American Journal of Biological Chemistry 2016; 4(1): 1-5 http://www.openscienceonline.com/journal/ajbc

In Silico Molecular Docking Study on the κ-Opioid

Receptor Antagonists BU09059 and BU09057

Dan-Qi Zhao1, Wen-Xia Ren1, Mei-Na Qiu1, Ru-Peng Ou1, Hua-Jun Luo1, *, Zhi Xie1, Wei-Qiao Deng2

1Hubei Key Laboratory of Natural Products Research and Development, College of Biological and Pharmaceutical Science, China Three

Gorges University, Yichang, China 2Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China

Email address

hjluo_ctgu@163.com (Hua-Jun Luo) *Corresponding author

To cite this article Dan-Qi Zhao, Wen-Xia Ren, Mei-Na Qiu, Ru-Peng Ou, Hua-Jun Luo, Zhi Xie, Wei-Qiao Deng. In Silico Molecular Docking Study on the κ-

Opioid Receptor Antagonists BU09059 and BU09057. American Journal of Biological Chemistry. Vol. 4, No. 1, 2016, pp. 1-5.

Received: October 13, 2016; Accepted: October 26, 2016; Published: December 6, 2016

Abstract

As the selective к-opioid receptor (KOR) antagonists, BU09059 and BU09057 were designed from JDTic by soft-drug principles. Although there were small differences in their chemical structures, BU09059 (Ki=1.72 nM) showed significantly higher affinity than analogue BU09057 (Ki=158.6 nM). To explain the interaction modes of BU09059 and BU09057 with KOR, in silico molecular docking calculations were studied using induced-fit docking (IFD) and molecular mechanics/generalized Born surface area (MM/GBSA) calculation methods. The docking Gscore, IFD score and ∆Gbind (binding free energy) of BU09059 (-8.76, -793.07 and -33.84 kcal/mol) are lower than those of BU09057 (-7.84, -790.51 and -14.56 kcal/mol), which is consistent with the experimental affinity results. From the calculation analysis, there was the significant difference in binding with the key residues Asp138 and Tyr139. The binding energies of BU09059 with Asp138 and Tyr139 are -1.63 and -6.18 kcal/mol, while those of BU09057 are 5.04 and -0.03 kcal/mol. These results could promote the rational design of novel KOR antagonists.

Keywords

BU09059, BU09057, к Opioid Receptor, Induced-Fit Docking, Binding Free Energy

1. Introduction

The к(kappa)-opioid receptor (KOR), belongs to the G protein-coupled receptor (GPCR) superfamily, plays a significant role in many physiological functions, such as drug abuse, depression and pain relief [1, 2]. In recent years, there is growing interest in KOR antagonist for psychiatric diseases treatment, including addiction and mood disorders [3-5]. Some high affinity and selective KOR antagonists such as JDTic, GNTI and norBNI, have been identified [6, 7]. Specially Wu et al. report the crystal structure of a human KOR construct in complex with JDTic at 2.9 Å resolution [8]. However, these compounds all have long-lasting effects

in vivo. JDTic has peak effect at 7 days and long-lasting receptor blockade up to 21 days [9]. Then Casal-Dominguez et al. synthesized BU09059 and BU09057 (Fig. 1) as JDTic analogues by soft-drug principles, which have the shorter duration of KOR antagonist action in vivo [10]. Although their chemical structures are almost similar, there is the distinct different affinity between BU09059 (Ki=1.72 nM) and BU09057 (Ki=158.6 nM) [10]. Many scholars have designed and synthesized some high selective and affinity KOR antagonists [11, 12], but the soft-drug interaction mechanism research of KOR antagonists is little. Hence, to explain the binding modes of BU09059 and BU09057 with KOR, in silico molecular docking calculations were studied in this paper by IFD and MM/GBSA calculation methods.

2 Dan-Qi Zhao et al.: In Silico Molecular Docking Study on the κ-Opioid Receptor Antagonists BU09059 and BU09057

Fig. 1. Chemical structures of JDTic, BU09059 and BU09057.

2. Experimental Section

2.1. Molecular Docking

From the Protein Data Bank [13], the crystal structure of the human KOR (chain A) in complex with JDTic (PDB code: 4DJH) [8] was used. Molecular docking calculations were performed through induced-fit docking method [14] in the Schrödinger software suite [15-17]. There were three consecutive steps in IFD protocol [18]. (1) The ligand was docked into a rigid receptor model with scaled-down van der Waals (vdW) radii. The Glide XP mode [19] was used for the initial docking, and 20 ligand poses were selected for protein structural refinements. The dimensions for the cubic boundary box centered on the centroid of JDTic were set to 25 Å × 25 Å × 25 Å. (2) Prime program was used to generate the induced-fit protein–ligand complexes. Each structure from the previous step was subjected to side chain and backbone refinements. All residues with at least one atom located within 5.0 Å of each corresponding ligand pose were included in the Prime refinement [20]. The refined complexes were ranked by Prime energy, and the receptor structures within 30 kcal/mol of the minimum energy structure were passed through for a final round of Glide docking and scoring. (3) Each ligand was re-docked into every refined receptor structure produced in the second step at default settings. The IFD score was calculated (IFD score = 1.0 Glide_Gscore + 0.05 Prime_Energy) and used to rank the IFD poses. The best pose complex was studied and visualized using PyMOL [21] and Maestro 9.3 [22].

2.2. MM/GBSA Calculations

MM/GBSA method was used to calculate the binding free energy (∆Gbind) for the best pose complex. MM/GBSA procedure in Prime program [23] was performed according to the following equation [24]:

bind MM solvG E G T S∆ = ∆ + ∆ − ∆ (1)

Where ∆EMM is the difference of the gas phase MM energy between the complex and the sum of the energies of the protein and inhibitor, including ∆EElect (electrostatic), ∆EVDW (van der Waals) energies and ∆Einternal (bond, angle, and

dihedral energies). ∆Gsolv is the change of the solvation free energy upon binding, and includes the electrostatic solvation free energy ∆GGB (polar contribution calculated using generalized Born model), and the nonelectrostatic solvation component ∆GSA (nonpolar contribution estimated by solvent accessible surface area). T∆S is the change of the conformational entropy upon binding, which calculated through normal-mode analysis Rigid Rotor Harmonic Oscillator (RRHO) in MacroModel module [25].

3. Results and Discussion

The induced-fit docking between KOR and BU09059 (BU09057) was simulated. The Glide Gscores and IFD scores (the best pose) of ligands were -8.76, -793.07 kcal/mol (BU09059) and -7.84, -790.51 kcal/mol (BU09057). The docking scores of BU09059 were better than those of BU09057. Although Casal-Dominguez et al. synthesized BU09059 and BU09057 as soft-drugs, there was no interaction mechanism explanation in details. To study interaction modes in the binding sites, the docking complex structures were compared (Fig. 2). Left pictures in Fig. 2 showed the ligands surrounded by the residues of KOR within 4 Å, and right pictures in Fig. 2 were interaction modes analysis by Ligand Interactions module embedded in Maestro 9.3 [22]. BU09059 was inserted into the pocket by the hydrogen bond and negative charged interaction with the key residue Asp138 (distance 1.916 Å) (Fig. 2A), similar to JDTic in this characteristic V shape [8]. Two hydroxyl groups of BU09059 interact with Tyr139 (distance 2.037 Å) and Cys210 (distance 1.818 Å) also through H-bonds. Meanwhile there are π- π stacking interactions between phenyl groups in two ends of the molecule BU09059 and Trp124 (Tyr139) (Fig. 2A). The side chain of BU09059 ester group interacts with Gly319 (Glycine interaction), Gln115 (polar interaction), Ile316 and Tyr320 (hydrophobic interactions). Two hydroxyl groups of BU09057 have H-bond interactions with Cys210 (distance 2.065 Å) and Tyr312 (distance 1.966 Å) (Fig. 2B). And there was π- π stacking interaction between the phenyl group of BU09057 and Trp124. But there was no H-bond interaction with Asp138 and no side chain interacting with KOR. So the molecular docking shape of BU09057 is not like BU09059 and JDTic.

American Journal of Biological Chemistry 2016; 4(1): 1-5 3

A

B

Fig. 2. Interaction modes of ligands with KOR: (A) BU09059; (B) BU09057. Left pictures: the ligands (carbon atoms: green, nitrogen atoms: blue, oxygen atoms: red) surrounded by the residues (labeled by light gray) of KOR within 4 Å; right pictures: ligand interaction modes analysis (legend in the bottom of Figure).

The binding free energies were calculated by MM/GBSA method (Table 1). The ∆Gbind value of BU09059 (-33.84 kcal/mol) is more favorable than that of BU09057 (-14.56 kcal/mol), which is consistent with the experimental Ki results. To investigate the key residues related to the binding mechanism, the binding free energies between BU09059 (BU09057) and KOR were decomposed into the contribution of each residue. The energy comparisons of residues in mainly binding sites are shown in Fig. 3. There was the significant difference in binding with the residues Asp138 and Tyr139, which show critical impact on JDTic, GNTI and

norBNI binding [8, 26]. The binding energies of BU09059 with Asp138 (H-bond and negative charged interactions) and Tyr139 (H-bond and π-π stacking interactions) are -1.63 and -6.18 kcal/mol, while those of BU09057 are 5.04 (Asp138) and -0.03 (Tyr139) kcal/mol. Because of the strong H-bond interaction, the binding energy of BU09059 with Cys210 is the highest (-9.98 kcal/mol). In addition, although the binding energies of BU09057 with Gln115 (-7.59 kcal/mol, polar interaction) and Tyr312 (-3.48 kcal/mol, H-bond interaction) are more favorable than those of BU09059, BU09059 has more favorable interactions with residues

4 Dan-Qi Zhao et al.: In Silico Molecular Docking Study on the κ-Opioid Receptor Antagonists BU09059 and BU09057

Lys227 (-4.45 kcal/mol, positive charged interaction), Ile290 and Ile294 (-2.74 and -3.67 kacl/mol, hydrophobic interactions). Schmitt et al. also reported that Asp138 and Lys227 were important to the affinities of JDTic fluoroalkyl

derivatives [12]. Therefore, the V shape of BU09059 is more favorable to fit the active pocket of KOR than the shape of BU09057.

Table 1. The binding free energies of BU09059 and BU09057 with KOR (kcal/mol).

Compounds ∆Einternal ∆EElect ∆EVDW ∆GGB ∆GSA T∆S ∆Gbind

BU09059 -0.00 -28.69 -64.44 45.33 0.72 -13.25 -33.84

BU09057 -0.01 -22.05 -44.30 37.17 0.59 -14.04 -14.56

Fig. 3. The comparison of energy decomposition for residues in binding sites of BU09059 (blue) and BU09057 (red). Horizontal axis: the residues in the binding pocket of KOR; Vertical axis: the binding free energies (kcal/mol).

4. Conclusion

The interaction modes of BU09059 and BU09057 with KOR were studied by molecular docking and MM/GBSA calculation methods. The docking scores and ∆Gbind values of BU09059 are more favorable than those of BU09057, which is consistent with the experimental Ki results. There was the significant difference in binding with the key residues Asp138 and Tyr139. The binding energies of BU09059 with Asp138 and Tyr139 are -1.63 and -6.18 kcal/mol, while those of BU09057 are 5.04 and -0.03 kcal/mol. And because of the strong H-bond interaction, the binding energy of BU09059 with Cys210 is -9.98 kcal/mol. These findings explained the high affinity reason of BU09059 and could promote the rational soft-drug design of novel KOR antagonists.

Acknowledgments

This work was granted by Open Fund of Hubei Key Laboratory of Natural Products Research and Development (China Three Gorges University) (No. 2016NP09).

References

[1] SD Glick; IM Maisonneuve; J Raucci; SArcher. Brain Res., 1995, 681, 147–152.

[2] HA Tejeda; TS Shippenberg; R Henriksson. Cell Mol. Life Sci., 2012, 69, 857–896.

[3] WA Carlezon; C Beguin; AT Knol; BM Cohen. Pharmacol. Ther., 2009, 123, 334−343.

[4] MR Bruchas; BB Land; C Chavkin. Brain Res., 2010, 1314, 44–55.

[5] M Metcalf; A Coop. AAPS J., 2005, 7, E704–E722.

[6] C Beguin; BM Cohen. Opiate Receptors and Antagonists From Bench to Clinic, 2009, 99–118, Humana Press, Totawa, NJ.

[7] FI Carroll; WA Carlezon. J. Med. Chem., 2013, 56, 2178−2195.

[8] H Wu; D Wacker; M Mileni; V Katritch; GW Han. Nature, 2012, 485, 327–333.

[9] I Carrol; JB Thomas; LA Dykstra; AL Granger; RM Allen. Eur. J. Pharmacol., 2004, 501, 111–119.

[10] JJ Casal-Dominguez; D Furkert; M Ostovar; L Teintang; MJ Clark. ACS Chem. Neurosci., 2014, 5, 177–184.

[11] S Hu; H Yu; Y Liu; T Xue; H Zhang. J. Mol. Model., 2013, 19, 3087–3094.

[12] S Schmitt; N Colloch; C Perrio. Eur. J. Med. Chem., 2015, 90, 742–750.

[13] HM Berman; J Westbrook; Z Feng; G Gilliland; TN Bhat; H Weissig; IN Shindyalov. Nucleic. Acids Res., 2000, 28, 235–242.

[14] W Sherman; T Day; MP Jacobson; RA Friesner; R Farid. J. Med. Chem., 2006, 49, 534–553.

American Journal of Biological Chemistry 2016; 4(1): 1-5 5

[15] Schrödinger, LLC, New York, 2010, www.schrodinger.com

[16] H Zhong; LM Tran; JL Stang. J. Mol. Graph. Model., 2009, 28, 336–346.

[17] HJ Luo; JZ Wang; NY Huang; WQ Deng; K Zou. J. Chem. Pharm. Res., 2014, 6(3), 1187–1194.

[18] HJ Luo; JZ Wang; WQ Deng; K Zou. Med. Chem. Res., 2013, 22, 4970–4979.

[19] Glide, version 5.6, Schrödinger, LLC, New York, NY, 2010.

[20] MP Jacobson; DL Pincus; CS Rapp; TJF Day; B Honig. Proteins, 2004, 55, 351–367.

[21] The PyMOL molecular graphics system, Version 1.5. Schrödinger, LLC, 2012.

[22] Maestro, version 9.3, Schrödinger, LLC, New York, NY, 2012

[23] PA Kollman; I Massova; C Reyes; B Kuhn; S Huo. Acc. Chem. Res., 2011, 33, 889–897.

[24] I Massova; PA Kollman. Perspect. Drug Discov. Des., 2000, 18, 113–135.

[25] MacroModel, version 9.8, Schrödinger, LLC, New York, NY, 2010.

[26] TA Vortherms; PD Mosie; RB Westkaemper; BL Rot. J. Biol. Chem., 2007, 282, 3146–3156.