Research Article | Volume: 8, Issue: 12, December, 2018

Computational approach toward targeting the interaction of porphyrin derivatives with Bcl-2

Muhammad Arba Yamin Sunandar Ihsan Daryono Hadi Tjahjono   

Open Access   

Published:  Dec 30, 2018

DOI: 10.7324/JAPS.2018.81208

Bcl-2 protein has been identified as a potential therapeutic target of cancer. Many inhibitors which target the protein have been developed. Here, we investigated the interaction between porphyrin conjugated with anthraquinone group with a variation on meso substituent either pyrazolium or pyridine, and Bcl-2 using computational molecular docking and molecular dynamics simulation. Molecular docking was performed using AutoDock Vina, while molecular dynamics (MD) simulation of 50 ns was conducted using AMBER16. Our study indicates that the designed compounds interacted with crucial residues of Bcl-2 active site. Prediction of binding free energy by molecular mechanics-Poisson Boltzmann solvent accessible surface area method shows that mono-H2PzP-AQ has a comparable affinity with that of cognate ligand, 1XJ. Additionally, porphyrin binding was mainly supported by electrostatic and van der Waals energies.

Keyword:     Bcl-2 cancer MM-PBSA molecular docking molecular dynamics simulation porphyrin.


Arba M, Yamin, Ihsan S, Tjahjono DH. Computational approach toward targeting the interaction of porphyrin derivatives with Bcl-2. J App Pharm Sci, 2018; 8(12): 060–066.

Copyright: © The Author(s). This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Apoptosis or a programmed cell death plays a key role in controlling cell development and tissue homeostasis. Its dysregulation is well known as a hallmark of cancer. Apoptosis that occurs through the mitochondrial pathway was regulated by B cell lymphoma-2 (Bcl-2) family proteins by engineering the integrity of the outer mitochondrial membrane. Bcl-2 proteins consist of antiapoptotic proteins and proapoptotic proteins. In a cancer cell, apoptosis was averted through dysregulation antiapoptotic proteins, including Bcl-2 resulting in their aberrant expression which alters the balance between proapoptotic and antiapoptotic members of the Bcl-2 family leading to preserved cancer cell survival (Ashkenazi et al., 2017). Therefore, targeting antiapoptotic Bcl-2 was considered a rational strategy to restore apoptosis function for cancer treatment.

Several small molecules with structural diversity are reported as Bcl-2 inhibitors such as Navitoclax, Venetoclax, and Obatoclax (Davids and Letai, 2013; Lin et al., 2016; Salem et al., 2016; Trudel et al., 2007; Vervloessem et al., 2018). Navitoclax has been approved by Food and Drug Administration (FDA) for curing chronic lymphocytic leukemia, the most frequent leukemia found in adults, and Obatoclax for curing different types of cancer. Despite their advantages, several miserable side effects were observed associated with the clinical use of those agents. The thrombocytopenia was found to be a major dose-limiting toxicity for Navitoclax, particularly in a single- agent setting (Ashkenazi et al., 2017). Meanwhile, the side effects of Obatoclax are largely neurological, including fatigue, euphoria, somnolence, ataxia, diarrhea, nausea, thrombocytopenia, anemia, and pneumonia (Wu et al., 2017). Those facts prompted tremendous efforts worldwide for finding more potent Bcl-2 inhibitors which have fewer side effects.

Porphyrins are well known for their crucial function such as transporting oxygen (haem) and mediating photosynthesis process (chlorophyll) (Ptaszyńska et al., 2018). Porphyrin has been widely used in therapeutic applications. Variations of porphyrin structure have been well reported both through periphery substitution and central nitrogen metalation in order to tune their therapeutic values (Carneiro et al., 2018). Porphyrin with positive charge was also reported to increase interaction with a molecule bearing negatively charged groups such as DNA and RNA in cancer cells. Porphyrin bearing pyridyl and pyrazolium groups attached to the meso position have attracted much interest for their therapeutic values. It was reported that the porphyrin bearing either pyridyl or pyrazolium as periphery substituents increases interaction with cancer cells (Carneiro et al., 2018).

Here, we report the Bcl-2 binding of porphyrin conjugated with anthraquinone group by using the computational approach. Rapid advances in computational approach in drug discovery have been well documented. It saves time and cost in the drug discovery process. In this study, variation on the structure of porphyrin was made by attaching either pyridyl or pyrazolium groups in the meso position. It is found that porphyrin with a mono substituent, H2PyP-AQ, has a comparable affinity with the cognate ligand of Bcl-2.


Macromolecule and ligand preparation

The structure of Bcl-2 was taken from the Protein Data Bank, PDB id 4LVT, resolution 2.05 Å (Souers et al., 2013). Six porphyrin derivatives were built using Gaussian 09 (Frisch et al., 2009) following our previous procedure (Arba et al., 2017). The porphyrin built are mono-H2PyP-AQ, bis-H2PyP-AQ, tris-H2PyP-AQ, mono-H2PzP-AQ, bis-H2PzP-AQ, and tris-H2PzP-AQ. Figure 1 shows the 2D structure of porphyrin derivatives. The optimized ligand was then used for further docking study.

Molecular docking and MD simulation

Molecular docking was performed by using AutoDock Vina (Trott and Olson, 2010). Protein and ligand were prepared using AutoDockTools and saved as “PDBQT” files. A config file was prepared containing the binding site box which was in the center of a cognate ligand of Bcl-2 with the dimension of 46 × 46 × 46 Å on x y z directions. The docked conformation with high binding energy was chosen for further MD simulation which was performed using AMBER16 software (Case et al., 2005; Salomon-Ferrer et al., 2013). For MD simulation, each protein and ligand was parameterized with ff14SB (Maier et al., 2015) and GAFF (Wang et al., 2004) force fields as well as AM1-BCC (Jakalian et al., 2002). The protein-ligand complex was solvated with the truncated octahedron TIP3P water model with a minimum distance of 10 Å around the system. System electroneutralization was achieved by the addition of Na+ counterions using Leap module of AMBER16.

Before proceeding to MD simulation, each system was minimized using Sander module of AMBER16. Minimization was performed by following our previous procedure (Arba et al., 2017). System was then heated from 0 to 100, 100 to 200, and 200 to 300 K, respectively. Each was done for 50 ps with a timestep of 0.0005 ps and backbone atom restraints for protein (k = 5 kcal mol−1 Å−2). Each system was then equilibrated at 300 K during two 100 ps with restraints (k = 5 and k = 3 kcal mol−1 Å−2), respectively, which was followed by 100 ps constant-temperature, constant-pressure (NPT) ensemble equilibration without restraint. The equilibrated system was then subjected to 50 ns MD production in NPT ensemble by using pmemd.cuda module in AMBER16 (Salomon-Ferrer et al., 2013). All other MD settings were adopted from our previous work (Arba et al., 2017). Analysis was performed by using CPPTRAJ module (Roe and Cheatham III, 2013) of AMBER16, while visualization was achieved with the help of Visual Molecular Dynamics software (Humphrey et al., 1996).

Binding free energy calculations

Our last in silico protocol was the calculation of the binding free energy, which was performed using the Molecular Mechanics-Poisson Boltzmann solvent accessible surface area (MM-PBSA) method (Arba et al., 2018a; 2018b; Kollman et al., 2000). The binding energy was calculated using 200 snapshots taken from 20 to 40 ns simulation trajectories with the python modules of AMBER16 (Miller et al., 2012). The MM-PBSA method for binding energy calculation was based on an assumption that the binding free energy of complex (ΔGbind) is the difference between the free energies of the complex (ΔGcomplex) and the unbound receptor (ΔGrec) and the free ligand (ΔGlig), as shown in the following equations:

Figure 1. The 2D structure of porphyrin derivatives.

[Click here to view]

â–µ G bind = G complex G rec G ligand [ 1 ]

= â–µ E MM + â–µ G sol T â–µ S [ 2 ]

â–µ E MM = â–µ E bond + â–µ E angle + â–µ E torsion + â–µ E vdw + â–µ E EEL [ 3 ]

â–µ G sol = â–µ G PB + â–µ G SA

The binding energy (ΔGbind) was a summation of the gas phase binding energy (ΔEMM), solvation free energy (ΔGsol), and the conformational entropy change upon ligand binding at temperature T (TΔS), respectively. Furthermore, bond energy, angle energy, torsion energy, van der Waals energy, and electrostatic energy, all constitute the ΔEMM, while polar contribution to solvation free energy (ΔGPB) was calculated by solving the Poisson-Boltzmann (PB) equation using a grid size of 0.5 Å, and the non-polar contribution (ΔGSA) which was calculated using the solvent accessible surface area (SASA) with solvent-probe radius set to 1.4 Å, both of which constitute ΔGsol.


To investigate the putative interaction mechanism of porphyrin derivatives with Bcl-2, molecular docking was conducted by using AutoDock Vina. Each porphyrin derivative was docked to the protein allowing the maximum torsion of each compound.

Firstly, the internal validation phase was performed by re-docking 1XJ over the Bcl-2 active site. The root mean square deviation (RMSD) of heavy atoms of re-docked and co-crystallized conformations of 1XJ was utilized to assess the reliability of the docking protocol. The result shows that the RMSD values of the two conformations were 0.77 Å, indicating the validated performance of docking (Goodsell et al., 1996; Ruslin et al., 2017). In the 1XJ docked pose, two hydrogen bonds (hbond) were formed between ligand and Arg104 and Gly142 residues of Bcl-2. Figure 2 depicts the docked and X-ray crystallographic poses of 1XJ and interacting 1XJ over the active site of Bcl-2.

Furthermore, porphyrin binding to the Bcl-2 was predicted using the validated docking protocol. Several interactions between porphyrin derivatives with Bcl-2 occurred such as hydrogen bond (hbond), hydrophobic and electrostatic interactions. Conventional hbond took place with Tyr105 as observed in bis-H2PzP-AQ and tris-H2PzP-AQ. The Glu133, Arg136, and Gly142 were also detected in the conventional hbond in bis-H2PyP-AQ, tris-H2PyP-AQ, and mono-H2PyP-AQ, while Arg143 and Leu198 were, observed in tris-H2PzP-AQ and mono-H2PyP-AQ, respectively. In addition, C−H−O hbonds were observed with Ala146 in bis-H2PyP-AQ and tris-H2PyP-AQ, while Leu134 was detected in bis-H2PyP-AQ binding.

Hydrophobic interactions of porphyrin derivatives consisted of pi–pi stacking, pi–pi T-shaped stacking, and pi-alkyl interactions. Pi–pi stackings were seen between mono-H2PzP-AQ and tris-H2PzP-AQ with Tyr105, while pi–pi T-shaped stackings were observed between mono-H2PyP-AQ, mono-H2PzP-AQ, and bis-H2PyP-AQ with each Tyr105 and Phe101, respectively. Meanwhile, tris-H2PzP-AQ and tris-H2PyP-AQ established two pi–pi T-shaped stackings with both Phe101 and Tyr105. Further, pi-alkyl interactions were found with Ala146 in tris-H2PzP-AQ, tris-H2PyP-AQ, bis-H2PyP-AQ, and mono-H2PzP-AQ, while those with Leu134 were observed with tris-H2PyP-AQ, bis-H2PyP-AQ, and mono-H2PzP-AQ. The ligand mono-H2PzP-AQ and mono-H2PyP-AQ share similar interacting residue with Arg143. Additionally, Ala97, Val145, and Leu198 were bound to mono-H2PyP-AQ, Met112 bound to mono-H2PzP-AQ, and Val130 to bis-H2PyP-AQ.

Figure 2. (a) The superimposition of docked and X-ray crystallographic poses of 1XJ; (b) the docked conformation of 1XJ in the binding site of Bcl-2.

[Click here to view]

In addition, pi-cationic interactions were observed between tris-H2PzP-AQ and Arg143, as well as tris-H2PyP-AQ and Arg136, while pi-anion interaction was observed between tris-H2PyP-AQ and Asp108. Figure 3 shows the interaction of each porphyrin derivative with Bcl-2.

Molecular dynamics simulation

Figure 4 depicts RMSD for all complexes. It shows that each complex has reached stability as indicated by stable RMSD values under 3 Å. The RMSD value of 1XJ (red-colored) tends to be slightly higher than those of porphyrin, indicating their better stability.

Figure 3. The interaction of (a) mono-H2PzP-AQ, (b) mono-H2PyP-AQ, (c) bis-H2PzP-AQ, (d) bis-H2PyP-AQ, (e) tris-H2PzP-AQ, and (f) tris-H2PyP-AQ, each with Bcl-2.

[Click here to view]

Additionally, fluctuation on amino acid residues was monitored through the root mean square deviation (RMSF) plot. Figure 5 depicts the RMSF plot versus residue number of amino acid. It shows that all complex has a similar fluctuation of amino acids during MD simulation. Generally, the amino acid fluctuation was under 4 Å indicating the stability of the complex. High fluctuation was observed in the Pro201 (Pro139) corresponding to carboxy end of the protein.

MM-PBSA binding free energy

The binding energy was predicted by using MM-PBSA calculation and the result is presented in Table 1, showing clearly that favorable energy contributions originated from van der Waals (ΔEVDW) and nonpolar desolvation energies (ΔEPBSUR), due to hydrophobic character of porphyrin macrocycle. The favorable contribution was also observed from electrostatic energy (ΔEELE), while net unfavorable electrostatic contribution was unfavorable due to positive polar desolvation energy (ΔEPBCAL). The electrostatic energy may be due to several electrostatic interactions which were described previously, including hydrogen bond and pi-cationic interactions. Furthermore, it is observed that porphyrin hybrid having one meso substituent, i.e., mono-H2PzP-AQ, had most negative binding energy (ΔGpred = −51.81 ± 4.75 kcal/mol), only slightly different from that of the cognate ligand with a binding energy of −54.72 ± 3.72 kcal/mol. This indicates the potential of that compound to be further developed in drug discovery.

Figure 4. RMSD value of protein heavy atom during MD simulation for (a) 1XJ (red), bis-H2PyP-AQ (green), and bis-H2PzP-AQ (blue); (b) 1XJ (red), mono-H2PyP-AQ (green), and mono-H2PzP-AQ (blue); and (c) 1XJ (red), tris-H2PyP-AQ (green), and tris-H2PzP-AQ (blue).

[Click here to view]

Figure 5. RMSF change during 50-ns MD simulation for (a) 1XJ (red), bis-H2PyP-AQ (green), and bis-H2PzP-AQ (blue); (b) 1XJ (red), mono-H2PyP-AQ (green), and mono-H2PzP-AQ (blue); and (c) 1XJ (red), tris-H2PyP-AQ (green), and tris-H2PzP-AQ (blue).

[Click here to view]

Table 1. The binding free energies and the corresponding components (kcal/mol).

[Click here to view]


In conclusion, porphyrin derivatives were able to interact with Bcl-2. The interaction of porphyrin was mainly supported by electrostatic and van der Waals energies. Bcl-2 binding preferred porphyrin with less number of meso substituents. The designed compounds, i.e., mono-H2PzP-AQ, can potentially be used as a Bcl-2 inhibitor as shown by binding free energy predicted by MM-PBSA, which is comparable with that of cognate ligand, 1XJ.


The authors wish to thank Ministry of Research, Technology and the Higher Education Republic of Indonesia for supporting this research through Hibah Penelitian Dasar Unggulan Perguruan Tinggi (PDUPT) Tahun 2018.


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