In-silico studies of Novel triazole derivatives as inhibitor of 14α demethylase CYP51
Shrusti Rao, Varadaraj Bhat, Fajeelath Fathima, Santosh Kumar, Ruchi Verma*
Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences,
Manipal Academy of Higher Education, Madhav Nagar-576104, Manipal, Udupi, Karanataka, India.
*Corresponding Author E-mail: ruchi.verma@manipal.edu
ABSTRACT:
An antifungal agent is a drug that eliminates fungal pathogens selectively from host with negligible toxicity to the host. Antifungal agents are classified into classes like antibiotics, antimetabolite, azole, and allyl amine. In this present work, the binding mode of novel designed triazole analogues with CYP51 has been investigated by flexible molecular docking. The molecular modeling, which gives the utilization of structural information of CYP51 can enhance the discovery of novel antifungal agents. Further in silico studies were performed in order to see their drug likeness properties and possible mode of interaction with target protein residue. Molecular docking of novel molecules was done by Schrodinger software using following steps like protein preparation, ligand preparation, grid generation, molecular docking. The protein PDB selected was 5V5Z. AZ3 analogue showed the best dock score. The docking value varied from -2.578 to-8.19 for the designed ligands. All the molecules showed good ADME properties and followed Lipinski rule of five. The designed molecules can serve as a lead for future antifungal drug discovery.
KEYWORDS: Molecular docking, triazole, antifungal.
INTRODUCTION:
One of the most common classes of antifungal agents are azoles. These antifungal drugs act by inhibiting CYP51, a necessary enzyme in the biosynthesis of ergo sterol, through a mechanism in which the heterocyclic nitrogen atom (N-4 of triazole) binds to the heme iron atom. Due to the large scale use of azoles severe resistance has developed, which significantly reduced their efficacy. Due to high emergence resistance of antifungal compound there is need to discover the newer antifungal agent which has broader spectrum of activity4,5. Recently computational studies have played a vital role in the design of novel molecules and also this is cost effective hence in this research work we have designed few triazole analogues based on the previous available literature and tried to investigate possible binding mode of azole with CYP51. Further we have performed several in silico studies using schrodinger software in order to check the druggability of the designed ligands. The molecular modeling, which gives the utilization of structural information of CYP51 can accelerate the discovery of novel antifungal agents.
EXPERIMENTAL:
Material and Methods:
In silico studies were carried out by Maestro version 11.4 (Schrodinger Inc.)6, on a hp computer with a Linux Ubuntu 18.04.1 LTS platform, Intel Haswell graphics card, 4 GB RAM and Intel Core i3-4160 processor.
Structure drawing:
In the 2D sketcher the molecules were drawn and it was saved.
Protein preparation7,8:
Protein 5V5Z was downloaded from protein data bank. The protein has co-crystallized ligand Itraconazole with resolution of 2.86Å. The protein was downloaded in PDB format and was preprocessed, optimized, refined by protein preparation wizard of Schrodinger software. All the missing amino acid residues were filled. The unwanted water molecules were removed.
Ligand preparation:
Lig Prep tool was used in order to generate possible conformers of all the drawn molecules at pH 7.0±2.0.
Target validation9:
The prepared protein was split into water and ligand molecule. The co-crystallized ligand was redocked at the same site.
Receptor Grid Generation:
The grid was generated around the ligand binding site by the grid generation tool of the software. This provided the site for the other molecules to be docked.
Molecular Docking10,11:
Docking was done to see the affinity and the interaction between the active site of protein and ligand molecules. The extra precession tool (XP) was used for better accuracy using glide tool12.
MMGBSA calculation13:
MMGBSA calculation for the ligand and protein complexes was performed in order to check the stability of the complex.
In silico drug likeness and ADME prediction14,15,16,17:
Qik prop tool of Schrodinger software was used in order to predict that whether the analogues have favorable ADME properties and whether they follow Lipinski rule of five.
Induced fit docking18,19:
In order to study the ligand binding modes and structural changes in the receptor, induced fit docking tool of Schrodinger software was used.
Reaction based enumeration:
The tool depicted the possible way of synthesis of these analogues.
RESULTS AND DISCUSSION:
Structure of the ligands:
Fig. 1: Triazole analogues
AZ1. R=H, AZ2. R=4-F, AZ3. R=3-F, AZ4. R=2-F, AZ5. R=2-Cl, AZ6. R=3-Cl, AZ7. R=4-Cl, AZ8. R=2-OCH3, AZ9. R=3-OCH3, AZ10. R=4-OCH3, AZ11. R=2-Br, AZ12. R=4-Br, AZ13. R=3-Br, AZ14. R=4-OH, AZ15. R= 4NO2, AZ16. R=3-OH, AZ17. R=2-OH, AZ18. R=2-NO2, AZ19. R=2, 4-OH
Nineteen triazole analogues were designed.
Molecular docking study:
Table 1: Molecular docking results of the ligands
|
Ligand |
Dock Score |
Prime MMGBSAA dG Bind (k.cal/mol) |
Interacting Residue |
|
Itraconazole |
-11.51 |
-62.54 |
Hie 377 |
|
ketoconazole |
-9.26 |
-60.95 |
Tyr 132, Tyr 118, Hie377, Ser 378 |
|
fluconazole |
-6.55 |
-36.12 |
Hie 377 |
|
AZ1 |
-6.44 |
-49.05 |
Tyr 132, Tyr 118 |
|
AZ2 |
-6.61 |
-45.17 |
Phe 232, Phe 380, Hie 377 |
|
AZ3 |
-8.19 |
-50.93 |
Tyr 132 |
|
AZ4 |
-6.79 |
-41.04 |
Tyr 118 |
|
AZ5 |
-7.20 |
-47.79 |
Tyr 132, Tyr 118 |
|
AZ6 |
-7.28 |
-28.53 |
Tyr 118, Hie 377 |
|
AZ7 |
-7.30 |
-31.26 |
- |
|
AZ8 |
-6.67 |
-30.23 |
Tyr 118, Hie 377 |
|
AZ9 |
-7.01 |
-32.00 |
Tyr 118, Tyr 132 |
|
AZ10 |
-6.29 |
-52.15 |
Tyr 118, Hie 377, Phe 308 |
|
AZ11 |
-7.25 |
-31.20 |
Tyr 118, Tyr 132 |
|
AZ12 |
-6.55 |
-47.75 |
Tyr 118, Hie 377 |
|
AZ13 |
-6.21 |
-17.67 |
Tyr 118, Tyr 132 |
|
AZ14 |
-7.01 |
-19.58 |
Tyr 118, Tyr 132 |
|
AZ15 |
-6.11 |
-33.96 |
- |
|
AZ16 |
-6.43 |
-34.22 |
Tyr 118, Hie 377 |
|
AZ17 |
-6.45 |
-47.35 |
Tyr 118, Tyr 132, Gly 303 |
|
AZ18 |
-2.58 |
-30.10 |
Phe 380, Phe 233 |
|
AZ19 |
-6.37 |
-49.97 |
Tyr 118, Gly 303 |
Through the Glide XP docking results it was evident that analogue AZ3 showed good dock score and binding affinity when compared to the other analogues. The co-crystal structure of Itraconazole displayed docking score of -11.51. Standard drugs ketoconazole, fluconazole showed dock score of -9.26, -6.55. The main interacting amino residues were found to be Tyr 118, Tyr 132, Hie 377
Fig. 2: 5V5Z Protein Ramachandran plot
Fig. 3. 2D interaction diagram of Itraconazole and AZ3 with 5V5Z
Figure 3a shows that Itraconazole displayed π-π interaction with HIE 377 residue of the amino acid. The phenyl ring of Itraconazole showed interaction with HIE377 and dock score was found to be 11.511. Figure 3b shows that the AZ3 molecule has π-π stacking with TYR 132 with the residue of amino acid. The dock score of AZ3 was found to be -8.19.
Fig. 4: a): 2D interaction of Ketokonazole with 5V5Z
Fig. 4: b) 2D interaction diagram of Fluconazole with 5V5Z
Fig. 4: displays 2D interaction of Ketokonazole and Fuconazole with 5V5Z
Induced it docking:
The best IFD score for AZ3 was found to be -1007.78 and the additional interactions with HEM amino acid residue was observed for this analogue
In silico drug like properties and ADME properties:
Table 2: In silico drug like properties study
|
Ligand |
QPLogPo/w |
PSA |
Hydrogen bond donor |
Hydrogen bond acceptor |
Molecular weight |
Rule of five |
|
Ketoconazole |
4.38 |
71.57 |
0 |
8 |
531.43 |
1 |
|
Fluconazole |
0.56 |
71.36 |
1 |
6 |
306.27 |
0 |
|
AZ1 |
3.03 |
81.40 |
0 |
5 |
334.37 |
0 |
|
AZ2 |
2.47 |
85.61 |
0 |
6 |
352.35 |
0 |
|
AZ3 |
2.95 |
80.81 |
0 |
6 |
352.36 |
0 |
|
AZ4 |
2.87 |
80.92 |
0 |
6 |
352.36 |
0 |
|
AZ5 |
3.18 |
80.84 |
0 |
6 |
368.82 |
0 |
|
AZ6 |
2.95 |
86.09 |
0 |
5 |
368.82 |
0 |
|
AZ7 |
2.69 |
85.72 |
0 |
6 |
364.40 |
0 |
|
AZ8 |
2.85 |
87.59 |
0 |
6 |
364.40 |
0 |
|
AZ9 |
2.77 |
89.12 |
0 |
6 |
364.40 |
0 |
|
AZ10 |
2.99 |
93.92 |
0 |
6 |
364.40 |
0 |
|
AZ11 |
3.05 |
81.06 |
0 |
6 |
348.40 |
0 |
|
AZ12 |
3.04 |
80.83 |
0 |
6 |
348.40 |
0 |
|
AZ13 |
3.04 |
81.46 |
0 |
6 |
348.40 |
0 |
|
AZ14 |
3.26 |
80.91 |
0 |
6 |
413.22 |
0 |
|
AZ15 |
3.29 |
80.83 |
0 |
6 |
413.27 |
0 |
|
AZ16 |
2.75 |
85.78 |
0 |
6 |
413.27 |
0 |
|
AZ17 |
2.28 |
103.41 |
1 |
6 |
350.37 |
0 |
|
AZ19 |
1.69 |
124.45 |
2 |
7 |
366.37 |
0 |
Table 3: Predicted ADME properties by Qik prop software
|
Ligand |
aQPPCaco |
bQPlogBB |
cQPlogKhsa |
dQPlogHERG |
eQPLogS |
fCNS |
|
|
Ketoconazole |
859.77 |
0.305 |
0.313 |
-5.112 |
-4.402 |
-1 |
|
|
Fluconazole |
807.39 |
-0.596 |
-0.479 |
-4.433 |
-2.015 |
0 |
|
|
AZ1 |
896.593 |
-0.61 |
0.086 |
-5.711 |
-4.352 |
0 |
|
|
AZ2 |
897.426 |
-0.51 |
0.132 |
-5.593 |
-4.726 |
0 |
|
|
AZ3 |
857.026 |
-0.52 |
-0.026 |
-5.51 |
-4.35 |
0 |
|
|
AZ4 |
875.22 |
-0.544 |
-0.042 |
-5.491 |
-4.149 |
0 |
|
|
AZ5 |
943.851 |
-0.453 |
-0.041 |
-5.56 |
-4.598 |
0 |
|
|
AZ6 |
880.098 |
-0.462 |
0.051 |
-5.553 |
-4.716 |
0 |
|
|
AZ7 |
491.934 |
-0.56 |
-0.053 |
4.251 |
-3.635 |
0 |
|
|
AZ8 |
919.829 |
-0.693 |
-0.077 |
-5.65 |
-4.22 |
0 |
|
|
AZ9 |
867.271 |
-0.71 |
-0.099 |
-4.115 |
-5.553 |
-1 |
|
|
AZ10 |
501.362 |
-0.765 |
-0.202 |
-4.189 |
-2.991 |
-1 |
|
|
AZ11 |
1010.208 |
-0.765 |
-0.207 |
-4.189 |
-2.991 |
-0 |
|
|
AZ12 |
858.615 |
-0.658 |
-0.1 |
-5.574 |
-4.584 |
0 |
|
|
AZ13 |
868.106 |
-0.653 |
0.099 |
-5.571 |
-5.571 |
0 |
|
|
AZ14 |
959.705 |
-0.436 |
0.064 |
-5.579 |
-4.99 |
0 |
|
|
AZ15 |
878.905 |
-0.454 |
0.077 |
-5.586 |
-4.837 |
0 |
|
|
AZ16 |
897.644 |
-0.456 |
0.24 |
-5.671 |
-5.237 |
0 |
|
|
AZ17 |
265.955 |
-1.217 |
0.058 |
-5.523 |
-4.39 |
-2 |
|
|
AZ19 |
75.104 |
-1.795 |
-0.056 |
-5.115 |
-4.239 |
-2 |
|
aQPPCaco= Predicted apparent Caco-2 cell permeability in nm/sec. Caco-2 cells are a model for the gutblood barrier.
bQPlogBB= Predicted brain/blood partition coefficient, cQPlogKhsa= Prediction of binding to human serum albumin, dQPlogHERG= Predicted IC50 value for blockage of HERG K+ channels, eQPlogS= Hydrogen bond donar, fCNS= Predicted central nervous system activity
All the analogues displayed drug like properties and followed Lipinski rule of five. QplogPo/w property shows partition coefficient of molecule in octanol/ water. The range is from -2.0-6.5. QPlogpo/w of ketoconazole and fluconazole was found to be 4.381 and 0.56.QplogPo/w of analogue of azole was found to be in between 1.69-3.29. CNS is one of property of ADME in which it describe penetration of molecule into CNS and has range of -2 to 2, in which the -2 is in the inactive from and +2 is in active from. Ketoconazole CNS value was found to be -1 which in inactive state and unable to enter into CNS, Fluconazole had the CNS value of 0. All the analogues were found to be inactive for CNS activity. QPlogBB is ADME property depicts the capability of the molecule to cross blood/brain barrier and has range of -3.0-1.21. All the analogue of azole has the QPlogBB in range of –0.61 to -1.795. All the analogues showed acceptable ADME properties.
Reaction based enumeration:
The possible mode of synthesis was found to be by a) amination reaction b) negishi reaction c) Suzuki coupling reaction
Fig. 5: a) Amination reaction b) Negishi reaction c) Suzuki coupling reaction
CONCLUSION:
One of analogue of azole has showed good dock score i.e. above -8.0. Further our focus will be to modify the structure by incorporating various substitutions, synthesize the active analogues and evaluate them by in-vitro and in-vivo studies. The study will help in identifying potent antifungal agent.
ACKNOWLEDGEMENT:
The authors would like to acknowledge the Schrodinger software facility provided by Manipal College of Pharmaceutical Sciences.
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Received on 14.12.2019 Modified on 14.03.2020
Accepted on 28.04.2020 © RJPT All right reserved
Research J. Pharm. and Tech. 2020; 13(12):5806-5810.
DOI: 10.5958/0974-360X.2020.01012.4