Project Report on Drug Designing Using insilico Method
-
Upload
anurag-yadav -
Category
Documents
-
view
9 -
download
1
description
Transcript of Project Report on Drug Designing Using insilico Method
IInn ssiilliiccoo ddeessiiggnniinngg ooff nnoovveell ttrriiaazzoollee ddeerriivvaattiivveess aass ssuubbssttiittuueenntt ffoorr rreessiissttaanntt ffuunnggiicciiddeess
DDiisssseerrttaattiioonn
SSUUBBMMIITTTTEEDD IINN PPAARRTTIIAALL FFUULLFFIILLMMEENNTT OOFF TTHHEE RREEQQUUIIRREEMMEENNTTSS FFOORR TTHHEE DDEEGGRREEEE OOFF
MASTER OF TECHNOLOGY IN INFORMATION TECHNOLOGY (SPECIALIZATION ‐ BIOINFORMATICS)
SSuubbmmiitttteedd bbyy
SSAARRIIKKAA SSAAHHUU IIBBII22000066001100
MM..TTeecchh IITT ((SSppeecciiaalliizzaattiioonn –– BBiiooiinnffoorrmmaattiiccss))
UUnnddeerr tthhee SSuuppeerrvviissiioonn ooff
Prof. Krishna Misra PPhh.. DD..,, FFNNAASScc
EEmmeerriittuuss PPrrooffeessssoorr iinn CChheemmiissttrryy DDeeppaarrttmmeenntt UUnniivveerrssiittyy ooff AAllllaahhaabbaadd
AAllllaahhaabbaadd -- 221111 000022 &&
CCoooorrddiinnaattoorr,, IInnddoo--RRuussssiiaann CCeennttrree ffoorr BBiiootteecchhnnoollooggyy,, IInnddiiaann IInnssttiittuuttee ooff IInnffoorrmmaattiioonn TTeecchhnnoollooggyy--AA ,, DDeeoogghhaatt JJhhaallwwaa ccaammppuuss,,
AAllllaahhaabbaadd
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD
IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY
AAllllaahhaabbaadd
(Deemed University) ((AA CCeennttrree ooff EExxcceelllleennccee iinn IInnffoorrmmaattiioonn TTeecchhnnoollooggyy EEssttaabblliisshheedd bbyy GGoovvtt.. ooff IInnddiiaa)
Date: ___________________
WWEE HHEERREEBBYY RREECCOOMMMMEENNDD TTHHAATT TTHHEE TTHHEESSIISS
PPRREEPPAARREEDD UUNNDDEERR OOUURR SSUUPPEERRVVIISSIIOONN BBYY SSaarriikkaa ssaahhuu
EENNTTIITTLLEEDD iinn ssiilliiccoo ddeessiiggnniinngg ooff nnoovveell ttrriiaazzoollee ddeerriivvaattiivveess
aass ssuubbssttiittuuttee ffoorr rreessiissttaanntt ffuunnggiicciiddeess BBEE AACCCCEEPPTTEEDD IINN
PPAARRTTIIAALL FFUULLFFIILLMMEENNTT OOFF TTHHEE RREEQQUUIIRREEMMEENNTTSS FFOORR
TTHHEE DDEEGGRREEEE OOFF MMAASSTTEERR OOFF TTEECCHHNNOOLLOOGGYY IINN
IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY ((SSPPEECCIIAALLIIZZAATTIIOONN--
BBIIOOIINNFFOORRMMAATTIICCSS)) FFOORR EEXXAAMMIINNAATTIIOONN
CCOOUUNNTTEERRSSIIGGNNEEDD
DEAN (ACADEMIC) THESIS ADVISOR
PPrrooff..UU..SS..TTwwaarrii PPrrooff..KKrriisshhnnaa MMiissrraa
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD2
IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY
AAllllaahhaabbaadd
(AA CCeennttrree ooff EExxcceelllleennccee iinn IInnffoorrmmaattiioonn TTeecchhnnoollooggyy EEssttaabblliisshheedd bbyy GGoovvtt.. ooff IInnddiiaa)
CCEERRTTIIFFIICCAATTEE OOFF AAPPPPRROOVVAALL**
The foregoing thesis is hereby approved as a creditable study in
the area of Information Technology carried out and presented in a
manner satisfactory to warrant its acceptance as a pre-requisite to
the degree for which it has been submitted. It is understood that by
this approval the undersigned do not necessarily endorse or
approve any statement made, opinion expressed or conclusion
drawn therein but approve the thesis only for the purpose for
which it is submitted.
COMMITTEE ON FINAL EXAMINATION FOR EVALUATION OF THE THESIS
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD3
DDEECCLLAARRAATTIIOONN
This is to certify that this thesis work entitled “in-silico designing of novel
triazole derivatives as substitute for resistant fungicides” is submitted by
me in partial fulfillment of the requirement for the completion of M.Tech in
Information Technology (specialization in Bioinformatics) to Indian Institute
of Information Technology, Allahabad comprises only my original work and
due acknowledgement has been made in the text to all other material used.
I understand that my thesis may be made electronically available to the
public.
SSaarriikkaa ssaahhuu
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD4
ACKNOWLEDGEMENT I am highly grateful to our honorable Director, IIIT-Allahabad, Prof. M.D. Tiwari for, his ever
helping attitude and encouraging us to excel in studies.
Besides, he has been a source of inspiration during my entire period of M.Tech at IIIT –
Allahabad.
I am thankful to Prof. U. S. Tiwary, Dean Academics, IIIT Allahabad for providing all the necess
ary requirements and for his moral support for this dissertation work as well during the whole co
urse of M. Tech.
The most notable source of guidance was my advisor, Prof. Krishna Misra, Professor, IIIT Alla
habad. I owe her a great deal of thanks for taking me under her wings and allowing me to soak u
p some of her knowledge and insight. She has not only made me to work but guided me to orient
towards research. I thank her for teaching me the ability to think critically and analytically throug
h the classical discussions we had in her office.
C.M. Bhandari, Coordinator of Indo Russian Center for Biotechnology for his honest dedication t
owards our education and career and for being with us in various levels of academic pursuits.
I am also grateful to assistant Dr. C.V.S. Siva Prasad, T. Lahiri, Mr. Vikram Katju, Mr. Pritish
Varadwaj, Mr. Manish Kumar and Ms. Anamika Singh for their support and motivation
throughout my research project work.
I also thankful to IPL, Lucknow for providing me the proposed structure of triazole compounds.
In completing this thesis work, I have disturbed, interrupted, Interrogated and discussed with
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD5
a great variety of people, yet I have never once met anything but patience and politeness. I cannot
thank everybody by name, but I would like to record my great debts of gratitude to Dr. Bakul gohel,
Dr. Neera singh, Dr. Shallu kalia, Dr. Hrishikesh Mishra, Dr.Sandeep Kumar, Mr. Buksh, Ms.Mona
chaurasia, Mr.Upendra Kumar, Mr. Rajesh Kesharawani, Mr.Shakti Kumar and Mr.Manarshi Das.
For not only helping me in studies but also for making this batch a house of learning through their
hard work and dedication.
I am also thankful to rest of classmates and M.Tech. Friends for their cooperation during my work. I
am also thankful to them for helping me in my project work and also some kind of discussion
regarding my work which helped me to understand the Concept regarding my work.
This acknowledgement will not complete until I pay my respectful homage to my Family especially
my parents, my brother and my sister, whose enthusiasm to see this work Complete was as infectious
as their inspiration. I am grateful to my parents for Their efforts in building my career, cheerfully
bearing with my whims and for Letting me make my own decisions
Finally, I thank God for giving me an opportunity to thank all these people.
Sarika sahuSarika sahu
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD6
CCoonntteennttss
LLiisstt ooff ffiigguurreess 1100
LLiisstt ooff ttaabblleess 1111
AAbbbbrreevviiaattiioonnss 1122
AAbbssttrraacctt 1133
1.
IInnttrroodduuccttiioonn
14
1.1
MMoottiivvaattiioonn
14 1.2 PPrroobblleemm ssttaatteemmeenntt 15 1.3 IInnttrroodduuccttiioonn ttoo ttrriiaazzoollee 16 1.4 TTaarrggeett ooff ttrriiaazzoollee 17 1.5 SStteerrooll 1144αα--ddeemmeetthhyyllaassee 17 1.6 SSyynntthheessiiss ooff eeggrroosstteerrooll 18 2. LLiitteerraattuurree rreevviieeww 21
2.1
IInnttrroodduuccttiioonn ttoo ffuunnggii
21 2.1.1 Plant fungal disease 21 2.1.1.1 Symptoms and signs of powdery mildew 22 2.1.2 Human fungal disease 23 2.1.2.1 Symptoms and signs of Aspergillosis 25 2.2 CCoommppuutteerr aaiiddeedd ddrruugg ddeessiiggnn 26 2.2.1 Introduction 26 2.2.2 Drug design cycle 28 2.3 HHoommoollooggyy mmooddeelliinngg 29 2.3.1 General Procedures 30
2.4 Docking
30
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD7
3. EExxppeerriimmeennttaall DDeessiiggnn aanndd AAnnaallyyssiiss 32
3.1
MMooddeelleerr pprrooggrraamm
33 3.1.1 Preparing the input file 34 3.1.1.1 Atom files 34 3.1.1.2 Alignment file 34 3.1.1.3 Script file 34 33..22 RReeffiinnee aanndd eevvaalluuaattiioonn 34 33..33 LLiiggaanndd 37 33..44 DDoocckk 41 3.4.1 Dock working principle 41 3.4.2 Preparing Molecules for Docking 42 3.4.2.1 Examine the target file 42 3.4.2.2 Prepare the receptor file 42 3.4.2.3 Prepare the ligand file 42 3.5 Sphgen 43 3.5.1 Generate the molecular surface of the receptor 43 3.5.2 Generate the spheres surrounding the receptor 43 3.5.3 Select a subset of spheres to represent the binding
site(s) 43 3.6 Grid 43 3.6.1 Creating a box around the active site 43 3.6.2 Generating the Grid 44 3.7 Rigid and Flexible Ligand Docking 44 3.7.1 Rigid Ligand Docking 44 3.7.2 Flexible Ligand Docking 44 4 FFllooww cchhaarrtt 45
5 RReessuullttss 47
5.1
DDoocckkiinngg rreessuulltt ooff pprrooppoosseedd ssttrruuccttuurree wwiitthh CCYYPP5511
48 5.1.1 Docking result of Blumeria graminis 48
5.1.2 Docking result of Aspergillus fumigatus 57
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
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD9
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
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD10
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
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD11
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
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD12
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.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD13
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
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD14
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.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD15
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,
Triarimol, Prothioconazole, Terconazole, Voriconazole, Posaconazole, Ravuconazole,
Isavuconazole, Miconazole, etoconazole, Econazole, Bifonazole, Tioconazole, Sulconazole,
Oxiconazole. These are fungicides which become resistant examples are Fluconazole,
Itraconazole, Ketoconazole, Posaconazole.
1.3 Introduction to triazole
1, 2, 3-Triazole is one of a pair of isomeric chemical compounds with molecular formula
C2H3N3, called triazole. Triazole is an aromatic heterocyclic compound having ring-chain
tautomerism.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD16
Figure1. Structure of 1, 2, 4-triazole and 1, 2, 3-triazole
1.4 Target of triazole
The main target enzyme of azole antifungal, the fungal sterol 14α-demethylase (CYP51), this
enzyme is highly conserved in human and animals throughout evolution. However, there are few
record that azole compound inhibit human sterol 14α-demethylase (CYP51) [9].These agents
inhibit the enzyme 14α-demethylase, a cytochrome P450 enzyme that catalyses the synthesis of
ergosterol. Therefore, ergosterol synthesis is inhibited and membrane integrity and function is
affected.
1.5 Sterol 14α-demethylase
CYP51 belongs to the superfamily of heme-containing cytochrome enzymes involved in
endogenous and xenobiotic substance metabolism. Azole shows antifungal activity by inhibiting
CYP51 enzyme in fungi which leads to the blocking of ergosterol biosynthesis in fungal cell
wall. These enzymes not only are expressed in fungi and yeast but also occurr in other bacteria
and mammals species [10].
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD17
1.6 Synthesis of ergosterol
There is similarity between fungal plasma membranes and mammalian plasma membranes, the
main difference is that having the nonpolar sterol ergosterol in fungi, while cholesterol in other
mammal, as the principal sterol. Because the plasma membranes are selectively permeable so it
controls the passage of materials into and out of the cell. Sterols present in the membrane
provide structure, modulation of membrane fluidity, and possibly control the physiologic
activities. Most of the antifungal agents interfere with ergosterol synthesis of cell wall.
Demethylation of lanosterol and synthesis of both ergosterol and chlorosterol is the first step.
The essential enzymes are related with fungal microsomes, which include an electron transport
system (ETS) analogous to the one in liver microsomes [11].
Figure 2.synthesis of ergosterol in fungi [12]
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD18
Figure 3.inhibition of 14-α sterol demethylase by azole drug [12]
Mechanism of Action of Azoles given in following flowchart :
The interaction with cytochrome P450-dependent enzymes present in human responsible for
drug interaction in human
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD19
Figure 4.synthesis of ergosterol
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD20
2.Literature review
2.1. İnroduction to fungi
fungi are amazing organisms which are neither plants, nor animals. Fungi cannot synthesize their
food from sunlight, water and carbon dioxide as plants do, in the process known as
photosynthesis. This is because they lack chlorophyll, its green pigment which plants use to
capture light energy. So, like other animals, they depend on the other organisms for food supply.
They do this in three ways. They may break down or 'rot' dead plants and animals. Organisms
which obtain their food this way are known as 'saprophytes'. Alternatively they may feed directly
off living plants and animals as 'parasites'. A third group is symbioiotic relationship with the
roots of plants; know as mycorrhizae . Some plants have ability to provide a secondary
metabolite which is act as a weapon for self defense. So it becomes very difficult to fungi to
combat on plant tissue. Some fungi produce phytotoxic metabolite, which is toxic for plant .
[13]
[14]
2.1.1. Plant fungal disease
Plant pathology is the branch of science to study plant diseases. Fungal diseases are the most
important biotic factors limiting crop production [15]. The diversity of fungal pathogen leads to
different kind of resistance, which is a challenging task to develop newer active compounds.
Some common plant diseases are powdery mildew, rust, leaf spot, blight, root and crown rots,
damping-off, smut, anthracnose, and vascular wilts.
In this project I worked on Blumeria graminis which causes powdery
mildew disease, and is one of the most important foliar diseases of wheat over the world,
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD21
growing only on living tissue and whose spores destroy the leaf by germinate on surface of leaf
[16]. Now day’s Blumeria graminis has become highly resistant to tridimenol, the most widely
used triazole in the late 1970’s and early 1980’s . The reason behind the resistance was pointed
to mutation in the CYP51gene, encoding a replacement of tyrosine for phenylalanine at position
136(Y 136 F), with resistance [17].
2.1.1.1. Symptoms and signs of powdery mildew
The symptoms of powdery mildew is white to gray spot on the surface of leave because fungal
grow mostly in upper leaf (epidermis layer) .some of the hyphae of fungi penetrate inside the
surface up to the cortex level and form a pustules [18]. Powdery mildew reproduce asexually and
produces conidia from conidiophores they form a chain like structure. The single spore look like
oval shape and colorless.
Figure5. Symptoms of Blumeria graminis in wheat plant
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD22
2.1.2Human fungal disease
Medical mycology is the science where we study the fungal disease in human and animals.
Mycosis is the branch of science where diseases of warm-blooded animals are studied. The
disease caused by fungi is not fatal, but sometime; they may be permanently scaring, so it gives a
very ugly view to the skin. The treatment of fungal diseases is very tedious than other bacterial
diseases. As we know that bacteria is a prokaryotic organism. The fungi are eukaryotes, so the
treatments that will kill only the fungal cell and without affecting our own cells is very tedious
task [19].
Human beings are highly protective to fungal diseases meaning thereby that they are highly
resistant.. When fungi do pass the resistance barriers of the human body and establish infections,
the infections are classified according to the tissue levels initially colonized [20].
Aspergillus fumigatus. Causes fungal diseases in human known as Aspergellosis The present
work has been done on this fungus.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD23
Taxonomic Classification
Kingdom:Fungi
Phylum:Ascomycota
Order:Eurotiales
FamilyTrichocomaceae
Genus: Aspergillus
Figure 6. Aspergillus fumigatus
Aspergillus species is a worldwide and omnipresent fungus. Aspergillosis is a very fatal disease
both in man and animals. Aspergillus is a thermophilic fungus that it may be grow at high
temperature.
Aspergillus fumigates is airborne fungal pathogens, with high mortality and
morbidity in the immunocompromised host. Form literature it has been seen that A. fumigates is
highly resistant to marketed fluconazole fungicides. There are also few records that A.fumigatus
become resistant to itraconazole (ITC). In A. fumigatus, there are cyp51and cyp51B proteins
these are two individual but linked with Cyp51 proteins. From the literature it proposed that there
are two molecular mechanisms by which A. fumigates become resistant:
• There is reduction of intracellular accumulated drug because they either increased
expression of efflux pumps or there might be reduced penetration of the drug.
• There is modification of Cyp51. To date the most prevalent mechanism of resistance in A.
fumigatus appears to be the modification of Cyp51, specifically mutations in cyp51A.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD24
There are point mutation at 220 position by methionine is substituted leads to the disruption
drug binding binding site. This substitution is responsible for that they become resistant, because
they involved in conformational changes associated with the catalytic cycle rather than in
residues that directly contact the drug [21].
Most studies concerning mechanisms of azole resistance in Aspergillus have been performed in
A. fumigatus and have demonstrated that resistance was associated with modification of the 14-α
sterol demethylase target enzyme (CYP51), specifically mutations in the gene cyp51A.
Importantly, different mutations appear to result in resistance to posaconazole and itraconazole
versus voriconazole and ravuconazole Cross-resistance to itraconazole and posaconazole has
been associated with amino acid substitutions at glycine 54 (G54) whereas cross-resistance to
voriconazole and ravuconazole has been associated with amino acid substitutions at G448 It has
been postulated, based on molecular modeling studies, that a substitution at G54 in the A’helix
of AF-CYP51A confers resistance to posaconazole and itraconazole by perturbing the binding of
the long side chain in the hydrophobic channel (channel 2) of the enzyme (20). Given that
voriconazole and ravuconazole lack a long side chain, substitutions at G54 would be predicted to
have no effect on the binding of these compact triazoles to the target [22].
2.1.2.1 Symptoms and signs of Aspergillosis
All birds, animals, including man are victims of Aspergillosis . The most common symptoms of
aspergillosis are pain in this sinuses, nose, or ear canal; facial swelling; cough and difficulty
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD25
breathing; chest pain; and fever and night sweats.. Aspergillus infection of the ear (called
otomycosis), can produce itching and a discharge, sometimes noticed as a spot on the pillow [23].
2.2. Computer aided drug design
2.2.1 Introduction
Structure-based ligand or inhibitor design, or rational drug design, as it is sometimes called,
aims to identify chemical compounds or peptides that bind strongly to key regions of biologically
relevant molecules, e.g. enzymes or receptors, for which three-dimensional structures are known.
Designed compounds should be able to inhibit or stimulate the biological activity of their target
molecules. The rapid progress of the human genome project is providing an ever-increasing
number of potential protein drug targets. Together with advances in structural determination
techniques such as nuclear magnetic resonance, crystallography and even homology modeling,
structure-based design of ligands or inhibitors has emerged as an important tool in drug
discovery and pharmaceutical research [24].
Computational methods are required to extract all of the relevant information from the available
structures and to use it in an efficient and intelligent manner to design improved ligands for the
target. Due to genome sequencing projects, the number of known sequences is increasing at a
rapid rate [25].
New target identification strategies and associated bioinformatics
technologies are being developed to categorize this vast body of information [26]. Today, many
scientists are working on ways to try to predict the three-dimensional structure of a protein from
its one-dimensional amino acid sequence [27]. There is also a worldwide effort in functional
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD26
genomics to determine as many three-dimensional structures of proteins as possible or to develop
computational approaches to cluster sequences into families of related proteins and then select
and solve the three-dimensional structure of a representative sequence. Computational methods
are needed to exploit the structural information to understand specific molecular recognition
events and to elucidate the function of the target macromolecule. This information should
ultimately lead to the design of small molecule ligands for the target, which will block its normal
function and thereby act as improved drugs. Most of the drugs currently on the market have been
found through large-scale random screening of compounds for activity against a target, for which
no three-dimensional structural information was available. That is, thousands of compounds (in
the company database) are screened for activity. High-throughput robotic screening methods
accelerate this process. In the end, it is hoped that at least a small number of compounds will be
active against the target. A good lead compound is active at concentrations of 10 mM or less [28].
The first example of structure-based design was reported by the group of
Beddell and Goodford in 1976 at Wellcome Laboratories in the United Kingdom [98].
Hemoglobin was selected as a target, which at the time was the only example of pharmacological
relevance with a known crystal structure. The goal of the studies was to develop a ligand that
acts similarly to the natural allosteric effector diphosphoglycerate. This endogenous ligand binds
to hemoglobin and regulates its oxygen affinity. Taking this molecule as a reference, the
Wellcome group designed dialdehyde derivatives and related bisulfite adducts which, as
expected, modify the oxygen affinity to hemoglobin. Several years later the antihypertensive
captopril, which inhibits the angiotensin-converting enzyme, was introduced onto the market;
this was the first drug to be developed using structural information. The past 20 years of drug
design have witnessed the structural characterization of a tremendously number of
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD27
therapeutically important targets. The increasing number of successful applications of drug
design has led to the discovery of new therapeutics. The recent development of human
immunodeficiency virus (HIV) protease inhibitors has convincingly demonstrated the impact and
the relevance of structure-based approaches to the development of new drugs [29].
2.2.2. The drug design cycle
The process of structure-based drug design is an iterative one and often proceeds through
multiple cycles before an optimized lead goes into phase I clinical trials. The first cycle includes
the cloning, purification and structure determination of the target protein or nucleic acid by one
of three principal methods: X-ray crystallography, NMR, or homology modeling. Using
computer algorithms, compounds or fragments of compounds from a database are positioned into
a selected region of the structure. These compounds are scored and ranked based on their steric
and electrostatic interactions with the target site and the best compounds are tested with
biochemical assays. In the second cycle, structure determination of the target in complex with a
micromolar inhibition in vitro, reveals sites on the compound that can be optimized to increase
potency. Additional cycles include synthesis of the optimized lead, structure determination of the
new target-lead complex, and further optimization of the lead compound. After several cycles of
the drug design process, the optimized compounds usually show marked improvement in binding
and, often, specificity for the target [30].
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD28
2.3. Homology modeling
Now days there are number of technique developed in molecular biology that provide easy to
identification, sequencing of DNA, RNA or proteins. To determine the three-Dimensional
structure of protein is very difficult and time consuming task. In the field of structural biology
the main objective to find out the three dimensional structure of protein from the there sequences.
[31]. the ultimate goal of protein modeling is to predict a structure from its sequence with an
accuracy that is comparable to the best results achieved experimentally. This would allow users
to safely use rapidly generated in silico protein models in all the contexts where today only
experimental structures provide a solid basis: structure-based drug design, analysis of protein
function, interactions, antigenic behavior, and rational design of proteins with increased stability
or novel functions [32].
One method that can be applied to generate reasonable models of protein
structures is homology modeling. In protein structure prediction, homology modeling, also
known as comparative modeling, is a class of methods for constructing an atomic-resolution
model of a protein from its amino acid sequence (the "query sequence" or "target").most of the
homology modeling technique based on the already known 3-D coordinates of the protein which
we called template structure or parent structure, it should be likely to resemble to the query
sequence (whose structure is not known). The model of our target protein is produced by the use
of both sequence alignment and template structure. Because protein structures are more
conserved than protein sequences, detectable levels of sequence similarity usually imply
significant structural similarity [33]. The quality of the homology model is dependent on the
quality of the sequence alignment and template structure [34].
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD29
2.3.1. General Procedures
The steps to creating a homology model are as follows [35]:
• Identify homologous proteins and determine the extent of their sequence similarity with
one another and the unknown
• Align the sequences
• Identify structurally conserved and structurally variable regions
• Generate coordinates for core (structurally conserved) residues of the unknown structure
from those of the known structure(s)
• Generate conformations for the loops (structurally variable) in the unknown structure
• Build the side-chain conformations
• Refine and evaluate the unknown structure.
2.4. Docking
The application of computational methods to study the formation of intermolecular complexes
has been the subject of intensive research during the last decade. It is widely accepted that drug
activity is obtained through the molecular binding of one molecule (the ligand) to the pocket of
another, which is commonly a protein. In the binding conformations of a complex of a protein
with a therapeutic drug, the molecules exhibit geometric and chemical complementarities, both
of which are essential for successful drug activity. The computational process of searching for a
ligand that is able to fit both geometrically and energetically to the binding site of a protein is
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD30
called molecular docking. The docking problem is analogous to an assembly-planning problem
where the parts are actuated by molecular force fields and have thousands of degrees of freedom.
In general docking process can be divided in to two phases. One is the searching algorithm,
which finds possible binding geometries of the protein and its ligand. The other is the scoring
function, which ranks the searching results and selects out the best binding geometry based on
the energies of the of the complexes or, more theoretical value, ∆Gbind, the binding free energy
difference between the bound and unbound states of the ligand and protein [36]. Ligand docking
and screening algorithms are now frequently used in the drug-design process, and have
additional application in the elucidation of fundamental biochemical processes. The purpose of
docking algorithms is now expanding beyond the original goal of fitting a given ligand into a
specific protein structure. Newer applications include database screening, lead generation and de
novo drug design [37].
.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD31
3. Experimental Design and Analysis
Software programs and database were used for molecular docking, virtual screening of ligand
database and building ligand molecules:
ACD ChemSketch: for drawing small molecules and visualizations.
OpenBabel: for file format conversions.
Chemfile Browser: for handling of sdf files.
CORINA: for 2D to 3D structure conversion.
Molegro Virtual Docker (MVD): for visualization, docking based virtual screening, side chain
minimization.
Filter: Filter is a program for eliminating inappropriate or undesirable compounds from a large data set
Swiss pdb viewer: for visualization and loop mdelling
Pymol: for visualization
Modeler: for 3-dimensional structure of unknown protein. Homology modeling by modeler
software
Dock 6.0: for screening of large date set
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD32
Chimera UCSF Chimera is a highly extensible program for interactive visualization and
analysis of molecular structures and related data, including density maps, supramolecular
assemblies, sequence alignments, docking results, trajectories, and conformational ensembles.
Chimera can read molecular structures and associated data in a large number of formats, display
the structures in a variety of representations, and generate high-quality images and animations
suitable for publication and presentation [38]. To search triazole target in fungi mainly in Blumeria
graminis, the target was carbon 14 α sterol demethylase (CYP51). The 3-dimensional structure
of CYP 51 was not available in Protein Data Bank (PDB).
3.1. Modeler program
We took mutated sequence of Blumeria graminis from NCBI and then PDB blast. Consequently
result of blast show only maximum 51 percent identity with 1e9x, 1ea1, 1u13, 1x8v. The output
result of BLAST search is based on the identity hits of amino acid residue, low E-value
(Expectation value), these two factor is very important for homology modeling.
It is straightforward to build a model using information from multiple
templates like 1e9x, 1ea1, 1u13, 1x8v. Simply provide an alignment between all of the templates
and your target sequence, and list all of the templates in the knowns argument, MODELLER will
automatically combine the templates; there is no need to superpose the structures [39].
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD33
3.1.1. Preparing the input file
There are three kinds of input files:
3.1.1.1. Atom files
Each atom file is named code.atm where code is a short protein code, preferably the PDB code
like 1e9x.atm.
3.1.1.2. Alignment file
One of the formats for the alignment file is related to the PIR database format; this is the
preferred format for comparative modeling: example blumeria.ali.
3.1.1.3. Script file
MODELLER is a command-line only tool, and has no graphical user interface; instead, you must
provide it with a script file containing MODELLER commands. This is an ordinary Python script.
in this project I used multiple-model default.py. Run MODELLER itself by typing the
following at the command prompt: mod9v1 multiple- model-default.py
3.1.2. Refine and evaluation
We used procheck to Checks the stereochemical quality of a protein structure, producing a
number of PostScript plots analyzing its overall and residue-by-residue geometry [40].
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD34
The input to PROCHECK is a single file containing the coordinates of your protein structure.
This must be in Brookhaven file format the input to PROCHECK is a single file containing the
coordinates of your protein structure. This must be in Brookhaven file format.
The outputs comprise a number of plots, together with a detailed residue-by-residue listing. The
plots are output in PostScript format, it shows Ramachandran plot.
Ramachandran plot: 86.9% core 11.4% allow 0.7% gener 1.0% disall
Figure7.Ramachandran plot for sterol 14 α -demethylase of Blumeria graminis
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD35
Figure8. 3-D structure of sterol 14 α− demethylase of Blumeria graminis modeled by MODELER9.0 program
Ramachandran plot: 90.3% core 8.7% allow 0.5% gener 0.5% disall
FFiigguurree99..RRaammaacchhaannddrraann pplloott ffoorr sstteerrooll 1144 α-- ddeemmeetthhyyllaassee ooff AspergillusAspergillus fumigatusfumigatus
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD36
Figure10. 3-D structure of sterol 14 α−demethylase of Aspergillus fumigatus modeled by
MODELER9.0 program
3.3. Ligand
The existing known triazole drugs have become resistant, so there was need for new modified
triazole derivatives. The proposed structures used as ligand for docking were taken from R&D
department of IPL Lucknow .the synthesis of these structure is feasible in laboratories , so in-
silico approach was used to validate the binding of the proposed structure.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD37
Proposed structure
N
O
O
N
N
N
S
2-((1,2,4)Triazole-1-carbothioyl)-3a,4,7,7a-tetrahydro-isoindole-1,3-dione
N
O
OO
N
N
N
2-((1,2,4)Triazole-1-carbonyl)-3a,4,7,7a-tetrahydro-isoindole-1,3-dione
N
N
N
O
N
(1,2,4)Triazole-1-carboxylic acid dimethylamide
N
N
NHS
(1,2,4)Triazole-1-thiol
N
O
O
N
N
N
S
2-([1,2,4]Triazole-1-carbothioyl)-isoindole-1,3-dione
N
O
O
N
N
N
O
2-([1,2,4]Triazole-1-carbonyl)-isoindole-1,3-dione
O
SN
N
N
Cl1,2,4-triazole-1-mercapto formyl chloride
Cl
ClCl
SN
N
N
(1,2,4)Triazole-1-mercapto-trichloromethane
Table 1: Name and structure of proposed ligand
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD38
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"
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD39
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
\
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD40
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].
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD41
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
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD42
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
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD43
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.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD44
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
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD45
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
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD46
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.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD47
5.1. Docking result of proposed structure with CYP51
5.1.1. Docking result of Blumeria graminis
Table 2a
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD48
Table 2b
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD49
Table 2c
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD50
Table 2d
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD51
Table 2e
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD52
Table 2f
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD53
Table 2g
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD54
Table 2h
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD55
Table 2i
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD56
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
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD57
Table 3C
Table 3D
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD58
Table 3E
Table 3F
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD59
Table 3G
Table 3H
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD60
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
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD61
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
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD62
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)
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD63
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
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD64
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.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD65
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.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD66
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.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD67
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)
Environmental Protection Agency Washington, D.C. 20460
[9] Jürg A. Zarn, Beat J. Brüschweiler, and Josef R.Azole Fungicides Affect Mammalian
Steroidogenesis by Inhibiting Sterol 14α-Demethylase and Aromatase , Schlatter
Environmental Health Perspectives VOLUME 111 | NUMBER 3 | March 2003
[10] Jürg A. Zarn, Beat J. Brüschweiler, and Josef R. Schlatter Azole Fungicides Affect
Mammalian Steroidogenesis by Inhibiting Sterol 14α-Demethylase and Aromatase
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD69
[11] Michael R. McGinnis Stephen K. Tyring, Introduction to Mycology
[12] Russell E. Lewis Antifungal Pharmacology, Pharm.D
[13] Sevtap Arikan, ANTIFUNGAL DRUGS Modes of Action Mechanisms of Resistance,
MD Hacettepe University Medical School Ankara Turkey
[14] M.soledade C pedras,prospect fro controlling plant fungal diseases-Alternative based
on chemical ecology and biotechnology; Canadian journal chemistry .82(9):2004.
[15] Introduction to Fungi, Doctor Fungus
[16] ICRISAT international crop research institute for semi-arid tropics, Pathology Fungal
Diseases.
[17] B.M. Cunfer; Powdery mildew
[18] H.J. Cools1, B.A. Fraaije, S.H. Kim and J.A. Lucas Impact of changes in the target P450
CYP51 enzyme associated with altered triazolesensitivity in fungal pathogens of cereal
crops, 5 July 2006
[19] Daamen, 1989; Wiese, 1987.
[20] Fungi as Human Pathogens Hawksworth (1992),
[21] Neal R. Chamberlain, Ph.D, Fungi and Human Disease Last revised 8/1/06by
[22] E. Mellado, G. Garcia-Effron, L. Alcazar-Fuoli, M. Cuenca-Estrella, and J. L.
Rodriguez-Tudela Servicio de Micología, Centro Nacional de Microbiología,
Instituto:Substitutions at Methionine 220 in the 14_-Sterol Demethylase (Cyp51A) of
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD70
Aspergillus fumigatus Are Responsible for Resistance In Vitro to Azole Antifungal Drugs
2004/Accepted 1 April 2004
[23] D.J. Diekema, S.A. Messer, R.J. Hollis, L. Boyken, S. Tendolkar, J. Kroeger and M.A.
Pfaller :Patterns of Triazole Cross-Resistance/Susceptibility Among a Large Collection
of Clinical Isolates of Aspergillus Species .University of Iowa Carver College of
Medicine, Iowa City, Iowa 52242
[24] health-cares.net(http://respiratory-lung.health-ares.net/aspergillosis.php July18,2005
[25] Zeng, Mini-Review: Computational Structure-Based Design of Inhibitors that Target
Protein Surfaces, Combinatorial Chemistry and High Throughput Screening, 3, 355-362
Jun, 2000.
[26] Andrade, M. A., & Sander, C. Bioinformatics: from genome data to biological knowledge.
Curr Opin Biotechnol 8, 675–683 1997.
[27] Kingsbury, D. T. Dev Res Bioinformatics in drug discovery Drug 41, 120–128 1997.
[28] Dunbrack, R. L., Gerloff, D. L., Bower, M., Chen, X. W., Lichtarge, O., & Cohen, F. E.
Meeting review: The Second Meeting on the Crit- ical Assessment of Techniques for
Protein Structure Prediction (CASP2), Asilomar, California, December 13–16, 1996. Fold
Des 2, R27-R42. 95. Westhead, D. R., & Thornton, J. M. (1998). Protein structure
prediction. Curr Opin Biotechnology 9, 383–389.1997.
[29] Houston, J. G., & Banks, M. The chemical-biological interface: developments in
automated and miniaturised screening technology. Curr Opin Biotechnol 8, 734–740,
1997.
[30] Gerhard, Klebe, Recent developments in structure-based drug design, J Mol Med, 78:269–
281, 2000.
[31] Anderson, A.C., Review, The process of structure-based drug design. Chemistry &
Biology Vol. 10, 787–797, 2003.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD71
[32] G Vriend Homology modelling for beginners introduction June 21 2000.
[33] Elmar Krieger, Sander B. Nabuurs, and Gert Vriend, homology modeling.
[34] Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A. Comparative protein
structure modeling of genes and genomes .Annu Rev Biophys Biomol Struct 29: 291-325,
2000.
[35] Chung SY, Subbiah S: A structural explanation for the twilight zone of protein
sequence homology Structure 4: 1123–27 1996.
[36] David R. Bevan molecular modeling of proteins and nucleic acids dept. of biochemistry,
Virginia tech 1997-2003
[37] Tao, Peng and Lai, Luhua, Protein ligand docking based on empirical method for
binding affinity estimation, Journal of computer-aided molecular design. 15: 429-426,
2001.
[38] Brendan, J.; McConkey; Vladimir Sobolev and Marvin Edelman The performance of
current methods in ligand–protein docking, CURRENT SCIENCE, VOL. 83, NO. 7. 2002.
[39] http://www.cgl.ucsf.edu/chimera/about.html
[40] Andrej Šali: MODELLER a Program for Protein Structure Modeling Release 9v1
[41] NIH MBI Laboratory for Structural Genomics and Proteomics
[42] Tiba Aynechi and P. Therese Lang: Generating the Grid, Scott Brozell, June 2007
[43] P. Therese Lang Preparing Molecules for DOCKing December 2006
[44] P. Therese Lang, Generating Spheres October 2007 by Scott Brozell.
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD72
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD73
Flexible docking
In silico designing of novel triazole derivatives as substituent for resistant fungicides
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD74