Project Report on Drug Designing Using insilico Method

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MASTER OF TECHNOLOGY IN INFORMATION TECHNOLOGY (SPECIALIZATION ‐ BIOINFORMATICS)

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SSAARRIIKKAA SSAAHHUU IIBBII22000066001100

MM..TTeecchh IITT ((SSppeecciiaalliizzaattiioonn –– BBiiooiinnffoorrmmaattiiccss))

UUnnddeerr tthhee SSuuppeerrvviissiioonn ooff

Prof. Krishna Misra PPhh.. DD..,, FFNNAASScc

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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

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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



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



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



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



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



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



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



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



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



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



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.



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



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.



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.



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].



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]



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



Figure 4.synthesis of ergosterol



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,



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



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.



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.



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



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



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



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].



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].



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



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].

.



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



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].



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].



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



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



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.



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



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"



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

\



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].



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



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



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.



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



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



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.



5.1. Docking result of proposed structure with CYP51

5.1.1. Docking result of Blumeria graminis

Table 2a



Table 2b



Table 2c



Table 2d



Table 2e



Table 2f



Table 2g



Table 2h



Table 2i



5.1.2. Docking result of Aspergillus fumigatus

Table 3 A

Table 3B



Table 3C

Table 3D



Table 3E

Table 3F



Table 3G

Table 3H



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



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



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)



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



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.



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.



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.



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.



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



[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),

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Flexible docking



Documents

Project Report on Drug Designing Using insilico Method