) of the molecule is calculated as E= E stretching + Figure 1 show the structure of piperidinoderivatives of 4- E bending + E torsion + E vander Waals + E electrostatic + E piperidinopyridine and 4-piperidinopyrimidine. hydrogen bond +cross term. These terms are of importance for the accurate calculation of geometric properties of molecules. The set of energy functions and the corresponding parameters are called a force field (Afshan, 2009). RESULTS AND DISCUSSION

Genetic algorithm In assessment using GOLD 3.2, OSC showed better affinity with piperidino derivatives. Our receptor retrieved Genetic algorithm (GA) is a computer program that mimics the from protein data bank (PDB ID: 1GSZ) is a complex process of evolution by manipulating a collection of data structures structure of OSC with anticholesteremic drug (drug Id called chromosomes. It is also stochastic optimization methods and RO-4A-8071). Target protein has four identical chains A, provides a powerful means to perform directed random searches in drug designing (Shahper, 2008). It study properties of QSAR, B, C, D with similar active sites, Hence B chain has utilizes the novel representation of the docking process, each chosen for analysis. The possible active site was chromosome encodes an internal conformation and protein active identified using Swiss PDB viewer, It is found 12 active site and includes a mapping from hydrogen-bonding sites in the site amino acid residues as TRP 169, ALA 170, ILE 261, ligand and protein (Seiburg, 1990; Rajasekhar et al., 2010). On PRO 263, TRP 312, PHE 365, ASP 374, ASP 376, ASP decoding a chromosome, fitness is evaluated by PLS (partial least 377, TYR 420, PHE 437, TRP 489 by the removal of squares) cross validation to position the ligand within the active site of the protein, in such a way that as many of the hydrogen bonds complexed anticholesteremic drug. Therefore it is chosen suggested by the mapping are formed (Kimura et al., 1998). as a most biologically favorable site for docking. The Docking of flexible ligands to macromolecules is paramount in conformational stability structure of OSC and piperidino structure based drug design, few programs that work with GA also derivatives were obtained by energy minimization using enable automated docking; another application of GA is the Universal Force Field performed in Argus Lab software. automated generation of small organic molecules using lipophilicity, electronic properties and shape related properties for calculation of the scoring function (Nissink et al., 2002). Docking of piperidino derivatives to OSC

Docking using GOLD Docking of piperidino derivatives with OSC was Genetic algorithm was implemented in GOLD v 3.2 that was applied performed using GOLD. The algorithm exhaustively to calculate the possible conformations of the drug that binds to the searches the entire rotational and translational space of protein (Selvaraj, 2008). The genetic algorithm parameters used the ligand with respect to the receptors. The various are population size-100, number of islands-5, niche size-2, solutions evaluated by a score, which is equivalent to the selection pressure-1.1, migrate-2, number of operators-100,000, absolute value of the total energy of the ligand in the mutate-95, cross over-95. During docking process, a maximum of 10 different conformations was considered for the drug. The protein environment. The best docking solutions GOLD Rani et al. 33

(a) (b)

Figure 1. Structure of piperidinoderivatives (a) 4-piperidinopyridine and (b) 4-piperidinopyrimidine.

Table 1. GOLD scores and interactions of anticholesteremic drug and piperidino derivatives.

Interaction Hydrogen bond distance S/N Compound GOLD score (D-H…A) between donor and acceptor (Å) 1 Anticholesteremic (Co-crystal ligand) TYR420 (NH…N) 2.606 52.04 (TYR 420) N- 2.471 2 4-piperidino pyridine H…O, (TRP312) 62.21 2.490 N-H…N

3 4-piperidino pyrimidine ASP376 (NH…O) 2.296 64.7

score for each compound was considered. It was noted that GOLD scores of 4-piperidinopyridine and 4-piperidinopyrimidine was 64.7 and 62.21, respectively, which is greater than anticholesteremic drug score value 52.04 as shown in Table 1, Figures 3 and 4. The drug like activity of the ligand molecules are characterized using ADME properties. Anticholesteremic drug and piperidino derivatives satisfy Lipinski rule of 5 and ADME properties results are shown in Table 2. The biological activity of piperidino derivatives were predicted using PASS by analyzing the parameters of probabilities of presence (pa) and probabilities of absence (pi). Activities of the molecules are shown in Tables 3 and 4.

Conclusion

OSC is the most recent potent drug target for Figure 2. Structure of oxidosqualene cyclase. hypercholesterolemia; the active site residues of OSC 34 J. Bioinform. Seq. Anal.

Table 2. Shows ADME properties of piperidino derivatives and anticholesteremic drugs.

M .W A R S F B R B # R R L n C C/n C # Ch r Ch r L o g p P SA D R S C S/N L ig M in : 20 0 M in : 0 M in : 0 M in : 0 M in : 0 M in : 0 M in : 0 M in : M in : 0 .1 M in : 0 M in : 2 M in : 2 M in : 0 M in : 5 M a x : 6 0 0 M a x : 6 M a x : 1 2 M a x : 1 5 M a x : 5 0 M a x : 7 M a x : 1 2 2 M a x : 1 .0 M a x : 2 M a x : 2 M a x : 6 M a x : 1 5 0 1 4-p ip e rid in o p y rid in e 49 4.2 0 8 5 27 4 6 21 10 0.500 0 0 1.9 2 9 5.09 2 4-p ip e rid in o p y rim id in e 508.2 0 8 5 27 4 6 21 10 0.470 0 0 2.24 9 5.09 3 A n tic h o le s te re m ic d ru g 380.3 2 6 4 25 4 7 21 7 0.333 0 0 2.84 78.35

Lig- ligand, M.W- molecular weight, DRS- donors, ARS- acceptors, FB- flexible bonds, RB- rigid bonds, R- ring number, RL- ring length, C- carbons, nC- non carbons, C/nC- ratio non carbons/carbons, #Chr- number of charges, Chr- total charge, Log p- log p (octanol/water), PSA- polar surface area.

4-piperidino pyridine 4-piperidino pyrimidine

Figure 3. Shows the docking results of piperidino derivatives with OSC. Rani et al. 35

Table 3. Activity for 4-piperidino pyridine.

Activity prediction 2.3-Oxidosqualene-lanosterol cyclase inhibitor Possible activities at Pa > 30% Pa Pi Activity 0.807 0.013 Neuropeptide agonist 0.632 0.007 Polarisation stimulant 0.670 0.046 Nootropic 0.564 0.025 Cognition disorders treatment 0.523 0.048 Vasodilator. cerebral 0.456 0.001 2.3-Oxidosqualene-lanosterol cyclase inhibitor 0.442 0.016 Antiobesity 0.434 0.034 Atherosclerosis treatment 0.461 0.061 Alpha-7 nicotinic receptor agonist

Table 4. Activity for 4-piperidino pyrimidine.

Activity prediction 2.3-Oxidosqualene-lanosterol cyclase inhibitor Possible activities at Pa > 30% Pa Pi Activity 0.877 0.004 Neuropeptide agonist 0.637 0.007 Polarisation stimulant 0.593 0.001 2.3-Oxidosqualene-lanosterol cyclase inhibitor 0.570 0.024 Cognition disorders treatment 0.600 0.075 Nootropic 0.533 0.008 Antiobesity 0.472 0.057 Alpha7 nicotinic receptor agonist 0.877 0.004 Neuropeptide agonist

70 60

e 50

o Anticholesteremic c

s 40

4-piperidino pyridine D e r L 30 o

O 4-piperidino pyrimidine c

G 20 s 10 0

Compounds

Figure 4. Comparisons of GOLD scores. 36 J. Bioinform. Seq. Anal.

were predicted using Swiss PDB viewer. The refined John G, Lawrence S, Scott SL (2006). Statin therapy and autoimmune models of OSC and piperidino derivatives were obtained disease: From protein prenylation to immunomodulation. Nat. Med., 6: 358-370. after energy minimization using the Argus Lab software. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997). Development The stable model of OSC was further used for virtual and validation of a genetic algorithm for flexible docking. J. Mol. Biol., docking of piperidino derivatives. It is well known that 267: 727-748. hydrogen bonding plays an important role in the structure Kimura T, Kiyoshi H, Kimito F (1998). GA strategy for variable selection in QSAR studies. Application of GA based region selection to a 3D- and function of biological molecules, especially for QSAR study of acetylcholinesterase inhibitors. J. Chemoinforma. inhibition in a complex. Piperidino derivatives play a vital Comp. Sci., 39: 112-120. role in holding the molecule at a place at the active site of Konstantin VB, Yan AI, Nikolay PS, Andrey AI Sean E (2005). OSC by hydrogen bonds. Applying Lipinski’s rule of 5 to Comprehensive computational assessment of ADME properties using mapping techniques. Curr. Drug Discov. Technol., 2: 99-113. piperidino derivatives to evaluate drug likeness Michael M, Muller P, Maier R, Eisel B (1996). Effects of novel 2,3 (absorption, distribution, metabolism and excretion), there oxidosqualene cyclase inhibitor on the regulation of cholesterol was no violation of the rule determining drugs biosynthesis in Hep G2 cells. J. Lipid Res., 37: 148-159. pharmacological activity in the body. PASS prediction Nissink JW Murray C, Hartshorn M, Verdonk ML, Cole JC, Taylor R (2002). A new test set for validating predictions of protein-ligand reveals that 4-piperidino pyridine and 4- piperidino interaction. Proteins, 49: 457-471. pyrimidine are potential inhibitor for OSC as target for Rajasekhar C, Anuradha CM, Chaitanya M, Babujan B, Yogiprasad P, hypercholesterolemia forming a hydrogen bonding and Chitta SK (2010). Structural characterization of pf serine with non-bonded interaction to act as a drug candidate hydroxymethyl transferase: A novel target for malaria. J. Bioinforma. Seq. Anal., 2: 13-24. yet pharmacological study will confirm it to be promising Ekins S, Mestres J, Testa B (2007). In silico pharmacology for drug in future. discovery: Applications to targets and beyond. Brit. J. Pharm., 152(1): 21-37. Selvaraj M, Malik BK (2008). Modeling of human CCR5 as target for HIV-I and virtual screening with therapeutic compounds. REFERENCES Bioinformation, 3(2): 89-94.

Shahper NK, Asad UK (2008). Computational simulation of Afshan N, Khalida B, Farhat B, Najaf AG, Naheed A (2009). Mitoxantrone binding with Human Serum Albumin. J. Proteomics Confirmational analysis (geometry optimization) of nucleosidic Bioinforma., 1: 17-20. antitumor antibiotic showdomycin by Argus Lab software. Pak. J. Sieburg HB (1990). Physiological studies in silico studies in the scinces Pharm. Sci., 22(1): 78-82. of complexity, 12: 321-342. Akifumi O, Ohgi T (2009). Validation of Argus Lab efficiencies for Soheila A, Gerhard B, Bertram C, Michael K, Dmitri F, Vladimir P binding free energy calculations. Chem. Bioinforma. J., 9: 52-61. (2001). Discriminating between drugs and non drugs by prediction of Alex MJ (2009). Docking studies on anticancer drugs for breast cancer activity spectra for substances (PASS). J. Med. Chem., 44: 2432- using Hex. Proceedings of the International Multi conference of 2437. Engineers and Computer Scientists, 1. Poroikov VV, Filimonov DA, Yu VB, Lagunin AA, Kos A (2000). Girija CR, Prasantha K, Chtan SP, Noor SB, Akheel AS (2010). Robustness of biological activity spectra predicting by computer Molecular docking studies of curcumin derivatives with multiple program PASS for noncongeneric sets of chemical compounds. J. protein targets for procarcinogen activating enzyme inhibition. J. Chem. Inf. Comput. Sci., 40(6): 1349-1355. Proteomics Bioinforma., 3(6): 200-203.

Cheng A, Merz KM (2003). Prediction of aqueous solubility of a divers

set of compounds using quantitative structure- property relationships

J. Med. Chem., 46: 3572-3580.

David K (1987). Inhibition of cholesterol biosynthsis. J. Nutr., 117: 1330-

1334.

Krovat EM, Steindl T, Lange T (2005). Recent advance in docking and

scoring. Curr. Computer-aided Drug Des., 1: 93-102.

Gavin JB, Paul MR (2000). Are statin anti-inflammatory? Curr. Control

Trials Cardiovasc. Med., 1: 161-165.

Gregory MB (2004). In silico ADME-TOX prediction: The more, the

merrier. Current Drug Discovery.

Hypercholesterolemia, Genetics Home Reference (2007). Jean-Didier M, Jinglei Y, Simon B, Louri K, Elain MR, Wolf CR, Gordon CKR, Mark JIP Michael JS (2006). In silico and in vitro screening for inhibition of cytochrome p450 CYP3A4 by comedications commonly used by patients with cancer. Drug Metab. Disposition, 34: 534-538.