In silico designing of novel triazole derivatives as substituent for resistant fungicides Dissertation SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF TECHNOLOGY IN INFORMATION TECHNOLOGY (SPECIALIZATION ‐ BIOINFORMATICS) Submitted by SARIKA SAHU IBI2006010 M.Tech IT (Specialization – Bioinformatics) Under the Supervision of Prof. Krishna Misra Ph. D., FNASc Emeritus Professor in Chemistry Department University of Allahabad Allahabad - 211 002 & Coordinator, Indo-Russian Centre for Biotechnology, Indian Institute of Information Technology-A , Deoghat Jhalwa campus, Allahabad INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD
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In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD8
5.2 DDoocckkiinngg rreessuulltt ooff vviirrttuuaall ssccrreeeenniinngg wwiitthh CCYYPP5511 61 5.2.1 Result of virtual screening for CYP51 protein of Blumeria
graminis 61
5.2.2 Result of virtual screening for CYP51 protein of Aspergillus fumigatus 62
6 DDiissccuussssiioonnss 65
7 CCoonncclluussiioonn 67
8 FFuuttuurree wwoorrkk 68
9
RReeffeerreenncceess
69
In silico designing of novel triazole derivatives as substituent for resistant fungicides
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LLiisstt ooff ffiigguurreess
FFiigguurree 11..
Structure of 1, 2, 4-triazole and 1, 2, 3-triazole
1177
FFiigguurree 22.. synthesis of ergosterol in fungi 1188
FFiigguurree 33 inhibition of 14-α demethylase by azole drug 1199
FFiigguurree 44.. synthesis of ergosterol 2200
FFiigguurree 55.. symptoms of Blumeria graminis in wheat plant 2222
FFiigguurree 66.. Aspergillus fumigatus 2244
FFiigguurree 77.. Ramachandran plot for sterol 14-α demethylase of Blumeria
graminis 3355
FFiigguurree 88.. 3-D structure of sterol 14 α−demethylase of Blumeria graminis modeled by MODELER9.0 program
3366
FFiigguurree 99.. Ramachandran plot for sterol 14 α−demethylase of Aspergillus fumigatus
3366
FFiigguurree 1100.. 3-D structure of sterol 14 α− demethylase of Aspergillus fumigatus modeled by MODELER9.0 program
3377
FFiigguurree 1111.. Main programs in DOCK suite 4422
FFiigguurree 1122.. Bar graph showing energy comparison of top 10 ligands on docking 6633
FFiigguurree 1133 Docking Result of virtual screening showing H-bond and Figure 13b triazole like molecule (Blumeria graminis)
6644
FFiigguurree 1144 Docking Result of virtual screening showing H-bond and Figure 13b triazole like molecule (Aspergillus fumigatus)
6644
In silico designing of novel triazole derivatives as substituent for resistant fungicides
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List of tables
TTaabbllee11..
Name and structure of proposed ligand
38
TTaabbllee 22.. Dock score of Blumeria graminis 48
TTaabbllee 33.. Dock score of Aspergillus fumigatus 57
TTaabbllee 44.. moldock score of virual screening Blumeria graminis 61
TTaabbllee 55.. moldock score of virual screening Aspergillus fumigatus 62
In silico designing of novel triazole derivatives as substituent for resistant fungicides
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Abbreviations
CYP51
RMSD
14-α sterol demethylase
Root Mean Squared Deviation
PDB Protein Data Bank
MW
H-bond
Molecular Weight
Hydrogen bond
HBa Hydrogen Bond Acceptor
HBd
DMI's
HPR
ITC
HIV
Hydrogen Bond Donor
Demethylation inhibitors
Host Plant Resistance
Itraconazole
human immunodeficiency virus
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Abstract
Sterol 14α demethylase (CYP51) is an enzyme play important role in metabolism of endogenous
and xenobiotic substances. The ergosterols provide membrane structure, modulation of
membrane fluidity, and possibly control of some physiologic events. Inhibition of this vital
enzyme in the ergosterol synthesis cycle leads to the declining of ergosterol in the cell membrane
and increase of toxic intermediate sterols, causing increased membrane permeability and
inhibition of fungal growth.
As the site of action is very specific so most of the fungus has become resistant to the triazole
derivatives. Because of the resistant developed in fungi several triazoles fungicides have
disappeared from the market and they no longer provide benefit or advantage in a disease control
program. So, we have to develop some novel triazole derivative which fights against the fungus
which have become resistant. We screened some novel triazole drugs whose synthesis is feasible
in laboratory. It would be good for resistant variety of fungal species before
engaging in costly experiments.
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1. Introduction
1.1 Motivation
Fungi are important multicellular organisms [1], some of them have economic value and others
participate in biological ecosystem. These degrade the dead organic material, the cycle of this
process is repeated through ecosystems. Most advanced (like mocot or dicot) plants need
association of fungi to grow such as Mycorrhizae that reside in the plant roots and provide
essential nutrients to the plant. Other useful fungi provide foods and antibiotic drugs (such as
penicillin). Apart from this beneficial aspect there are pathogenic fungus which causes number of
diseases in plants, human and animals. The absence of chlorophyll makes fungi totally dependant
on host and the similarity between the membranes of fungi and plant/animal is the main cause
why fungal infections are so stubborn. [2].
There are number of different type of fungicides available, the demethylation inhibitors (DMI's)
one of them best fungicides. It has many advantageous features; including far above the ground
fungicidal activity, very low toxicity to other organisms, defending and healing properties and
compatibility with integrated pest management. They share a related mode of action inhibiting
the formation of sterols, such as ergosterol, which are important in fungal cell walls. Each
compound may act in a slightly different part of the biochemical pathway to make sterols but the
result is a similar spectrum of activity against diverse diseases [3].due to the resistance of fungi to
some fungicides, it fails to control the fungal disease. The sterol demethylase (DMI's) have
become one of the important groups of fungicides and are being very much watched for symbols
that resistance might increase or developed. They are chemically diverse groups which all
prevent the same demethylation step in the synthesis of ergosterol, a critical substance of cell
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walls in many fungal organisms [4]. The reason for resistance of a plant to certain fungicides is
due to it’s overuse or misuse in some way or the other. This effects the genetic make up of the
plant which is inherited by the next generations.[5].
Computer aided drug design like structure based approach can be used
effectively against resistant fungal species. To develop novel drug which bind to target and
inhibit the synthesis of cell wall of the fungi. We have designed computationally new triazole
derivative which bind to active site of the receptor lead to inhibit the synthesis of cell wall.
This project will help in designing the new triazole derivative’s drug. There is need for designing
new fungicides agents as the problem of resistance developed in the strains of the pathogens and
also sensitivity of some patients with some drugs. Particularly this project is important for me as
it is giving me opportunity to work on live projects whose predictions will be validated in wet-
lab with collaborations with some Laboratories around the world.
1.2 Problem statement
Fungicides have been used to control number of plant diseases for over one hundred and fifty
years. The triazoles are the most important group of fungicides that are available to cereal
growers. They are used to control many diseases of cereals. Single genetic changes usually
produce highly resistant strains of pathogens [6]. In the 1800's unpleasant incident took in
Ireland's, killed 1.5 million people, ¼ of Ireland's total population. The crop was vanished by a
fungal disease. There are at least 50,000 diseases of crop plants. Still New diseases are revealed
every year. About 15% of the total U.S. crop production is lost annually to infectious diseases
despite improved cultivars and disease control techniques.
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Disease-causing organisms also know as pathogen, they reproduce and mutate quickly. These
organisms acquired genetic resistance to chemical controls and have the capability to pass on a
disease to new hybrids [7].
Currently most of the fungal organisms have become resistant to marketed triazole derivatives
because their site of action (active site) has mutated loosing the sensitivity for different triazoles.
Some of the triazole based drugs thus disappeared from the market. In this project work we try to
develop novel triazole derivative which effective against that fungal organism which becomes
resistant. They become resistance because the target sites become change so, we have to find
new target site and related drug. The most of the triazole derivative are toxic to rat and rabbit and
other mammalian [8]. These are marketed triazole drug Ketoconazole, Itraconazole, Fluconazole,
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Triazole like drug also taken from Zinc Database for virtual screening, then filter
these molecule on the basis of drug like properties.
Drug-like filter MIN_MOLWT 200 "Minimum molecular weight" MAX_MOLWT 600 "Maximum molecular weight" MIN_NUM_HVY 15 "Minimum number of heavy atoms" MAX_NUM_HVY 35 "Maximum number of heavy atoms" MIN_RING_SYS 0 "Minumum number of ring systems" MAX_RING_SYS 5 "Maximum number of ring systems" MIN_RING_SIZE 0 "Minimum atoms in any ring system" MAX_RING_SIZE 20 "Maximum atoms in any ring system" MIN_CON_NON_RING 0 "Minimum number of connected non-ring atoms" MAX_CON_NON_RING 15 "Maximum number of connected non-ring atoms" MIN_FCNGRP 0 "Minimum number of functional groups" MAX_FCNGRP 18 "Maximum number of functional groups" MIN_UNBRANCHED 0 "Minimum number of connected unbranched non- Ring atoms" MAX_UNBRANCHED 6 "Maximum number of connected unbranched non- Ring atoms" MIN_CARBONS 7 "Minimum number of carbons" MAX_CARBONS 35 "Maximum number of carbons" MIN_HETEROATOMS 2 "Minimum number of heteroatoms" MAX_HETEROATOMS 20 "Maximum number of heteroatoms" MIN_Het_C_Ratio 0.10 "Minimum heteroatom to carbon ratio" MAX_Het_C_Ratio 1.0 "Maximum heteroatom to carbon ratio" MIN_HALIDE_FRACTION 0.0 "Minimum Halide Fraction" MAX_HALIDE_FRACTION 0.5 "Maximum Halide Fraction" MIN_ROT_BONDS 0 "Minimum number of rotatable bonds" MAX_ROT_BONDS 20 "Maximum number of rotatable bonds" MIN_RIGID_BONDS 0 "Minimum number of rigid bonds" MAX_RIGID_BONDS 35 "Maximum number of rigid bonds" MIN_HBOND_DONORS 0 "Minimum number of hydrogen-bond donors" MAX_HBOND_DONORS 6 "Maximum number of hydrogen-bond donors" MIN_HBOND_ACCEPTORS 0 "Minimum number of hydrogen-bond acceptors" MAX_HBOND_ACCEPTORS 8 "Maximum number of hydrogen-bond acceptors" MIN_LIPINSKI_DONORS 0 "Minimum number of hydrogens on O & N atoms" MAX_LIPINSKI_DONORS 5 "Maximum number of hydrogens on O & N atoms" MIN_LIPINSKI_ACCEPTORS 0 "Minimum number of oxygen & nitrogen atoms" MAX_LIPINSKI_ACCEPTORS 10 "Maximum number of oxygen & nitrogen atoms" MIN_COUNT_FORMAL_CRG 0 "Minimum number formal charges" MAX_COUNT_FORMAL_CRG 3 "Maximum number of formal charges"
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MIN_SUM_FORMAL_CRG -2 "Minimum sum of formal charges" MAX_SUM_FORMAL_CRG 2 "Maximum sum of formal charges" MIN_CHIRAL_CENTERS 0 "Minimum chiral centers" MAX_CHIRAL_CENTERS 4 "Maximum chiral centers" MIN_XLOGP -5.0 "Minimum XLogP" MAX_XLOGP 6.0 "Maximum XLogP" #choices are insoluble<poorly<moderately<soluble<very<highly MIN_SOLUBILITY moderately "Minimum solubility" PSA_USE_SandP false "Count S and P as polar atoms" MIN_2D_PSA 0.0 "Minimum 2-Dimensional (SMILES) Polar Surface Area" MAX_2D_PSA 150.0 "Maximum 2-Dimensional (SMILES) Polar Surface Area" AGGREGATORS true "Eliminate known aggregators" PRED_AGG true "Eliminate predicted aggregators" #secondary filters (based on multiple primary filters) GSK_VEBER true "PSA>140 or >10 rot bonds" MAX_LIPINSKI 1 "Maximum number of Lipinski violations" MIN_ABS 0.5 "Minimum probability F>10% in rats" PHARMACOPIA true "LogP > 5.88 or PSA > 131.6" ALLOWED_ELEMENTS H, C, N, O, F, S, Cl, Br ELIMINATE_METALS Sc,Ti,V,Cr,Mn,Fe,Co,Ni,Cu,Zn,Y,Zr,Nb,Mo,Tc,Ru,Rh,Pd,Ag,Cd
\
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3.4. DOCK
DOCK addresses the problem of "docking" molecules to each other. In general, "docking" is the
identification of the low-energy binding modes of a small molecule, or ligand, within the active
site of a macromolecule, or receptor, whose structure is known.
3.4.1. Dock working principle
Dock software is based on the force field energy scoring. This is including van der waals Force,
molecular mechanics and electrostatic energy. [42].
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Receptor coordinate
Sphegen Site characterization Negative image of the site
Grid Precompute score grid for rapid evaluation
Dock Screen molecule for complementarity With receptor
Ligand coordinate
Figure11. Main programs in DOCK suite
3.4.2Preparing Molecules for Docking The purpose of this document is to describe the steps required to prepare molecules as input for a DOCK run that attempts to predict the orientation of a ligand in an active site [43]. 3.4.2.1. Examine the target file The first step in any docking project is selecting the file that will be used for the structure of the target. This file contains Cartesian coordinates for the protein; crystallographic waters. Each of these components must be dealt with separately before DOCK can be used. 3.4.1.2. Prepare the receptor file
• Open the target file (in pdb format) in Chimera. • Use Dock Prep tool to complete receptor preparation. • Examine warnings from Dock Prep procedure
3.4.1.3. Prepare the ligand file a) Open the ligand file in Chimera e) Add hydrogen Calculate charges using the Chimera Add Charge tool The Add Charge tool is a call to the antechamber program. Antechamber is a
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Set of auxiliary programs for molecular mechanic (MM) studies. (C) Save the molecule in mol2 format
3.5. Sphgen
Sphgen identifies the active site, and other sites of interest, and generates the sphere centers that
fill the site. The purpose of this document is to describe the steps required to prepare active site
spheres for a DOCK run [44].
3.5.1. Generate the molecular surface of the receptor
The molecular surface of the target is generated, According to the Richards and Connolly, the
surface of protein is determined by rolling a drop of water molecular whose radius about 1.4 A0
over the surface of protein which forms a van der waals force. This method used for calculating
each of sphere size.
3.5.2. Generate the spheres surrounding the receptor
Sets of overlapping spheres are used to create a negative image of the surface invaginations of the target. To generate spheres from the molecular surface and the normal vectors, the program sphgen that is distributed as an accessory with DOCK is used. Spheres are calculated over the entire surface, producing approximately one sphere per surface point.
3.5.3. Select a subset of spheres to represent the binding site(s)
Use the largest cluster generated by sphgen.
3.6. Grid
This tutorial describes the generation of the grid used for grid-based scoring in DOCK.
3.6.1. Creating a box around the active site
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The interactive program showbox is used to visualize and define the location and size of the grid
to be calculated using grid.
3.6.2. Generating the Grid
Grid creates the grid files necessary for rapid score evaluation in DOCK. Two Types of scoring
are available: contact and energy scoring.Within the DOCK suite of programs, the program
DOCK matches spheres (generated by sphgen) with ligand atoms and uses scoring grids (from
grid) to evaluate ligand orientation
3.7. Rigid and Flexible Ligand Docking 3.7.1. Rigid Ligand Docking According to the rigid ligand docking, ligand should be completely rigid throughout the process.
The main reason to minimize the energy. Rigid docking is applied only in scientific setting
means that ligands are already expanded conformationally no further needed to expand it.
3.7.2. Flexible Ligand Docking Flexible docking allowed the ligand to be flexible. In this procedure ligand rearrange there
conformation according to the response to there receptor .A ligand can acquired different number
of conformation. This type of docking excluded the double bond character to maintain the energy.
The location of each flexible bond is used to partition the molecule into rigid segments. A
segment is the largest local set of atoms that contains only non-flexible bonds.
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Target protein
Target Search
TTaarrggeett PPDDBB
ffoouunndd??
Yes Download PDB
No
Model the protein
IIss aaccccuurraaccyy >>8855%%??
No Loop Modeling
YES
4. Flow chart
Accept structure
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LLiiggaanndd
Accept Target structure
Triazole like structure download form Zinc
Database
Filter the Ligand
Prepare the target for docking
Prepare Ligand for Docking
DDOOCCKK
Final output
Exit
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5. Results
Docking with proposed structure shows better H-bonding and binding energy with Target protein
CYP51 of both Blumeria graminis and Aspergillus fumigatus, while the docking with known or
existing fungicides showing poor binding energy and H-bonding.
Later virtual screening was carried out for finding novel inhibitor of CYP51 of both Blumeria
garminis and Aspergillus fumigatus. The 1049 triazole like molecule obtained from Zinc
Database, then filer the drug like molecule using open eye solution software for filtering, finally
667 triazole like molecule obtained then dock.
The molecules 2-(1H-1,2,4-triazol-1-ylcarbonothioyl)-3a,4,7,7a-tetrahydro-1H-isoindole-
1,3(2H)-dione and 2-(1H-1,2,4-triazol-1-ylcarbonyl)-3a,4,7,7a-tetrahydro-1H-isoindole-1,3(2H)-
dione showing good binding energy and Hydrogen bond with target protein CYP51 of Blumeria
garminis.
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5.1. Docking result of proposed structure with CYP51
5.1.1. Docking result of Blumeria graminis
Table 2a
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Table 2b
In silico designing of novel triazole derivatives as substituent for resistant fungicides
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Table 2c
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Table 2d
In silico designing of novel triazole derivatives as substituent for resistant fungicides
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Table 2e
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Table 2f
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Table 2g
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Table 2h
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Table 2i
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5.1.2. Docking result of Aspergillus fumigatus
Table 3 A
Table 3B
In silico designing of novel triazole derivatives as substituent for resistant fungicides
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Table 3C
Table 3D
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Table 3E
Table 3F
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Table 3G
Table 3H
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5.2. Docking result of virtual screening with CYP51
5.2.1. Result of virtual screening for CYP51 protein of Blumeria graminis
Top14 ligands found after docking on the basis of energy score.
Molecule
name MoleDock score Affinity logP MW HBA HBD
ZINC00560057 -142.479 -28.6438 2.42 332.381 3 5
ZINC00064573 -139.477 -31.0579 1.67 274.236 4 4
ZINC00570929 -138.766 -26.4399 2.1 293.323 2 3
ZINC00560047 -136.565 -38.0105 2.46 336.388 4 1
ZINC00115651 -134.905 -32.7638 3.52 331.416 4 3
ZINC00576263 -134.578 -29.4997 2.09 293.323 2 3
ZINC00541851 -134.205 -25.9191 3.29 324.423 4 2
ZINC00182689 -133.3 -34.015 2.82 332.404 5 2
ZINC00560030 -133.179 -33.706 2.24 336.413 4 2
ZINC00411662 -132.115 -34.9462 2.82 346.815 4 2
ZINC00360664 -131.55 -33.9106 3.27 324.377 2 2
ZINC00560066 -131.035 -29.5436 2.52 332.424 4 2
ZINC00299084 -130.959 -29.4402 0.25 315.327 5 3
ZINC00129663 -130.829 -26.5647 1.91 244.292 3 2
Table 4.moldock score of virual screening Blumeria graminis
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5.2.2. Result of virtual screening for CYP51 protein of Aspergillus fumigatus
Top14 ligands found after docking on the basis of energy score.
Molecule
name
MoleDock score Affinity logP MW HBA HBD
ZINC00406640 -147.227 -33.8897 2.79 339.42 4 2
ZINC00115651 -146.22 -37.2478 2.32 330.41 4 3
ZINC00268949 -145.349 -29.813 2.14 342.37 4 2
ZINC00032585 -143.004 -29.6872 1.15 344.39 4 2
ZINC00360663 -139.897 -33.4501 3.27 324.38 2 2
ZINC00411656 -139.812 -31.1697 2.5 325.39 4 2
ZINC00360584 -139.188 -35.0356 3.59 344.8 2 2
ZINC00182689 -133.3 -34.015 2.82 332.41 5 2
ZINC00115647 -138.607 -40.961 2.56 308.75 4 3
ZINC00246313 -137.954 -36.5279 2.26 318.38 5 2
ZINC00285763 -137.58 -29.3326 3.59 338.43 4 1
ZINC00285731 -136.537 -26.9243 1.02 317.32 3 3
ZINC00068477 -136.179 -33.2195 3.05 339.8 4 1
ZINC00081006 -135.895 -28.6802 3.16 335.38 4 2
Table 5. Moldock score of virual screening for Aspergillus fumigatus
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Figure 12a.Bar graph showing energy comparison of top 10 ligands on docking (Aspergillus fumigates)
Figure 12b.Bar graph showing energy comparison of top 10 ligands on docking (Blumeria graminis)
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Figure 13b
Figure 14b
Figure 14a Docking Result of virtual screening showing H-bond and Figure 14b triazole like molecule (Aspergillus fumigatus)
Figure 14a
Figure 13a Docking Result of virtual screening showing H-bond and Figure 13b triazole like molecule (Blumeria graminis)
Figure 13a
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6. Discussion
The B. graminis and A. fumigatus belong to the kingdom Fungi. Fungi cannot synthesize their
own food from sunlight because of lack of chlorophyll, it is a green pigment in plant which help
in the synthesis of own food from sunlight and CO2 [1]
. Fungi cause different diseases in plants
and humans. The target protein for triazole is carbon 14α sterol demethylase (CYP51), this
protein play important role in the synthesis of sterol in the membrane of fungi [10]. Nowadays
CYP51 protein has become resistant to the marketed antifungal triazole fungicides like
fluconazole, epoxoconazole, triadimol, itraconazole and Propiconazole, because of mutation in
target protein at the binding site. The reason behind mutation may be due to prolonged use of
these fungicides. IPL Lucknow has designed some triazole derivatives for commercial use. We
have studied in silico interaction of these compounds with the target protein.
The complete structure of CYP51 is not available in protein data bank (PDB) so, the target
protein was modeled by comparative homology modeling using modeler software (9.0 version).
The accuracy of the model was 86.9 % of residue fall in core region and other 11.4 % in
allowable region in Ramachandran plot in case of B.graminis.In case of A.fumigatus modeled
accuracy is 90.3% of residue fall in core region and 7.3% in allowable region. We had screened
out best two molecules on the basis of Hydrogen bonding and binding energy from above
proposed compounds using Dock software. We also took 1049 triazole derivatives compound
from Zinc database. After filtering these compounds on the basis of drug like molecules by filter,
we obtained finally 667 compounds.
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We had screened all these compounds and obtained top 10 molecules which show best binding
energy and hydrogen bonding with the same target protein present in both the species i.e.
B.graminis and A.fumigatus, shown in table 4 and table 5.respectively.
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7. Conclusions
The proposed structures 2-(1H-1,2,4-triazol-1-ylcarbonothioyl)-3a,4,7,7a-tetrahydro-1H-
isoindole-1,3(2H)-dione and 2-(1H-1,2,4-triazol-1-ylcarbonyl)-3a,4,7,7a-tetrahydro-1H-
isoindole-1,3(2H)-dione are showing best docking energy in case of B.graminis , while N,N-
dimethyl-1H-1,2,4-triazole-1-carboxamide shows best binding energy in case of A.fumugatus .
After virtual screening with CYP51 of Aspergillus fumigatus, N-(3, 4-dimethylphenyl)-2-[[5-(4-
pyridyl)-2H-1, 2, 4-triazol-3-yl] sulfanyl] acetamide showing good docking score and Hydrogen
bonding. While Blumeria graminis shows best energy and Hydrogen bonding with N-
isopropylideneamino-4-[2-(2H-1, 2, 4-triazol-3-ylsulfanyl) acetyl] amino-benzamide. A
comparison of the screened compounds from zinc database and the proposed structure, the
former show better binding energy than proposed structure.
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8. Future work
This project was aimed at finding novel fungicides for the inhibition of CYP51, an important
enzyme playing crucial role in sterol synthesis in fungi. We have found novel fungicides which
might be helpful for the inhibition of sterol synthesis. These novel fungicides compounds may
act as a potent and specific inhibitor for CYP51 enzyme; though their efficacy, toxicity and
pharmacokinetic properties need to be studied experimentally.
The following steps are for future work
• We are planning to use different parameters, so that more result can be obtained from
Zinc database.
• Chemical synthesis of proposed molecules.
• Spraying on infected plants and comparing proposed molecules with mutated fungicides.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD68
9. References
[1] http://en.wikipedia.org/wiki/Fungi
[2] http://www.ucmp.berkeley.edu/fungi/fungi.html
[3] A brief guide to the management of pesticide resistance in the turf and nursery industries
in Australia –journal Paton Fertilizers, March 2007
[4] Understanding fungicide resistance Robert Beresford- HortResearch, Auckland
Originally published in: The Orchardist Vol: 67 No :( 9):24, Oct 1994
[5] Fungicides – A Practical Approach to Resistance Management for Potato Diseases.
[6] Eugene O’Sullivan, Brendan Dunne, Steven Kildea, and Ewen Mullins Teagasc, Oak
Fungicide Resistance – an increasing problem ,Park Crops Research Centre, Carlow irish
agriculture and food and technology
[7] Master Gardner Ohio state university extension
[8] Frederick M. Fishel “Pesticide Toxicity Profile: Triazole Pesticides”; United State (US)