IRSAPS Bulletin Vol 1, Issue 3

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IRSAPS Bulletin

(A periodical published by Indian Research Scholars’

Association for Promoting Science)

Three-dimensional structure of tRNA-enzyme complex, anticodon stem loop (ASL)

of tRNA containing hypermodified nucleoside, hn6Ade at 3'-adjacent (37

th) position

in the anticodon loop of tRNA

Vol. 1, Issue 3

Sep-Dec 2011

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IRSAPS Bulletin 2011, Vol. 1, Issue 3 © IRSAPS

A. i

Scope and Aim of Indian Research Scholars’ Association for Promoting

Science (IRSAPS)

Indian Research Scholars’ Association for

Promoting Science (IRSAPS) is created to spread

brotherhood through scientific research to every part of

our country! The aim is to develop a spirit of healthy

scientific discussions that could aid and advance ideas

through scientific knowledge exchange as well as acting

as a communion of mundane necessities. The

association will strive to facilitate the research in basic

sciences and augment infrastructure facilities with

renewed effort to make career in basic sciences a viable

and attractive option for the younger generation. In

our limited scope, the singular aim of IRSAPS will

be dissemination and decentralization of knowledge to

all remote corners of the country that could help

materialize dreams of the deserving ones who are

focusing on science based careers. Since India’s future

equally depends on the knowledge pool in basic

sciences, the association will endeavor to encourage

budding researchers by encouraging them at school and

higher secondary levels through the network of

volunteers. Though the association is a nonprofit

organization, it will encourage entrepreneurship among

the members through scientific innovations.

IRSAPS also endeavors to provide a platform of

knowledge exchange dedicated, but not limited to

Indian science research scholars. We are sure that

this will ignite new thinking and aspirations from all

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The association will take appropriate steps to

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the governing body members.

Date of establishment: 14 August 2010

Total number of members (December 2011): 360


A. ii

IRSAPS Bulletin

Volume 1, Issue 3

Issue Editor: Prof. A. K. Gade

Release date: 30th

January 2012

This journal is published by Indian Research Scholars’ Association for Promoting Science.

To join IRSAPS, please visit: http://www.irsaps.org

1st Issue: January-April

2nd

Issue: May-August

3rd

Issue: September-December

Statement on current journal’s policy: IRSAPS Bulletin does not have a peer review policy for

articles. Authors are solely responsible for the authenticity of content and correctness of all

articles. It is a free open source online journal. Nevertheless, readers and authors are referred to

the announcement for a change in Journal’s policy. Authors are requested to follow ethical

guidelines, failing to which may lead to rejection of manuscripts and withdrawal of published

articles.

Cover page details: Three-dimensional structure of tRNA-enzyme complex, anticodon stem loop

(ASL) of tRNA containing hypermodified nucleoside, hn6Ade at 3'-adjacent (37

th) position in the

anticodon loop of tRNA are shown. The hypermodified nucleoside hn6Ade found in the

anticodon loop of hyperthermophilic organisms.

Courtesy: Bajarang V. Kumbhar and Kailas D. Sonawane

*, Structural Bioinformatics Unit,

Department of Biochemistry, Shivaji University, Kolhapur, Maharashtra, India.

Contact person: Dr. Kailas D Sonawane.

E-mail: [email protected].

©Indian Research Scholars’ Association for Promoting Science, 2012. All rights reserved.

Reproduction in whole or in part of this journal for any other purpose except for the educational

interest is prohibited without the prior written consent.

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A. iii

Associate Editors and Editorial Board Members*

1. Dr. Amit K. Chattopadhyay

School of Engineering and

Applied Sciences

Mathematics (NCRG)

Aston University

Birmingham B4 7ET, UK

E-mail: akchaste[at]gmail.com

2. Prof. Aniket K. Gade

Department of Biotechnology

Sant Gadge Baba Amravati University,

Amravati - 444602.

Ph.No(O): 0721 2662206,07,08 Ext.267,

Fax: +91 721 2660949,

2662135

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Center for AIDS Health

Disparities Research

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and Biochemistry

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Meharry Medical College

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1005 Dr. DB Todd Jr Blvd,

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E–mail: cdash[at]mmc.edu

4. Prof. Deben C Baruah

Professor

Department of Energy

Tezpur University

Tezpur 784028

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baruahd[at]tezu.ernet.in

5. Dr. Jadab Sharma

Cookson India Research

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Bangalore, India

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7. Dr. Manish C. Pathak

Emory University

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USA

8. Dr. M. Buchi Suresh

Center for Ceramic Processing

International Advanced

Research Institute for Powder

Metallurgy and Material

Processing (ARCI)

Balapur, Hyderabad-500005

India

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9. Dr. Prakash Bhosale,

Senior Scientist,

Bioprocess Research and

Development

DowAgrosciences

Indianapolis, USA.

10. Prof. Ramesh C. Deka

Department of Chemical

Sciences

Tezpur University, Tezpur -

784 028

Tel: +91-3712-267008

(extension 5058)

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11. Dr. Sanjeev Malik

Department of Mathematics,

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Roorkee, India

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12. Dr. Sonika Saddar

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Biology

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Center

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13. Dr. T. Govindaraju

Assistant Professor

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Advanced Scientific Research

(JNCASR)

Jakkur, Bangalore 560064, India

Tel: +91 80 2208 2969

14. Dr. Ujjal Gautam

ICYS-MANA Research Fellow

National Institute for Materials

Science, 1-1, Namiki, Sukuba,

Japan-3050044

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ujjalgautam[at]gmail.com

15. Dr. Vijayakumar H. Doddamani

Associate Professor

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Publication and Distribution*

1. Dr. Amit Sharma

Unite de Catalyse et de Chimie du

Solide (UCCS)

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Scientifique, BP 48

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FRANCE 59651

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2. Dr. P. R. Naren

Senior Assistant Professor (SAP)

School of Chemical and

Biotechnology (SCBT)

Shanmugha Arts, Science,

Technology

and Research Academy (SASTRA)

Sastra University,

Tirumalaisamudram, Thanjavur,

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3. Mr. Qureshi Ziyauddin

Institute of Chemical Technology

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Maharashtra, India

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4. Dr. Rupam Jyoti Sarma

Department of Chemistry

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Guwahati, Assam, India

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5. Dr. Santosh B. Chavan

Jay Biotech, Pune, India

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sbchavan23[at]gmail.com

* List is incomplete


A. iv




A. v

Announcement

The publication of IRSAPS Bulletin will be discontinued from the next issue. IRSAPS Bulletin is

now re-christened as ‘Journal of Interdisciplinary Science’ which will become a peer reviewed

international journal with ISSN/IBN number. The journal will be initially released as online open source

journal. In view of this development, the reviewing policy of the journal has been changed with

immediate effect. All research manuscripts submitted for publication in the journal will now subject to

peer reviewing. However, current policy will continue for all non-research articles like science news. All

articles must be in the new Journal format, which will soon be made available at http://irsaps.org. Now

onwards, authors are also required to send a signed copyright agreement form.

All communications related to the new Journal will initially be operated from the following

branch offices:

1. Department of Chemical Sciences 2. Department of Biotechnology

Tezpur University Sant Gadge Baba Amravati University

Napaam, Tezpur Amravati- 444602

Sonitpur, Assam-784028 Phone: +91-721-2662206,07,08, Ext. 267

Contact person: Prof. Ramesh C. Deka Fax: +91-721 2660949

Contact person: Prof. Aniket Gade

3. Department of Biochemistry/Microbiology

Shivaji University Kolhapur

Kolhapur-416004

Contact person: Prof. K. D. Sonawane

Journal of Interdisciplinary Science

We invite research and review articles for the introductory issue of ‘Journal of Interdisciplinary Science’.

Readers are requested to visit the journal website (will be available soon) for further announcements. We

look forward for your active cooperation.

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I R S A P S B u l l e t i n 2 0 1 1 , V o l . 1 , I s s u e 3 © I R S A P S

A.vi

Contents

1. Editorial 1

2. Magnetic nanocomposite films 2

3. Molecular Modeling Study of Hypermodified Nucleic Acid Base 3-hydroxynorvalylcarbamoyl adenine,

hn6Ade Present at 3'-adjacent Position in Anticodon Loop of Hyperthermophilic tRNAs 8

4. Microbial Genomics Tool (MGT 1.0) for Bacterial Codon Usage Analysis 16

5. An Applicaton of Radon And Wavelet Transforms for Image Feature Extraction 20

6. Use of Proteinase Inhibitors from Okra for Inhibiting the Helicoverpa armigera (Hubner) gut

Proteinases 25

7. Science cartoons B.i

1

Few lines from the editorial desk……………!

All over the world is celebrating the year

2011 as the international year of chemistry, with the

motto for the occasion “Chemistry – our life, our

future”, which signifies the importance of chemistry

for our existence and in our life. Chemistry is

considered as the Central Science among the three

branches of science. IRSAPS has been doing its bit in

the promotion of chemistry by publishing articles,

conducting webinars and more emphasis is been

given to popularizing science as a whole. In this issue

IRSAPS has aptly decided to amalgamate the

chemistry and life together to focus on

“Biochemistry” for the current issue along with the

articles from other branches of science as well. This

journal is providing a platform to promising young

researchers from all fields of science, engineering

and medicine for publishing their research work and

ideas, for the cause of promoting science.

In this issue there are five scientific articles

covering magnetic nanocomposite films, Radon and

Wavelength transforms, protease inhibitors,

molecular modeling and Microbial genomics and

couple of science cartoons. The first article is a brief

review on magnetic nanocomposite films and their

applications, while the second article is about

molecular modeling studies of hypermodified nucleic

acid base N6-(3-

hydroxynorvalylcarbamoyl) adenine. The third

article discusses a new microbial genomics tool for

the codon usage analysis while fourth article is on

the application of wavelet and Radon for the rotation

and translation invariant image transform analysis

and their use for image enhancement and feature

extraction. The fifth article provides a detail study of

the protease inhibitors based insect resistance

management strategies.

From the next issue, IRSAPS Bulletin will go

international and it will be released as „Journal of

Interdisciplinary Science‟. Accordingly, we plan to

publish articles covering broader subject areas. We

hope the current issue will give a glimpse of what

journal is aiming to bring the flavor of

interdisciplinary science into a single platform. We

hope everyone will enjoy reading it and appreciate it

as a source of promoting science. We look forward

for the active participation from the scientific

community.

-Aniket K. Gade

2

Magnetic nanocomposite films

Hardeep Kumar

Institute of Physics, University of São Paulo, São Paulo 05508-090, Brazil

Email: [email protected]

This is an article focusing on practical applications of certain nanocomposite (NC) materials, together with a brief overview of

the basic principle of their operation. The nanocomposites have been categorized in to two broad sub-classes - (I) magnetic

multilayers and (ii) granular nanocomposites. It has been shown that the operational principle of both sub-classes of NCs rely on

the mechanism of magnetoresistance (both GMR and TMR), a quantum mechanical phenomenon that characterizes a relatively

low resistant electrical conductivity for parallel spin ferromagnets as opposed to antiparallel spin orientations. The latter half of

the article shows practical applications of such conformational magnetization. It has been argued that electronic devices

functioning around the 1 GHz frequency range would benefit from the usage of FM-I granular films, a particular variety of soft

materials, a property attributed to a combination of electromagnetic shielding and extraordinary Hall resistivity.

1. Introduction

Nearly all natural and synthetic materials are

heterogeneous, i.e. they are microscopically built by

different components or phases. In nanocomposite (NC)

materials, one of the solid constituents traditionally exhibits

a nanoscale structure, with length scales up to 100 nm. The

concept of enhancing properties and improving

characteristics of materials through creation of multi-phase

NCs is not new. The idea has been practiced ever since

civilization started and humanity began producing efficient

materials for functional purposes. Typical examples of

naturally evolved nanocomposites (NCs) can be found in

the form of bone, tooths etc. Among the early examples of

human made NCs, the tempera colours used in the Ajanta

caves (200 BC), the Lycurgus Cup made by the Romans (in

400 AD) and Maya blue, a blue dye used by the Mayas (in

700 AD) are of particular interest. The multifunctional

properties of the NCs are often complex relations defined

by varying sizes, shapes and relative fractions of the

constituent components. The possibility of realizing unique

properties of NCs leads to pave the way to a broad range of

technological applications ranging from aerospace [1], gas

sensing [2], data storage [3,4], automobile industry [5],

medical [6], non-linear optics [7], to solar energy

applications [8]. The NCs can be processed in the bulk or

thin film form, but in order to realize compact and reduced

size technological devices/components the research in thin

films is under more attention. In NC thin films the

constituent components can be arranged principally in two

ways:

(a) (b)

Fig. 1 Schematic illustration of (a) multilayer and (b) granular nanocomposites (NCs). ‘A’ and ‘B’ represent the

constituents of the NCs, with the dimensions of at least both/one of A and B in nanometer range in multilayer/granular NCs.

A

A

A

B

B B

A

3

Fig. 2 Schematic illustration of GMR effect in Fe /Cr multilayer [9]

Fe

Fe

Cr H = 0

Fe

Fe

Cr H = - 40 KG

(i) Multilayer NCs: Layer by layer arrangement of

constituents, thickness of each layer (constituent) is in the

nanometer range (Fig.1 (a)).

(ii) Granular NCs: When one of the dimensions is in the

nanometer range i.e. zero dimensional or one dimensional

or two dimensional (see Figure 1(b).

2. Magnetic Nanocomposite films

2.1. Magnetic Multilayers

One of the important phenomena discovered in magnetic

multilayers eg. Fe/Cr is the Giant magnetoresistance

discovered by Baibich et al. [9] and simultaneously by

Binash et al. [10] in 1988. Magnetoresistance (MR) is the

change in electrical resistance of a conductor by a magnetic

field. In non-magnetic conductors, it is relatively small. In

magnetic materials and magnetic multilayers, the spin

polarization of the electrons leads to large MR effects in

small magnetic fields. The variation of the resistance as a

function of the magnetic field observed by Baibich et al.

for Fe/Cr multilayers at 4.2 K is shown in Figure 2. When

the magnetic field is increased, the configuration of the

magnetizations in neighboring Fe layers changes from

antiparallel to parallel, leading to a drop in the resistance

(see Figure 2). Since the reduction of the resistance is

significant [9, 10], this effect has been called Giant

Magnetoresistance or GMR. The physical origin of GMR

can be attributed to the influence of the electron spin the

electronic transport in ferromagnetic conductors i.e. spin

dependent scattering at the interfaces and on bulk of the

multilayer structures.

The tunnel magnetoresistance (TMR), which is

the newest type of the magnetoresistance effect, has

attracted more interest than AMR and GMR because of its

high magnetoresistance ratio at room temperature. The

multilayered device of the tunnel magnetoresistanec

structure consists of two ferromagnetic electrodes separated

by a very thin nonmagnetic insulator layer. The tunnel

current through the insulator layer depends on the

magnetization direction of the two ferromagnetic electrodes

relative to each other in the presence of an external

magnetic field. Imposing the spin conservation constraint

on the tunneling process, the tunneling conductance can be

written as a sum of two independent conduction channels:

one channel for each spin direction. The relative variation

of conductance and the density of states (DOS) of each spin

channel are then linked as follows in the Jullière formula:

TMR=

2P1 P2

1− P1 P2, where

ii

ii

iD+D

DD=P

4

Fig. 3 Schematic of spin dependent tunneling: Density of states (DOS) of two ferromagnetic electrodes in

antiparallel and parallel configuration in FM/I/FM layer [11].

`

Fig. 4 Schematic illustration of (a) multidomain, (b) single domain structures for bulk and NPs; each arrow represents the

magnetic moment of an atom, (c) Critical size of single domain and superparamagnetism of several materials, (d) shows the

coercivity of magnetic NPs as a function of size, and (e) the corresponding hysteresis loops as a function of size [11]

The )(D 1 and )(D 2 are DOS of the two

ferromagnetic electrodes at the Fermi level for the two spin

directions. Figure 3 shows the density of states for both

ferromagnetic electrodes in anti-parallel and parallel

configurations. In the antiparallel configuration, majority

(minority) electrons from the first electrode would seek

minority (majority) empty states in the second electrode

which would lead to low tunneling conductance/current. On

the other hand, in parallel configuration, minority electrons

(spin up or spin down) would pass into minority states and

majority electrons would pass into majority states leading

to high tunneling conductance/current.

2.2. Magnetic granular films

Magnetic granular films are the nanocomposte

films with a typical combination of magnetic nanoparticles

5

(MNPs) embedded at random in an immiscible non-

magnetic matrix (Figure 1(b)), exhibit a wide range of

novel properties associated with MNPs. First, MNPs can

respond to an external magnetic field without physical

contact, making them attractive for remote applications.

Second, as the size of the MNPs reduces from the bulk to

the nanoscale, different magnetic properties, compared with

their bulk counterparts, can be obtained. When particle size

is smaller than a critical size (Dcrit) as in Figure 4(c), multi-

domain magnetic structures in the bulk (Figure 4(a)) will

become single domain (Figure 4(b)). In the vicinity of Dcrit ,

the coercivity of MNPs is largest and will decrease as

particle size decreases, until it reaches the

superparamagnetic limit (Dsp), as defined in Figure 4(c) for

various materials, below which the coercivity is zero for all

sizes at room temperature (see Figure 4(d)) [12].

Superparamagnetism is a unique property of single domain

MNPs, and is determined by size, temperature and

measurement time. Finally, and more intriguingly, the

properties of MNPs are tunable as a function of particle

size, particle size distribution and interparticle interactions.

Depending upon nature of non-magnetic matrix, two types

of granular films can be considered:-

(1) Ferromagnetic metal-Metal (FM-M) granular films,

where immiscible matrix is a noble metal eg. Au, Cu and

Cr etc.

(2) Ferromagnetic metal-Insulator (FM-I) granular films,

where immiscible matrix is an insulator (I) eg. SiO2, Al2O3,

MgO, ZrO2 etc.

The work on FM-I granular films was pioneered

by Abeles et al. In the recent times, these granular films

have attracted a considerable attention because they exhibit

a wide variety of interesting properties in magnetism and

magneto-transport, which suggest their prospective

applications in multiple fields. For instance, MNPs

embedded in either insulating or metallic matrix show

peculiar magnetic or magneto-transport properties like

enhanced coercivity, superparamagnetism, high

permeability, high resistivity, GMR or TMR and giant Hall

effect (GHE). Further, out of FM-M and FM-I granular

films, FM-I granular films show superior magnetotransport

(GHE and magnetoresistance) properties. The attractive

applications of FM-I granular films include high coercivity

that is required for information storage, high permeability,

high resistivity for shielding and bit writing at high

frequencies, MR sensors and read heads, high sensitivity

Hall sensors [13]. In addition, FM-I granular films are

reported to be potential candidates for field emission and

solar energy applications also.

It is very important to prepare FM-I granular

films with controlled MNP size, uniform composition and

uniform thickness for most of the applications. A large

number of physical techniques like sputtering (radio

frequency and ion-beam), thermal co-evaporation, Pulsed

laser deposition (PLD) and ion-implantation; and chemical

routes eg. spin-coating and dip-coating have been used to

prepare FM-I granular films of different materials.

Amongst these techniques, sputtering is the best in terms of

film thickness and composition uniformity, and large area

deposition. In the following sections the important areas of

application of FM-I granular films will be discussed.

3. Applications of FM-I granular films

3.1 Tunneling Magnetoresiatance

TMR was studied first in Ni-SiO2 granular films by

Gittleman et al. in early 70s [14], they suggested spin-

dependent tunneling as the origin of MR effect and hence it

was attributed as tunneling magnetoresistance (TMR)

phenomenon. But the magnitude of TMR was even less

than AMR in Ni-Fe alloys, so was not of interest till

Fujimori et al.’s report of large TMR in Co: Al-O system

[15]. In FM-I granular films giant magnetoresistance is

observed when the volume fraction (xv) of magnetic

particles is below percolation threshold (xp), caused by

spin-dependent tunneling of conduction electrons at the

metal-insulator interfaces [15]. FM-I granular films are

important to study as it enriches the mechanism of TMR

and of observation of interesting effects like coulomb

blockade due to electrons tunneling into small metal

particle. Recently, the enhancement of MR caused by the

cotunneling effect with Coulomb blockade and other

magnetotransport properties, such as spin injection and

accumulation effect, has been found in granular films [16].

3.2 Extraordinary Hall effect

6

Fig. 5 (a) HRTEM micrograph, and (b) The dependence of complex permeability i on frequency f for the

(FeCo)57:(SiO2)43 granular film.

(a)

(b)

The Hall effect in semiconductors is the basis of many

devices in measuring magnetic fields. In nonmagnetic

metals, the ordinary Hall coefficient is low because of the

high carrier density. The stronger effect that Hall

discovered in ferromagnetic conductors came to be known

as the extraordinary Hall effect (EHE) or anomalous Hall

effect (AHE). Hall resistivity for magnetic materials is

expressed as:

MμR+BR=ρ sxy 00 (1)

where B is the magnetic induction, M is the magnetization,

μ0 is the magnetic permeability of free space, R0 is the

ordinary Hall coefficient and Rs is the extraordinary Hall

coefficient. The first term represents the ordinary Hall

effect while the second term, coming from the

extraordinary/spontaneous Hall effect, is a characteristic of

ferromagnetic materials, and is proportional to its

magnetization. The origin of the EHE lies in the spin-orbit

interaction present in a ferromagnet. Rs obey a power law

relationship with the electrical resistivity, given by Rs=αρn,

where α is a constant. Smit’s classical asymmetric

scattering gives the exponent n=1 while the quantum

mechanical side-jump scattering theory yields n=2. It is

reported that both ordinary and extraordinary Hall

resistivity increases ~ 102-103 and 103-104 times,

respectively for FM-I granular films (Ni-SiO2, Co-SiO2,

etc.) In the vicinity of percolation threshold (xp) compared

to the corresponding bulk FM material [17].

3.3 High frequency applications

With the development of telecommunication technology

and highly integrated electronic devices, electromagnetic

shielding has been intensively studied in the past years to

satisfy the requirements of reducing undesirable

electromagnetic radiation and protecting delicate

components from possible electromagnetic interference. It

is well known that a highly permeable material can increase

the inductance of an inductor, generally by a factor of the

relative permeability of the material. Thus a substantial

increase in inductance and hence in the quality factor can

be obtained if no extra losses are produced by the magnetic

material. The two main loss mechanisms in an inductive

material at high frequencies are the ferromagnetic

resonance FMR frequency and eddy current losses.

(2)

Whereas, 10

+Hμ

M=μ

k

s'

r (3)

is derived from the Landau-Lifshitz-Gilbert equation. Hk is

the anisotropy field and the gyromagnetic factor. It is thus

in general necessary to maximize MS and pick a reasonable

value for Hk (trade-off between and fFMR) in order to

achieve a high FMR frequency. Eddy current losses are

minimized by having a high resistive material and a small

characteristic dimension (e.g. layer thickness). Of course, a

high relative permeability μr

'is desirable, since μ

r

'is

directly related with the level of the output signals of the

RF magnetic devices. The possible material candidates for

high frequency applications are:-

7

(i) Ferrites: Ms is small => μr

' is low and fFMR is also

relatively low. Therefore, bulk ferrites are not widely used

in high-frequency applications, although they are mostly

insulators

(ii) Ferromagnetic metal/alloys: Ms is large and Hk is

small => μr

' is large. But small resistivity (ρ) value

implies Large eddy current losses. Therefore FM metals are

not suitable for practical use in high frequency applications.

(iii) FM-I granular films (xv>xp): The FM-I granular films

consist of nano sized particles, which are separated by

insulating regions. This microstructural feature leads to

achieve a high resistivity (ρ). Secondly, if the size of NPs is

reduced less than a critical length known as exchange

length (Lex), exchange coupling between the magnetic

particles takes place. This forces the magnetizations of

particles to be aligned parallel, therefore, leading to a

cancellation of magnetic anisotropy and the compensation

of the demagnetization effect of individual particles. As a

result, the average anisotropy (Hk) of the film and hence the

coercivity Hc reduce considerably. Thus, the FM-I granular

films are expected to have a high μr

' value and low eddy

current losses even in the high frequency region.

Bulk Co, Fe and FeCo, have the highest Ms

values of 2.3, 2.1 and 1.79 emu/cc, respectively among the

magnetic materials and one expects good high frequency

response of FM-I granular films based on Co, Fe, FeCo.

There are many works on Co, Fe and FeCo based FM-I

(where I: SiO2, Al2O3, ZrO2 etc.) granular films in literature

for high frequency applications [17]. Figure 5(a) and (b)

shows the HRTEM micrograph and The dependence of

complex permeability μ= μ'− i μ ' ' on frequency f for

the (FeCo)57:(SiO2)43 granular film, it is clear from Figure

5(b) that this granular system can be used upto 1 GHz

range [18].

4. Summary

In this article we have mainly focused on two kinds of

magnetic nanocomposite (NC) structures: (i) Multilayer

and (ii) granular NCs . In FM-M (FM-I) based granular

films GMR (TMR) effect is observed for FM volume

fraction, xv<xp. In FM-I granular films an enhancement in

ordinary (x102-103) and extraordinary Hall resistivity

(x103-104) than corresponding FM is observed near

percolation threshold (xv<xp) than corresponding FM

counterpart and can be used in Hall sensors applications.

FM-I granular films are best soft materials for integrated

electronic devices employed in near 1GHz range

applications for electromagnetic shielding.

5. References

1. Voevodin A. A., O’Neill J. P., and Zabinski J. S. Surface

and Coatings Tech. (1999) 116, 36.

2. Julián Fernández C. de, Manera M. G., Spadavecchia

J., Maggioni G., Quaranta A., Mattei G., Bazzan M.,

Cattaruzza E., Bonafini M., Negroa E., Vomiero A.,

Carturan S., Scian C., Della Mea G., Rella R., Vasanelli L.,

and Mazzoldi P. Sensors and Actuators B (2005) 111, 225.

3. Huajun Z., Jinhuan Z., Zhenghai G., and Wei W. J.

Magn. Magn. Mater. (2008) 320, 565.

5. Usuki A., Kawasumi M., Kojima Y., Okada A.,

Kurauchi T., and Kamigaito O. J. Mater. Res. (1993) 8,

1174.

6. Benzaid R., Chevalier J., Saâdaoui M., Fantozzi G.,

Nawa M., Diaz L. A., and Torrecillas R., Biomaterials

(2008) 29, 3636.

7. Tatsuma T., Takada K., and Miyazaki T., Adv. Mater.

(2007) 19, 1249.

8. Wang M., Lian X., and Wang X. Curr. Appl. Phys.

(2009) 9, 189.

9. Baibich M. N., Broto J. M., Fert A., Nguyen Van Dau F.,

Petroff F., Etienne P., Creuzet G., Friederich A., and

Chazelas J. Phys. Rev. Lett. (1988) 61, 2472.

10. Binash G., Grünberg P., Saurenbach F., and Zinn W.

Phys. Rev. B (1989) 39, 4828.

11. Schuhl A. and Lacour D., C. R. Physique (2005) 6,

945.

12. Wen, T. and Krishnan K. M. J. Phys. D: Appl. Phys.

(2011) 44, 393001.

13. Kumar H., Ghosh S., Bürger D., Zhou S., Kabiraj D.,

Avasthi D. K., Grötzschel, R., and Schmidt H. J. Appl.

Phys. (2010) 107, 113913.

14. Gittleman J. I., Goldstein Y., and Bozowski S. Phys.

Rev. B (1972) 5, 3609.

15. Fujimori H., Mitani S., and Ohnuma S., Mater. Sci.

Eng. B (1995) 31, 219.

16. Yakushiji K., Ernult F., Imamura H., Yamane K.,

Mitani S., Yakanashi K., Takahashi S., Maekawa S., and

Fujimori H. Nat. Mater. (2005) 4, 57.

17. Denardin J. C., Knobel M., Zhang X. X., and

Pakhomov A. B. J. Magn. Mater. (2003) 262, 15.

18. Ge S., Yao D., Yamaguchi M., Yang X., Zuo H., Ishii

T., Zhou D., and Li F. J. Phys. D: Appl. Phys. (2007) 40,

3660.

8

Molecular modeling study of hypermodified nucleic acid base 3-

hydroxynorvalylcarbamoyl adenine, hn6Ade present at 3'-adjacent position in

anticodon loop of hyperthermophilic tRNAs

Bajarang V. Kumbhar and Kailas D. Sonawane

*

Structural Bioinformatics Unit, Department of Biochemistry, Shivaji University, Kolhapur. 416 004, India

Phone: +91 9881320719, +91 231 2609153, Fax No: +91 231 2692333

*Email: [email protected]

Conformational preferences of hypermodified nucleic acid base N6-(3-

hydroxynorvalylcarbamoyl) adenine, hn6Ade have been investigated theoretically using PCILO, RM1 and HF-SCF

methods. Automated geometry optimization using Density Functional Theory (B3LYP/6-31G** basis set) has also been made to

compare the salient features. Molecular dynamics (MD) simulations have been performed on the preferred conformations of

hn6Ade to find out the hydration effect. The preferred conformation of hn6Ade is such that the N6-(3-

hydroxylnorvalylcarbamoyl) side chain spreads away ‘distal’ from the five membered imidazole moiety of adenine. The atoms

N(6), C(10) and N(11) of ureido group as well as amino acid atoms such as C(12) and C(13) remains coplanar with the purine

base in the preferred conformations. The most stable structure of hn6Ade is stabilized by the intramolecular interactions between

N(1)…HN(11) which would be useful to protect the N(1) site of adenine from participating in the usual Watson-Crick base

pairing at 3'-adjacent (37th) position of anticodon loop of tRNA. This may help maintain proper reading frame of mRNA during

protein biosynthesis process. MD simulation study of hn6Ade reveals that free rotations around the bond N(11)-C(12) could be

possible. The characteristics feature of this modified base is the presence of methyl group which is involved in the interaction

between O(13)…HC(15). These interactions could play an important role in the stabilization of tRNA structure at elevated

temperatures in case of hyperthermophilic organisms.

1. Introduction

The hypermodified nucleosides naturally occur at

34th and 37th positions in the anticodon loop of tRNA from

all domains of life.1-3 These modified components are

derivatives of the four common ribonucleosides. Most of

the modifications involve simple alkylation, hydrogenation,

thiolation or isomerization of these four common

ribonucleosides in the base and the 2'-hydroxyl group of the

ribose. However, some modifications involve complex

chemical modifications which are characterized by the

presence of diverse functional groups in base substituents,

such tRNA components are referred as hypermodified

nucleosides. Hypermodified nucleosides N6-(3-

hydroxynorvalylcarbmoyl) adenine, hn6Ade and its 2-

methylthio derivative N6-(3-hydroxynorvalylcarbmoyl)

adenine, mS2hn6Ade which occur at the 3'-adjacent (37th)

position in anticodon loop of tRNA of hyperthermophilic

bacteria and archaea.4 The anticodon 3'-adjacent

modifications help define reading frame for the codon-

anticodon interaction by preventing extended Watson-Crick

base pairing whereas, the modifications present at 34th

position may restrict or enlarge the scope of wobble base

pairing.5-7

Transfer RNA which recognizes codons starting

by U contain hydrophilic modified nucleosides such as

t6Ade, m6t6Ade and mS2t6Ade occurs at the 3'- adjacent

position of anticodon loop of tRNA.3,8 The orientation of

the N(6) substituent in t6Ade, m6t6Ade, and mS2t6Ade has

been found to be ‘distal’ (spreads away from the N(7) of

adenine ring) in the crystal structure9 as well as predicted

theoretically by using quantum chemical PCILO method.10-

11 In these modifications the N(6) substituent spreads away


9

Fig. 1 Atom numbering and nomenclature for the various torsion angles of hn6Ade. A fully extended (all trans) but proximal

conformation is shown here.

from the five membered imidazole moiety of the adenine

ring and becomes inaccessible for participation in the usual

Watson-Crick base pairing with codons and thus help

define the proper reading frame for the codon-anticodon

interaction during protein biosynthesis process.

The previous studies on the conformational

preferences of the hypermodified bases i6Ade and its

mS2i6Ade along with its hydroxylated derivatives like cis-

io6Ade, trans-io6Ade, cis-mS2-io6Ade and trans-mS2io6Ade

along with various forms of the lysidine (k2C) have been

studied computationally 12-13. Recently, multiple iso-

energetic conformations of wybutine (yW)14 and

conformational preferences of m2G and m22G have also

been reported.15

The structural significance of hn6Ade has not

been investigated by any experimental methods. Hence,

present study has been performed to understand the

conformational preferences of hypermodified nucleic acid

base, N6-hydroxynorvalylcarbamoyl, hn6Ade using various

energy calculation and MD simulation methods. It is also of

interest to find out the structural role of hydrophobic –

CH2CH3 group present in the side chain of hn6Ade. It has

found that 3-hydroxynorvalylcarbamoyl substituent spreads

away from the five membered imidazole moiety of adenine

preventing N(6)H and N(1) site in the usual Watson-Crick

base pairing.

1. Nomenclature, Conventions and procedure

Figure 1 depicts the atom numbering and

identification of the various torsion angles describing

rotations around the respective acyclic chemical bonds. In

the N(6) substituent the torsion angle

[N(1)C(6)N(6)C(10)] describing rotation around the bond

C(6)-N(6) and measures the orientation of the bond N(6)

and C(10) with respect to the

C(6)N(1) from the cis (eclipsed,0) position in the right-

hand sence of rotation. Likewise, the torsion

angles [C(6)N(6)C(10)N(11)], [N(6)C(10)N(11)C(12)],

[C(10)N(11)C(12)C(13)],Ө[N(11)C(12)C(13)C(14)],

ψ1[C(12)C(13)C(14)C(15)], ψ2[C(13)C(14)C(15)H],

ω[C(12)C(13)O(13)H], φ1[N(11)C(12)C(16)O(16a)],

φ2[C(12)C(16)O(16a)H] define the rotation of the

successive chemical bonds along with the main extension

of the substituent. The extended conformation with the

adopted convention has been chosen initially as a reference

point in the energy calculations. The standard bond length

and bond angle values are retained from the earlier

10

Table 1 Torsion angle values of the starting structures obtained by PCILO method (Conformer I and II) for hn6Ade molecules

Sr.

No

Torsion Angle

(degree)

Relative

Energy

hn6ade molecule:

I

01800θ=300, 0, 0, ω60 φ1= 90 φ2=180. 0.0

III 01800θ=300, 0, 0, ω0φ1=300φ2=180. 2.0

Table 2 Full geometry optimization calculation using semi-empirical RM1and PM3 methods over the PCILO starting conformer

I and II of hn6Ade

Conformer

Torsion Angle (degree) Relative

Energy

α β δ θ ψ1 ψ2 ω φ1 φ2

hn6Ade

RM1

I 2 357 171 281 295 188 181 71 48 186 0.00

II 1 337 171 284 293 187 180 72 233 175 1.06

PM3

I 12 336 160 279 308 202 181 56 71 184 22.62

III 11 335 162 283 303 201 180 54 251 172 22.92

investigation on t6Ade9 because N6-(3-

hydroxynorvalylcarbamoyl) adenine, hn6Ade is an

analogue of N6-threonylcarbonyl adenine, t6Ade.

2.1. Conformational search and geometry optimization

The conformational space has been searched for

the modified nucleic acid base hn6Ade using quantum

chemical PCILO method.16-18 This method has been found

useful in conformational analysis of many bio-organic

molecules including nucleic acid constituent.19-20 In PCILO

method polarities of each bond in the molecule are

optimized throughout the conformational energy

calculation and energy correction terms up to third order

are retained for each conformation. In logical selection of

grid points approach is used for searching the most stable

structure and the alternative stable structure.21

Conformational search by PCILO method resulted into two

conformations (conformation I and II) and these

conformations are then used as starting structures for the

full geometry optimization calculations using PM322 and

RM123 methods in order to find out the most stable

structure of hn6Ade. The lowest energy stable structure is

then again optimized at ab-initio level using Hartree-Fock

SCF (6-31G**) method.24 In this way most stable structure

of hypermodified nucleoside, hn6Ade obtained by HF-SCF

(6-31G**) method using the PC Spartan Pro version 06

V1.1.0 software.

2.2. Molecular dynamics simulation study

To investigate the hydration effect on the

modified base hn6Ade we performed molecular dynamics

(MD) simulation study using Sybyl 7.3 commercial

software from Tripos, Inc.25 The PCILO-RM1-HF

optimized preferred conformations of modified base

hn6Ade used as a starting geometry for molecular dynamic

simulation. Kollman-all-atom force field26 with Gasteiger-

Marsilli charges and TIP3P model water has been chosen

for molecular dynamics simulation study. Minimal cubic

periodic boundary conditions of diameter 35.968Å have

been applied. Trajectories are taken for time span of 10 ps.

The constant temperature (canonical ensemble) simulation

at 300 K were used along with 8 Å-non bonded cut off and

dielectric function ‘constant’ held at 1. For temperature

ramp from 0 K to 200 K, 10 ps interval of 50 K and for 200

11

Fig. 2 Most stable structure of hn6Ade (PCILO conformer I) obtained by PCILO-RM1-HF 6-31G** optimization (α=1º,

β=356º, γ=173º, δ=276º, θ=297º, ψ1=185º, ψ2=180º, ω=59º, φ1=52º, φ2=182º).

Table 3 Geometrical parameters for hydrogen bonding interactions in the PCILO-RM1-HF optimized stable conformations

of hn6Ade (Figure 2).

Atom involved

1-2-3

Distance

atom Pair

1-2 A°

Distance

atom Pair

2-3 A°

Angle

1-2-3

degree

Figure

Ref.

N(1)...HN(11)

O(16b)...HO(13)

O(13)...HC(15)

2.026

2.441

2.600

0.996

0.945

1.083

132.21

121.01

95.00

2

2

2

K to 300 K, 10 ps interval of 25 K temperature steps were

used. The other usual conditions applied includes 1 fs time

step, initial Boltzmann velocity distribution, and shake

algorithm for hydrogen atoms, 10 fs non-bonded update

with scaled velocities. To remove steric clashes initially,

5000 cycles of steepest descent minimization steps were

applied to the whole system. This minimized system

considered for 200 ps equilibration protocol followed by

5000 cycles of steepest descent energy minimization.

Finally system is subjected for 1ns of production run time

by maintaining all parameters as described above. All

calculations were performed on HP xw8600 workstation.

3. Results and Discussion

3.1. Conformational search by PCILO method

Table 1 depicts the torsion angle values of conformation I,

and II of hypermodified nucleoside hn6Ade obtained after

the multidimensional conformational search carried out

using semi empirical PCILO method. The relative energy

difference between conformations I and II found below 2.0

Kcal/mol (Table 1). These two conformations are

considered as starting conformers in this study. The

structural properties of hn6Ade are not studied by

crystallographically or by using NMR. Hence, in order to

search the whole conformational space of hn6Ade, the full

geometry optimization has been performed over the

conformation I and II (Table 1) using semi-empirical RM1

and PM3 methods and results are shown in (Table 2). The

relative energies of geometry optimized conformations

using RM1 and PM3 methods are then compared to

indentify energetically stable conformer of hn6Ade. It has

been revealed that conformation I obtained by PCILO-RM1

optimization is found energetically stable conformation as

compared to conformation II (Table 2). Hence, PCILO-

RM1 optimized conformation I (Table 2) is then subjected

12

Table 4 Geometrical parameters for the torsion angles and hydrogen bonding interactions for average structure and snapshot

structures after molecular dynamics simulation study of PCILO-RM1-HF optimized stable structure of hn6Ade.

Average/

snapshots

structures

(ps)

Torsion angle

(degree)

Atoms involved

(1-2-3)

Distance

atom pair

1-2 (A)

Distance

atom

pair 2-3

(A)

Angle

1-2-3

(degree)

Figure

No.

0-300

θ=277, ,

, ωφ1=54

φ2=239.

N(1)...HN(11)

O(10)...HO(13)

2.248

1.701

1010

0.960

119.41

166.13

3a

350

θ=179, , ,

ω

φ1=127φ2=340

N(1)...HN(11)

2.401

1.010

127.79

3b

550

θ=181, , ,

ωφ1=287

φ2=15

N(1)...HN(11)

O(13)...HN(11)

2.058

2.574

1.010

1.010

132.22

92.27

3c

Fig. 3 A) 1ns MD simulated average structure of hn6Ade at 0-300 ps. B) Snapshot structure of hn6Ade at 350 ps. C) Snapshot

structure of hn6Ade at 550 ps.

to full geometry optimization with the help of Hartree-Fock

Self Consistent Field (HF-SCF) method using 6-31G**

basis set to find out preferred most stable conformation of

hypermodified nucleoside, hn6Ade.

3.2. Geometry optimized stable conformation of hn6Ade

The predicted most stable structure of the hypermodified

nucleic acid base N6-(3-hydroxynorvalylcarbamoyl)

adenine, hn6Ade obtained by PCILO-RM1-HF (6-31G**)

optimization is depicted in Figure 2. The optimized

torsion angles describing the conformation are α=1º,

β=356º,γ=173º, δ=276º, θ=297º, ψ1=185º, ψ2=180º, ω=59º,

φ1=52º, φ2=182º. This most stable conformation of hn6Ade

may be compared to the crystal structure of t6Ade9-10, an

analogue of hn6Ade. The N(6) substituent 3-

hydroxynorvalylcarbamoyl side chain spreads away ‘distal’

from the five membered imidazole moiety of adenine ring

as observed in N(6)–threonylcarbamoyl adenine t6Ade 9-10,

m6t6Ade and mS2t6Ade.11 This kind of orientation prevents

13

Fig. 4 Molecular dynamics simulation results of hn6Ade. A) Stabilization in α torsion angle. B) Stabilization in torsion

angle. C) Stabilization in torsion angle. D) Fluctuations in torsion angle. E) Fluctuations in torsion angle. F) Fluctuations

in 1 torsion angle. G) Fluctuations in 2 torsion angle. H) Fluctuations in torsion angle. I) Fluctuations in 1 torsion angle.

J) Fluctuations in 2 torsion angle. K) Fluctuations in hydrogen bonding interaction between N(1)…HN(11).

extended Watson-Crick base pairing of adenine base at 3'-

adjacent (37th) position and thus avoid misrecognition of

codons. The intramolecular interactions (Table 3) between

N(1)…HN(11), O(16b)…HO(13) and interaction between

O(13)…HC(15) may provide stability to the structure

(Figure 2). Due to series of conjugated bonds extending the

partial double bond character from the adenine ring through

N(6), C(10), O(10) and N(11) the torsion angles α, β and γ

are essentially constrained to adopt planar cis or trans

orientation. In addition to this the strong steric repulsion

from proximal orientation of N(6) substituent atoms to N(7)

ruled out the trans orientation of α torsion angle. The

hydrophobic –CH2CH3 group of hn6Ade prefers extended

conformation forming an intramolecular interactions within

the molecule. The interaction between O(13) and HC(15)

of hydrophobic –CH2CH3 group observed the in most

stable and alternative stable conformations of hn6Ade could

play an important role during codon-anticodon interactions

in hyperthermophiles. This extra hydrophobic group

present in hn6Ade as compared to t6Ade may be helpful for

the growth of hyperthermophilic bacteria and archaea at

elevated temperatures.4

3.3. MD simulation of PCILO-RM1-HF optimized

stable structure of hn6Ade

Molecular dynamics (MD) simulation has also

been performed to explore the conformational space of

hypermodified nucleic acid base hn6Ade using Sybyl7.3

software.25 The PCILO-RM1-HF optimized stable structure

14

(Figure 2) is used as starting geometry for 1ns MD

simulation study. The results of torsion angle and

geometrical parameters for the average structure and

snapshot structure are shown in the Figures 3A-C and

Table 4. We analyzed average structure at 0-300 ps and

snapshot structures at 350 ps and 550 ps to compare the

conformational preferences of most stable structure

obtained by PCILO-RM1-HF optimization (Figure 2). The

average and snapshot structures maintain ‘distal’

orientation of the N(6)-substituted side chain i.e. spreads

away from the five membered imidazole moiety of adenine

ring as observed in the most stable structure of hn6Ade

(Figure 2). The uriedopurine ring as well as intramolecular

interaction between N(1)…HN(11) (Figure 4K) are well

maintained during 1ns molecular dynamics simulation

period as observed in the PCILO-RM1-HF optimized

structure (Figure 2).

The average structure (0-300 ps) having torsion

angle values are (θ=

277, , , ω23φ1=54φ2=239). The

torsion angle values for α, β, γ, φ1 and show small

differences whereas the θ and ψ1 changes by 20, ω varies

about 30 whereas φ2 shows large variation as compared to

stable structure (Figure 2). The average structure is

stabilized from the hydrogen bonding interactions between

N(1)…HN(11) and O(10)…HO(13) (Figure 4A and Table

4). The snapshot structure taken at 350 ps (Figure 3B) also

shows basic interaction between N(1)…HN(11) as

observed in (Figure 2). The 3-hydroxylnorvalycarbamoyl

side chain maintains ‘distal’ orientation (Figure 3B and

Table 4). The next snapshot structure taken at 550 ps also

maintains interaction between N(1)…HN(11) and

O(13)….HN(11) as shown in (Figure 3C).

The interaction between O(13)….HN(11) suggest that the

hydroxyl group ‘HO(13)’ of norvalylcarbamoyl group of

hn6Ade orient towards the N(1) site of adenine and could

play an important role to prevent extended Watson-Crick

hydrogen bonding from 3'-adjacent site of anticodon loop

of tRNA. This proves that the rotations around the bond

N(11)-C(12) are possible in case of hn6Ade also similarly

as explained in the crystal structure of t6Ade.27 The average

(0-300 ps) as well as snapshot structures 350 ps and 550 ps

maintained the uriedopurine ring as well as hydrogen

bonding interaction between N(1)…HN(11) (Figure 3A, B

and C). The geometrical parameters for torsion angle

values and hydrogen bonding interaction for the snapshot

structure are listed in the Figures 3B-C and Table 4. The

above discussed snapshot structure taken at 350 ps (Figure

3B) and 550 ps (Figure 3C) clearly show that the norvalyl

group is free to rotate around the bond N(11)-C(12), these

results are in close agreement with experimental study of

modified base t6Ade.27

The fluctuations in the torsion angle Figure

4 (A-B) maintained well during 1ns MD simulation study

whereas torsion angle Figure 4C fluctuates between

180 which indicates that the uriedopurine ring as well as

hydrogen bonding interaction between N(1)…HN(11)

(Figure 4K) would be important for the orientation of the

N(6)-substituted side chain to ‘trans’ whereas other torsion

angles show fluctuations over 1ns MD simulation period

(Figure 4). The torsion angle Figure 4Dand θ Figure

4E maintained their initial values up to 500 ps and then

fluctuates between 120-180after ps till end of the

simulation period. The next torsion angle φ1 Figure

4Ifound well maintained up to 0-300 ps after that it

fluctuates between -120 to -150 up to 500ps.Torsion

angles Figure 4FFigure 4GFigure

4HandFigure 4J show fluctuations between 180.

The fluctuations of torsion angles as shown in (Figure 4D-

J) and above discussed snapshot structures (Figures. 3B,

C), it clearly indicates that the norvalyl group of hn6Ade is

free to rotate around the bond N(11)-C(12) as similarly

shown in previous experimental study of t6Ade 27 which is

an analog of hn6Ade having extra methyl group. The

hydrophobic –CH2CH3 group of hn6Ade point towards the

N(1) site of adenine and thus could interact with codons if

present at 3'-adjacent side of anticodon loop of tRNA.

4. Conclusions

Conformational preferences of modified base,

hn6Ade performed using PCILO method followed by semi

empirical RM1-HF optimization along with molecular

dynamics simulation study shows that N(6) substituted 3-

15

hydroxynorvalylcarbamoyl side chain of hn6Ade prefers

‘distal’ conformation. The most stable and alternative

stable conformations are stabilized by the hydrogen

bonding interaction between N(1)…HN(11) of 3-

hydroxynorvalylcarbamoyl side chain which is a

characteristic feature of uriedopurine as found in earlier

studies on t6Ade 10, mS2t6Ade and m6t6Ade.11 This

intramolecular interaction may help prevent extended

Watson-Crick base pairing at 3´-adjacent (37th) position

during codon-anticodon interactions. In addition to this the

most stable structures of 3-hydroxynorvalylcarbamoyl

substituent of hn6Ade (Figure 2) shows intramolecular

interaction between O(16b)…HO(13) and a weak

interaction between O(13)…HC(15) which might play an

important role in the stabilization of tRNA structure of

hyperthermophilic organisms at higher temperature range.

Molecular dynamics (MD) simulation study

clearly shows that the norvalylcarbamoyl group of hn6Ade

is free to rotate around the bond N(11)-C(12) similarly as

observed in earlier experimental study of modified base

t6Ade.27 Intramolecular interactions between

N(1)….HN(11) and O(13)….HN(11) of hn6Ade also

maintained during MD simulation study as observed in

PCILO-RM1-HF preferred structure. The extended

orientation of hydrophobic –CH2CH3 group of hn6Ade

towards the N(1) site of adenine base might provide

hydrophobic environment at 3'-adjacent site of tRNA

anticodon loop during codon-anticodon interactions. Such

orientation of –CH2CH3 group could also play an important

role in the translocation process in order to have smooth

and in phase protein biosynthesis process of

hyperthermophiles at elevated temperatures.

Acknowledgements

KDS is gratefully acknowledged to Department of Science

and Technology (DST), New Delhi (No.SR/FT/LS-

028/2007) and University Grants Commission, New Delhi

for financial support under the scheme UGC SAP DRS-I

sanctioned to Department of Biochemistry, Shivaji

University, Kolhapur. BVK is gratefully acknowledged to

University Grants Commission, New Delhi for providing

fellowship as a Project Fellow under the scheme UGC SAP

DRS-I.

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Chim. Acta. (1969), 15, 100.

19. Pullman B., and Pullman A. Adv. Protein. Chem.

(1974) 28, 347.

20. Pullman B., and Saran A. Prog. Nucleic. Acid. Res.

Mol. Biol. (1976) 18, 215.

21. Tewari R. Int. J. Quant. Chem. (1987) 31, 611.

22. Stewart J. J. P. J. Comp. Chem. (1991) 12, 320.

23. Rocha G. B., Freire R. O., Simas A. M., and Stewart J.

J. P. J. Comp. Chem. (2006) 27, 1101.

24. Hehre W. J., Radom L., Schleyer P. V. R., and Pople J.

A. In Ab Initio Molecular orbital Theory, Wiley, New

york, (1986).

25. Tripos International (2006) SYBYL 7.3, Tripos

International, South Hanley Rd., St. Louis, Missouri,

USA

26. Weiner C. J., Kollman P. A., Case D. A., Singh U. C.,

Ghio C., Alaqom G., Protera S., and Weiner P. J. J. Am.

Chem. Soc. (1984) 106, 765.

27. Murphy F. V., Ramakrishnan V., Malkiewicz A., and

Agris P. F. Nat. Struct. Mol. Biol. (2004) 11, 1186.

16

Microbial Genomics Tool (MGT 1.0) for Bacterial Codon Usage Analysis

Rajendra Verma1, Ragini Gothalwal

1, Kamalraj Pardasani

2, Anil Prakash, Kishor Shende

1*

1. Bioinformatics Center (SubDIC), Dept. of Biotechnology, Barkatullah University Bhopal M.P. India

2. BIF, Department of Applied Mathematics, MANIT, Bhopal M.P. India

*E-mail: [email protected]

Richard Grantham (1980) proposed genome hypothesis stating that codon catalogue can be a genomic feature to study the genome

variability. New genome sequencing technology has resulted into flood of genome sequences in databases. Looking towards the need of

future of bacterial genome analysis, MGT (Microbial Genomics Tool) development was initiated. MGT is developed with java

programming language with user-friendly interface. It can calculate codon usage frequency and indices. It also gives nucleotide

composition with e-translated protein sequences. It produces result in MS-Excel and text file format, which can be further processed for

statistical analysis. This software is open access and it can be obtained from Source forge web site

(http://sourceforge.net/project/micromictool/). It has the utility in research and teaching of bacterial genomics. Future development plan for

MGT is inclusion of statistical analysis application and microarray analysis.

1. Introduction

Over the past three years, parallel DNA sequencing

platform have reduced the cost of DNA sequencing. Next

generation sequencing has the potential to dramatically

accelerate biological and biomedical research, by enabling the

comprehensive genome sequencing and analysis1. More than

7000 sequenced bacterial genomes are available at NCBI ftp site

(http://ncbi.nlm.nih.gov). Chromosomal DNA stores (RNA in

RNA viruses) genetic information to carry out cellular processes

and same is transferred generation after generation. Three

important processes viz. replication, transcription and

translation are meant to transfer the genetic information to form

functional assembly, a cell. Translation is the process where

signals in nucleotide sequence form is converted to sequence

amino acid. There are 64 possible codons and 20 amino acids;

hence the code is redundant and multiple codons can specify the

same amino acid according to Wobble hypothesis given by

Crick. Multiple codons coding for single amino acid are called

synonymous codons. The correspondence between codons and

amino acids is nearly universal among all known living

organisms with a few variations. Different organisms often

show different preferences for synonymous codons called as

codon usage bias. The codon usage patterns differ significantly

depending upon several factors such as mutational bias, natural

selection for translation optimization. Codon usage analysis in

an organism helps in understanding the basis of molecular

biology of gene regulation and gene expression. This can

indirectly help in understanding the morphology, physiology

and phylogeny of organisms2-8.

The controversial ideas of Kimura, Kings and Jukes on

natural evolution led some early detractors to postulate that

usage of synonymous codon in protein coding genes is not

necessarily random and that codon composition could be biased

towards the codons that would match the tRNA pool of the host

organism. This prediction was partially confirmed by Grantham

and his co-workers. They compiled codon usage table for all the

sequences genes available at that time and proposed that each

genome has a particular codon usage signature that reflects

particular evolutionary forces acting with that genome2-4.

Consequently they proposed „Genome Theory‟. According to

this theory “Codon usage pattern of a genome was a specific

characteristic of an organism”. Organism specific codon usage

pattern suggested that the variation in codon usage pattern might

be correlated with variation in tRNA abundance2-3, which

ultimately affects the gene expression4. Early studies of E. coli

codon usage pattern showed remarkable variation in strongly

and weakly expressed genes4. A modulation of the coding

strategy according to expression was proposed such that codons

found in abundant mRNA were under selection for optimal

codon-anticodon pairing4. A later study in E. coli found that the


http://sourceforge.net/project/micromictool/

http://ncbi.nlm.nih.gov/

17

variation in codon usage is dependent on translational level and

the codon usage of abundant protein genes could be

distinguished from other genes9.

Codon usage pattern analysis is a method to understand the

bacterial genome. Various codon usage indices are formulated

to understand the codon usage bias and factor that affect this

biasing. Codon usage indices such as RSCU (Relative

Synonymous Codon Usage), CAI (Codon Adaptability Index),

CBI (Codon Bias Index), Third nucleotide composition of codon

(GC3), Nc (Effective number of codon), Fop (Optimal Codon

usage) etc. These indices can help to understand the factor

shaping the codon usage in different species, organisms, genera

or even different cellular processes in single organisms.

Increased involvement of computers and computational

techniques led to development of many user friendly software

tools. Simultaneously the advancement of Information

technology also led to storage of complex data and tools for this

type of data analysis. In 1999 CodonW was developed by John

Peden for comprehensive analysis of codon usage frequency.

This is designed to simplify the Multivariate Analysis

(Correspondence Analysis) of codons and amino acids usage. It

also calculates standard indices of codon usage. It has both

menu and command line interface10. Lacks of user friendly

interface is the main demerit of this tool but still it widely used

for codon usage analysis. ACUA11 software can calculate most

of codon usage indices, codon frequency, RSCU, CAI values,

C3s, G3, T3, and A3s; and also the result can be visualized in

MS-Excel file. Major drawback is it lacks the major multivariate

analysis algorithms and also it is not updated since long. E-

CAI12 server side tool is to estimate an expected value of Codon

Adaptation (eCAI). JCat13 is a novel tool, which calculates the

codon usage adaptability of a target gene to its potential

expression host. It is a server side web application, designed in

java. Other software such Jemboss14 and BioEdit15 can calculate

codon usage frequency and RSCU values, but they can process

only a single sequence or sum of codon frequency of all the

ORFs (Open Reading Frames) present in the file. Most of these

softwares are suitable enough to work on specific single task.

CodonW is one suit able to calculate most of the codon usage

indices. But it has command line operation without any

graphical interface. EMBOSS18, Jembos14 and BioEdit15 can

calculate only codon usage frequency and RSCU values. Some

of them can calculate only CAI (Codon Adaptation Index)

values. ACUA11 is not updated since long and some of its

application doesn‟t work.

This project was undertaken to develop a software tool

MGT (Microbial Genomics Tool) that will be user friendly and

will provide the output format suitable for statistical analysis in

most of the Windows based statistical analysis software tool.

The provision is also made to implement the inclusion of new

applications which will be developed in future. MGT was

planned for open access.

2. Methodology

2.1. MGT Development

MGT is a standalone software tool developed in java

programming language on NetBeans IDE 7.0.1

(http://netbeans.org ). User-friendly interface was developed

using Java swing package. MGT interface is shown in Fig-1.

Installation package of MGT was created in MSI format

(Windows Installation Technology) using software “Advanced

Installer 8.6” trail version. (http://www.advancedinstaller.com)

2.2. Codon Usage Indices

2.2.1. Relative Synonymous Codon Usage (RSCU)5: RSCU is

calculated as the observed codon usage divided by the average

codon usage for that amino acid (equation). A value of 1.00 is

obtained if all codons for a particular amino acid are used

equally. RSCU removes the influence of amino acid

composition that is present in raw codon usage data.

Eq. (2.2.1)

Where, Xij is the frequency of the jth codon for the ith amino

acid, encoded by in synonymous codons.

2.2.2. Synonymous site composition statistics15: The GC3

value is the fraction of codons, which are synonymous at the

third codon position and have either a „G‟ or a „C‟ at that codon

position. Similar way AT3s can be calculated.

Eq. (2.2.2)

http://netbeans.org/

http://www.advancedinstaller.com/

18

Where, NNU, NNG, NNC etc. refer to the total number of

codons of that form

2.2.3. Effective Number of Codons (Nc)15: The effective

number of codons provides a way to quantify differential codon

usage of a particular gene to the equal use of synonymous

codons. Nc is an estimate of the strength of general codon usage

bias. It may be influenced by mutation biases and/or selection

for particular codons. The genetic code has five amino acid

family types (non-synonymous, 2- fold, 3-fold, 4-fold and 6-fold

synonymous amino acids). The Nc value is calculated as the

arithmetic average of all non-zero homozygosity values for each

of the amino acid family types.

Eq. (2.2.3.1)

Where, Fi - average homozygousity for the class with „i‟

synonymous codons

Homozygosity for each amino acid is estimated from the

squared codon frequencies.

Eq. (2.2.3.2)

Where, k - number of synonyms; n - total usage of k-fold

synonymous amino acid; F - homozygosity; Pi = frequency of

usage of „ith‟ synonymous codon.

Expected value of Nc if codon bias is solely a function of GC3s.

Eq. (2.2.3.3)

Where, S - frequency of G+C (i.e. GC3)

3. Result and Discussion

3.1. Microbial Genomics Tool (MGT) interface

MGT 1.0 interface has two main menus „File‟ and „Help‟ (Fig-

1). „Fasta File Only‟ field is to access the input file through

„Browse‟ tab. „Result Bar‟ is text box which can visualize the

results of calculations. The third portion is „Tool Box‟ divided

into two parts. First part is „Codon Analysis‟ that contains 3 tabs

corresponding to 3 different applications. „Codon Table‟ tab

calculate codon usage frequency, „RSCU‟ tab calculate RSCU

value table and „Other Value‟ tab calculate the GC3, AT3, Nc,

Enc values etc. Second portion is of „Nucleotide Composition‟

containing 2 tabs. „N Table‟ tab calculate the individual

nucleotide composition and percent AT and GC contents of

gene. „Translation‟ tab perform the e-translation of the ORF

sequence and returns the protein sequence.

3.2. Program input

MGT accepts nucleotide sequence as input text file with

ORFs nucleotide sequences in fasta format as shown in Fig-2.

The sequence file is loaded through „Browse‟ tab. Multi-

sequence is also accepted by MGT, which is present in CodonW

but it is command line and total codon frequency is obtained for

either single or number of nucleotide sequences in Jemboss and

BioEdit.

3.3. Program output

MGT software calculates average and percentage codon

frequency; codon usage indices such as AT3, GC3, RSCU, Nc

and ENc value. Nucleotide composition calculation includes

frequency of nucleotides (A, T, G and C), AT-percent and GC-

percent values. Output can be visualized in „Result Bar‟ text box

Fig. 1 MGT 1.0 interface showing tab and text boxes for

different applications. The result output is shown in „Result

Bar‟ text box. The result is also shown in MS-Excel file.

19

Fig. 2 Input file with Fasta formatted nucleotide sequence.

and also can be saved in MS Excel file format (Fig-3). As the

result file is tab delimited it can be further processed for

advanced statistical analysis by suing any window based

Statistical software.

4. Conclusion

Microbial Genomic Tool (MGT 1.0), the first version is

developed for In-silico research bacteria genomic study. This

version is provided with user-friendly graphical interface.

Multiple sequences can be passed to tool and tabulated result for

each sequence can be obtained. It provides application for

calculation of codon frequency, RSCU, AT3, GC3, Nc Enc,

nucleotide composition and percent values. MGT development

was initiated with the future intention to provide multi-

application software for bacterial genome analysis. MGT1.0 is

open access and can be obtained from sourceforge site

(http://sourceforge.net/project/micromictool/), a site for open

source software. This software is at infancy stage and future

plan includes addition of applications for statistical analysis of

codon usage data and microarray data.

Acknowledgments

The author is grateful to BTISNET, Department of

Biotechnology, Government of India New Delhi for their

constant and encouraging support. We also acknowledge the

Sourceforge team (http://sourceforge.net/) for providing server

place to make software open access.

References

1. Shendur J. and Hanlee J. Nature Biotechnol. (2008) 26, 1135.

2. Grantham R. C., Gautier and Gouy M. Nucleic Acids Res.

(1980) 8, 1892.

3. Grantham R. C., Gautier, Gouy M., Mercier R. and Pave A.

Nucleic Acids Res. (1980) 8, r49.

4. Grantham R. C., Gautier, Gouy M., Jacobzone M. and

Mercier R. Nucleic Acids Res. (1981) 9, r43.

5. Sharp P. M. and Li W. H. J. Mol. Evol. (1986) 24, 28.

6. Sharp P. M. and Li W. H. Nucleic Acid Res. (1987) 15, 1281.

7. Sharp P. M., Bailes E., Grocock R. J., Peden J. F. and Sockett

R. E. Nucleic Acids Res. (2005) 33, 1141.

8. McInerney J. O. Bioinformatics (1998) 14, 322.

9. Gouy, M. and Gautier C. Nucleic Acids Res. (1982) 10, 7055.

10. Peden J. http://www.sourceforge.net/ (2005).

11. Umasanker V., Vijay K., Arun K. and Dorairaj S.

Bioinformation (2007) 2, 62.

12. Garcia-Vallve S., Puigbo P., Bravo I. G. BMC Bioinformatics

(2008) 9, 65.

13. Grete A., Hiller K., Monice, Much R., Nortemann B.,

Dietmar C., Hempel and John D. Nucleic Acid Res. (2005) 33,

W536.

14. Carver T. and Bleasby A. Bioinformatics (2003) 19, 1837.

15. Hall T. A. J. Nuclic Acid SYMP (1999) 41, 95.

16. Wright, F. Gene (1990) 87, 23.

17. Rice P., Longden I. and Bleasby A. Trends Genet. (2000) 16,

276.

Fig. 3 Result window showing results in (A) Result Bar (B)

Result in MS-Excel file.

http://sourceforge.net/project/micromictool/

http://sourceforge.net/

20

(a)

(b)

Fig. 1 (a) An example of a set of parallel lines for a

chosen θ = 45◦ in the (x, y) plane and (b) the

localization of corresponding points in the (θ, r) plane

in which the discrete Radon transform is evaluated.

An application of radon and wavelet transforms for image feature extraction

Heena Patel*, Saurabh dave, Himanshu Patel, and Chintan dave

Ganpat University, Kherva, India

*[email protected]

In this paper we proposed wavelet and Radon for the rotation and translation invariant image transforms analysis and their use for

image enhancement and features extraction. Main focus of this paper is to use two-dimensional Radon and wavelet transforms to

form fundamental mathematical tools in these areas. Results are verified in the MATLAB environment both for data and for

analysis of biomedical images.

1.1 Introduction

The Radon transform is named after the Austrian

mathematician Johann Karl August Radon (December 16,

1887 – May 25, 1956). The main application of the Radon

transform is CAT scans, where the inverse Radon

transform is applied. The Radon transform can also be

used for line detection.

Radon transform forming a very important

mathematical tool used in tomography is based upon works

of Johann Radon born in 1887 Litomerice. His doctoral

dissertation has been defended in Vienna in 1910 and his

most appreciated works were devoted to integral geometry.

The Radon transform1 belonging to this category

introduced in 1917 is defined as a collection of 1D

projections around an object at angle intervals θ. The

Radon transform of a two-dimensional (2-D) function f(x,

y) is defined as:

R(θ,r)R(θ,r)[f(x,y)]=

dxdyyxryxf )coscos(),(

Eq. 1

Where, r is the perpendicular distance of a line from the

origin and θ is the angle formed by the distance vector.

The present work allows features extraction by blocks

of Radon transform, wavelet transform and blocks of image

preprocessing. Individual features are obtained by

connection of these blocks using a wavelet decomposition

block into the second level. Two features obtained by this

decomposition are sum of squared image component

coefficients evaluated in the first and the second

decomposition level by high-pass filters both for image

columns and rows. Results of features’ variance with

application of different methods are displayed both

graphically and in the tables.


21

Fig.2 Block diagram of the proposed technique

(a)

(b)

(c)

Fig. 3 Visualization of (a) input MR image rotated by

θ= 90◦, its (b) Radon transform depicted as points for

θ= 0◦ − 180◦ and for the same r for each θ, and (c)

inverse Radon transform.

20 40 60 80 100120

20

40

60

80

100

120

20 40 60 80 100120

20

40

60

80

100

120

(degrees)

x'

0 50 100 150

-50

0

5020

40

60

For the constant value of Θ the set of parallel lines for

different values of r are presented in Figure 1(a). The

parallel lines are used for the integration of the given

image. The plane (x, y) is transformed in this way to the

plane (θ, r). The transformation proceeds by integration of

the given image along parallel lines in the plane (x, y) and

resulting value is then marked in the graph as a point for a

given θ and r as depicted in Figure 1(b). Each point has a

different intensity of color, depending on its value, having

value 0 corresponding to black and 1 corresponding to

white color presented in Figure 3(b). A discrete Radon

transform called Hough transform has been introduced in

1972 by R. Duda and P. Hart 2, 3 as a tool for image features

extraction.

1.2 Simulation of Radon Transform

In the Simulink environment there is no block for the

Radon transform. A general block called ”Matlab function”

can be used instead. This block has a single input and

single output. Parameters of this block include:

• Name of existing function of Matlab library or name of

the created function as M-file

• Output dimensions specified for returned single value

• Choice to Collapse 2-D results to 1-D

The Matlab function or M-file use every ”Matlab

function” block for processing of the input value. Figure 2

presents block diagram of the direct and inverse Radon

transform of MR image and visualization of Radon

transform image. Input image is loaded as a constant and

output variable is frame-based. Input image and images

after transformation are visualized in the matrix viewer

presented in Figure 3 and sent to workspace in direct and

inverse radon transform.

1.3 Radon Transform to Detect Lines

The Radon transform is closely related to a common

computer vision operation known as the Hough transform.

You can use the radon function to implement a form of the

Hough transform used to detect straight lines adjusted to

22

(a)

(b)

Fig. 4 (a) Original image (b) edge image.

50 100 150

50

100

150

50 100 150

50

100

150

Fig. 5 Wavelet decomposition

(a)

(b)

Fig. 6 (a) 2-Level and 4-Level Decomposition. (b) 2-

level Decomposition of reference image

function that limits the duration of the analyzed signal

segment.

1.4 Principles of Image Wavelet decomposition

Wavelet functions used for signal analysis are derived

from the initial function W(t) forming basis for the set of

functions.

Wa,b(t)= ))(1

(1

bta

Wa

Eq. 2

For discrete parameters of dilation a=2m and translation

b=k 2m. Wavelet dilation, which is closely related to

spectrum compression, enables local and global signal

analysis. The principle of signal and image decomposition

for resolution enhancement is presented in Figure 4.The

wavelet transform has gained a great deal of interest due to

its time localization and multiresolution properties. 4-7

Fourier transforms (FT) lack time localization as frequency

components are attributed to the entire time signal and not

to specific parts of it. Windowed Fourier Transforms

(WFT) achieves this localization by using a window WFT

uses fixed size windows that cannot be suite the speed of

the changing phenomena observed in the input signals.

Wavelets solve this problem by using the so called

mother wavelet which can be scaled and translated to

achieve both time localization and multi-resolution. The

decomposition stage results in this way in four images

representing all combinations of low-pass and high-pass

initial image matrix. The reconstruction stage includes row

23

Table 1 STD computed from rotated MR image

features.

STD of MR Image

Features

Feature-1 Feature-2

DWT 0.0013 0.0254

RT-DWT 2.97 X 10-5 0.0023

Fig. 7 Individual Simulink blocks which create one level of wavelet decomposition and reconstruction.

upsampling at first and row convolution in stage R.1. The

corresponding images are then summed. The final step R.2

assumes column upsampling and convolution with

reconstruction filters followed by summation of the results

again. In the case of one-dimensional signal processing,

steps D.2 and R.1 are omitted.

1.5 Simulation of Wavelet Transform in Simulink

Environment

Wavelet transform diagram was created with blocks of

Simulink library. Block”DWT” computes the discrete

wavelet transform using a filter bank with specified

highpass and lowpass filters. The filters can be user-defined

or formed by wavelets of the Wavelet Toolbox. The output

is set to ’Multiple ports’. It enables to see each sub band as

a frame-based vector or matrix. The common block

”Transpose” enables matrix transposition. In our diagram it

enables matrix transposition after column downsampling to

proceed row decomposition. We transpose matrix after the

row decomposition to visualize matrix right. Diagram for

one decomposition and reconstruction levels is presented in

Figure 5.

The whole diagram for image decomposition into the

second level and its reconstruction is presented in Figure 6.

Block diagrams mentioned above have been created to

obtain definition of features of rotated images. We compare

the standard deviation (STD) of the sum of squared

diagonal DWT transform coefficients in the first and the

second decomposition levels using MR images obtained by

rotation from 0 to 180 degrees with step 10◦ using (i)

diagram with the plain DWT,(ii) diagram for the Radon.

2. Results

Thanks to the objective confrontation of STDs, Table 1 is

the bright example that the Radon transform is a powerful

tool expressively contributing to image analysis. The

improvement of the STD between the plain DWT and RT-

DWT by an order has been verified. We achieved also a

small improvement by denoising of the magnetic resonance

image. Therefore image enhancement is very desirable

24

(d) (e) (f)

Fig. 8 Visualization of (a, d) input MR image, (b, e) wavelet decomposition, and (c, f) image wavelet reconstruction

here. We also tested with simulink of MATLAB (Figure 7)

and also using other images (Figure 8) which is in built in

MATLAB. Image preprocessing allows further research

devoted to the optimization of wavelet coefficients

thresholding to denoise the original image.The proposed

method of image features extraction allows the estimation

of the rotation invariant image features and moreover it is

very flexible as it allows the use of different wavelet

functions and different rotation steps in case of the Radon

transform.

3. Conclusions

The above results show the importance of wavelet and

Radon for the rotation and translation invariant image

transforms analysis and their use for image enhancement

and features extraction. The major finding of the present

work is to use two-dimensional Radon and wavelet

transforms to form fundamental mathematical tools. It is

assumed that further studies will be devoted to feature

based image segmentation and further methods of rotation

and translation invariant feature selection using appropriate

image transforms.

4. References

1. Bracewell R. N. Fourier Analysis and Imaging. Kluwer

Academic Press, (2003).

2. Choi D. I. and Park. S. H. IEEE Trans.Neural Networks,

(1994) 5, 561.

3. Duda R. O. and Hart P. E. Comm. ACM, (1972) 15, 11.

4. Gavlasová A. and Procházka A. Simulink modeling of

radon and wavelet transforms for image feature extraction,

Institute of Chemical Technology, Department of

Computing and Control Engineering.

5. Malviya A. and Bhirud S. G. International Conference

on Emerging Trends in Electronic and Photonic Devices &

Systems, ELECTRO-2009.

6. Ramprasad P., Nagaraj H. C. and Parasuram, M. K.

International Journal of Computer Science (2009) 4, 2.

7. Wikipedia. Johann Radon.

http://en.wikipedia.org/wiki/Johann Radon.

25

Use of proteinase inhibitors from okra for inhibiting the Helicoverpa armigera

(Hubner) gut proteinases

Shilpa K.Udamale and M.P.Moharil*

Biotech Centre, Department of Botany, Dr. Panjabrao Deshmukh Agricultural University

Akola, Maharashtra- 444 104, India

*Email: [email protected]

The Abelmoschus esculentus, okra, genotypes and its wild relatives were analyzed for the presence of trypsin,

chymotrypsin and Helicoverpa gut proteinases (HGPs) inhibitors (HGPIs), with the aim to identify potent inhibitors of H.

armigera gut proteinases. Proteinase Inhibitors (PIs) obtained from wild relatives of okra exhibited stronger inhibition of HGPs

than the PIs obtained from genotypes of okra. In in vitro inhibitory assay against HGPs, A. tuberculatus 90396 and 90515, wild

relatives of okra, showed high tryptic inhibitory (71.8% and 69.2%), chymotryptic inhibitory (68.5% and 66.2%) and

Helicoverpa gut proteinase activity (70.2% and 68.2%). Electrophoretic studies showed the variation in trypsin inhibitors (TIs),

chymotrypsin Inhibitors (CIs) and HGPIs isoforms in wild relatives of okra, whereas, its genotypes of okra mostly showed

monomarphic profile. Maximum eight HGPIs isoforms were found in A. tuberculatus (90396 and 90515). In insect bioassay

studies, significant reduction in weight of H. armigera larvae were found, when larvae fed on PIs obtained from A. tuberculatus

(90396 and 90515). Thus result of the present investigation indicate that, further exploration of PIs obtained from A.

tuberculatus (90396 and 90515) will be helpful for developing PIs base insect resistance management strategies.

1. Introduction

Helicoverpa armigera, Hubner (Lepidoptera:

Noctuidae), a highly devastating polyphagous crop pest,

has a broad host spectrum causes a significant yield losses

in many agriculturally important crops like cotton,

chickpea, pigeonpea, corn, maize, tomato, okra,

sorghum, pearl millet, sunflower and groundnut

(Volpicella et al.1). Thirty percent of all pesticides used

worldwide are directed against H. armigera which

resulted into high levels of insecticide resistance in this

pest. Insecticide resistance in H. armigera is widespread

problem in India, Pakistan, China, Australia, Thailand

and Indonesia (Ahmad2). The use of Bacillus

thuringiensis (Bt) either in the form of formulation and

transgenic plant may lead to develop resistance in insect in

a short period of time. Since many insect pests have

developed resistance to Bt like chemical pesticides (Oppert

et al.3). Therefore, it is important to search and develop

alternative methods of controlling these pest and

proteinase inhibitors (PIs), constituent of natural plant

defense system, promises to lead in this aspect in near

future (Mosolov and Valueva 4).

Plant synthesizes various proteinaceous compounds

against an insect attack, among the several plant defense

proteins. Proteinase inhibitors (PIs) are abundantly

present in seeds and storage tissues represents up to 10

per cent of the total protein (Casaretto and Corcuera5).

PIs act as antimetabolic proteins, which interfere with

the digestive process of insects. PIs are particularly

effective against phytophagous insects and micro-

organisms. The defensive capabilities of PIs rely on

inhibition of proteinases present in insect guts or

secreted by micro-organisms, causing a reduction in the

availability of amino acids necessary for their growth

and development. Most PIs interact with their target

proteinases by contact with the active (catalytic) site of

the proteinase resulting in the formation of a stable

proteinase-inhibitor complex that is incapable of

enzymatic activity (Lawrence and Koundal6).

Preliminary studies on presence of proteinase inhibitors

from seeds of okra by Ogata et al,7 showed that PIs from

okra inhibited both bovine trypsin and chymotrypsin,

which are typical digestive enzymes. This study showed


26

that okra seeds contain PIs of trypsin, chymotrypsin

which constitute the defense machinery.

In the present work, different okra genotypes and

it’s wild relatives were screened for the presence of PIs.

Several potent and high potential PIs were identified in

wild relatives of okra. Bioassays were performed to

ascertain the potency of the okra inhibitors in inhibiting

the growth of H. armigera larvae. This outcome can be

exploited for planning the strategies for developing

insect resistance transgenic plants in future.

2. Material and Methods

2.1 Seed material and PI extraction

Seeds of the different genotypes of okra were kindly

provided by Senior Research Scientist, Chilli and

Vegetable Research Unit, Dr. PDKV, Akola and wild

relatives were obtained from National Bureau of Plant

Genetic Resources (NBPGR), Raichur and NBPGR, Akola.

Dry seeds were grounded to a fine powder, defatted and

depigmented with several washes of acetone and hexane.

The solvent was filtered off and seed powder was obtained

upon air drying. The powder was mixed with five volumes

of 0.1M Sodium Phosphate Buffer (SPB) pH, 6.8 and kept

overnight at 4°C for extraction with intermittent shaking.

The suspension was centrifuged at 12,000 rpm for 20 min

at 4oC and the supernatant was stored in aliquots at -20oC.

The protein content of the extract was determined by

Bradford’s method (Bradford 8).

2.2 Extraction of HGPs

Late third or early fourth instar larvae, from homogenous

culture of H. armigera were dissected and mid-gut was

isolated and stored frozen at -780C. Required gut tissue was

homogenized in 1 volume of 0.2M glycine-NaOH buffer

(pH 10.0) and kept for 2 h at 10oC. The suspension was

centrifuged at 12,000 rpm for 20 min and the supernatant

was used as a source of HGPs.

2.3 Electrophoretic visualization of HGPs

HGPs were detected by using by SDS-polyacrylamide

gel. Enzyme extracted from the mid gut of H. armigera

larvae was diluted and electrophoresed on 12% SDS-

polyacrylamide gels along with treatment buffer 60mM

Tris-HCl pH 6.8, 2%SDS, 20% glycerol and 0.1%

bromophenol blue (Gujar et al,9). After electrophoresis,

SDS-polyacrylamide gel was washed in 2.5% Triton X-100

for 10 min to remove SDS, then incubated in 2% casein in

Glycine-NaOH (10 pH), gel was then stained with

coomassie brilliant blue R-250. HGPs bands were revealed

as white bands with dark blue background.

2.4 Proteinase and PI assays

Total proteinase activity was measured by azocaceinolytic

assay (Marcheti et al, 10). For azocaceinolytic assay, midgut

homogenate was mixed with (130 µl) of Tris-HCl buffer,

pH 9. To the above mixture, 100 µl of 2% azocasein was

added and incubated for 1 hr at 370 C. The reaction was

stopped by adding 500 µl of 5% ice cold trichloroacetic

acid (TCA). After centrifugation at 14000 rpm for 15 min

at 40 C, an equal volume of 1M NaOH was added to the

supernatant an absorbance was measured at 420 nm. The

protease activity of sample was calculated using trypsin

standard curve in terms of tryptic unit (TU).

Tryptic and Chymotryptic activities were estimated using

the chromogenic substrates N--Benzyl-L-argine p-

nitroanilide (BApNA, Sigma) and N-Succinyl-Ala-Ala-

Pro-Leu-p-nitroanilide(SAApLNa, Sigma),respectively,

dissolved 100 mg/ml in dimethyl sulfoxide . Midgut

supernatant were diluted 1:100 in buffer containing (200

mM Tris, pH-8.0, 20 mM CaCl2) and 50 µl were added to

microplate well and 50 µl BApNA for tryptic and

SAApLNa for chymotryptic were added after 30 second

incubation at 370C, absorbance was estimated at 405 nm.

For the inhibitory assays, a suitable amount of inhibitor and

HGPs extract was preincubated for 30 min at 370 C prior to

the addition of substrate. H. armigera trypsin

,chymotrypsin and total gut proteinase inhibitory activities

were estimated by using substrate BApNA, SAApLNa and

azocasin. 30 µl proteinase inhibitor and 50 µl gut extract

were preincubated for 30 min. at 370 C. after that 50 µl

substrate were added to each well after 1 min incubation at

370 C, the reaction was terminated by addition of 500 µl of

5% TCA and absorbance was monitored at 405 nm. For

total gut proteinase inhibitory activity, after adding 5%

TCA centrifuged it and 50 µl of 1 N NaOH were added

and absorbance was estimated at 405 nm. One proteinase

unit was defined as the amount of enzyme that increases

27

Fig.1 Electrophoretic visualization of H. armigera gut

proteinase isoforms .

Table 1: H. armigera gut proteinases activity

Sr.

No. Proteinases

Enzyme activity

(U/gut)a

1 Total

Proteinase

activity

2.15 ± 0.001

2 Tryptic

activity

1.97 ± 0.003

3 Chymotryptic

activity

1.84 ± 0.002

aAll figures are mean of triplicate ± SE.

absorbance by 1 OD/ min and one PI unit was defined as

the amount of inhibitor that causes inhibition of 1 unit of

proteinase activity under the given assay conditions.

2.5 Electrophoretic visualization of TIs , CIs and HGPIs

isoforms

TIs,CIs and HGPIs isoforms were detected by using 10%

polyacrylamide gel having 1% gelatin (Felicioli et al,11).

The respective gels were transferred to solutions containing

0.1 % trypsin or 0.1 % Chymotrypsin or HGP extract of

equivalent activity, and incubated for 1 hrs with constant

shaking. The gels were washed with warm water, fixed in

10 % TCA, stain with Coomassie Brilliant Blue R-250 and

destained. Isoforms were revealed as blue bands against

white background.

2.6 Bioassay of PIs against H. armigera larvae

Bioassay was carried out at insect rearing facility of

Department of Entomology, Dr. PDKV, Akola. Eggs,

neonate and early instars larvae of H.armigera were

collected from experimental field of Dr. PDKV, Akola.

This culture was maintained in the laboratory at 27oC at

80% relative humidity on fresh and soft seeds of pigeonpea

until further use. Bioassay was carried out according to

protocol given by Bhavani et al,12. Fresh and soft seeds of

pigeonpea were pressed by thumb and forefinger gently and

put into multiwell rearing tray for releasing larvae. PIs

obtained from A. tuberculatus 90396 and A. tuberculatus

90515 (50 μg of protein concentration) were loaded

between the cavity of two crushed grains with the help of

micropipette. Second larval instar of H. armigera was

selected to start bioassay. Constant exposure of PI was

maintained during whole experiment up to pupation of

larvae.

The observations of larval weights were taken after every

24 hrs after ingestion of food. Control population was also

maintained simultaneously without PIs. The observation on

larval mortality, larval weight, pupal weight, number of

malformed pupae and malformed adult were also recorded.

CRD design was used for statistical analysis.

3. Results and discussion

3.1 Activity and visualization of gut proteinases of H.

armigera

Total gut proteinase (azocaseinase), trypsin like proteinases

(BApNAase) and chymotrypsin like proteinases

(SAApLNase) activities present in gut of H.armigera were

assayed (Table 1). Total Proteinases activity was observed

to be 2.15 U/gut, among it tryptic activity was found to be

slightly higher (1.97U/gut) than chymotryptic activity (1.87

U/gut). Electrophoretic visualization of H. armigera gut

28

1. A .tuberculatus 90396 , 2. A. tuberculatus 90515, 3. A .tuberculatus 90400, 4. A.. tuberculatus 140957, 5. A. tuberculatus 90402, 6. A.

ficulneus 140986, 7. A. tetraphyllus 90398, 8. A .tetraphyllus 90461, 9. A. tetraphyllus 90386, 10. A. ficulneus 41748, 11. A. ficulneus

141042, 12. A. tetraphyllus 92503, 13. A. ficulneus 210361, 14. A. tetraphyllus 90404, 15. A. ficulneus 140947, 16. A. angulossus 203832, 17. A. angulossus 203863, 18. A. angulossus 470751, 19. A. manihot 141019, 20. A. manihot 141045, 21. A. angulossus 203833, 22. A.

angulossus 203834, 23. A. manihot 141012, 24. A. moschatus 140985, 25. A. moschatus 141056, 26. A. moschatus 141065, 27. A. moschatus

470737, 28. A. moschatus 470747, 29. A. manihot 329394, 30. Arka bahar, 31. Parbhani kranti, 32. AKO –107, 33. Arka anamika, 34. AKO-37, 35. Pusa A-4, 36. AKO-111, 37. AKO-102, 38. Adunika, 39. VRO-3. M- Standard Molecular Weight Marker

Fig.2 Helicoverpa gut proteinase inhibitors (HGPIs) isoforms from different genotypes

and wild relatives of okra (Plate 2)

proteinase isoforms were also carried out by 12% SDS-

polyacrylamide (Figure 1). As reveled from the Plate 1,

total H. armigera gut proteinase activity was distributed in

ten isoforms, ranging from molecular weight 118.0 kDa to

16.2 kDa. The apparent density of P1, P2, P3, P7, P8 and P9

found to be high, while that of P4, P5, P6 and P10 were low.

Earlier studies on proteolytic activity of lepidopeteran

insect gut showed that, insect gut comprises of many

isoforms of proteinases having diverse properties and

specificities (Johnston et al,13). Harsulkar et al,14, studied

the isoforms of gut proteinases of H.armigera, their study

revealed that H.armigera gut proteinase activity was

distributed in six isoforms. Similarly, Potdar 15 studied

proteinases of H. armigera gut, he showed ten isoforms of

proteinases in the gut of H. armigera.

The presence of proteinases of different specificities in

the midgut has great significance for the survival and

adaptation of phytophagous insects on several host plants.

The adaptation of pests to host plant PIs probably results

from the selection pressure acting on an entire insect

population when they encounter PIs of their host plants

(Harsulkar et al,16). Thus, ten isoforms of HGP found in

present investigation supported the polyphagous nature of

Helicoverpa armigera.

3.2 Electrophoretic profiles of TIs, CIs and HGPIs

isoforms from different genotypes of okra and its wild

relatives

29

Table 2 Helicoverpa gut proteinase inhibitory potential of PIs isolated from okra genotypes and its wild relatives.

Sr.

No

Genotype HGP tryptic

inhibitory activity (%)

HGP chymotryptic

inhibitory activity

(%)

HGP total proteinase

inhibitory activity

(%)

Wild relatives of okra

1 A .tuberculatus 90396 71.8±0.001 68.4±0.004 70.2±0.002

2 A. tuberculatus 90515 69.2±0.003 66.2±0.004 68.3±0.003

3 A .tuberculatus 90400 62.4±0.005 59.3±0.005 61.4±0.001

4 A.. tuberculatus 140957 67.0±0.006 62.7±0.003 60.6±0.004

5 A. tuberculatus 90402 60.4±0.006 60.2±0.004 62.2±0.002

6 A. fiulneus 140986 54.4±0.005 50.2±0.003 46.1±0.002

7 A. tetraphyllus 90398 49.4±0.002 51.7±0.003 43.1±0.005

8 A .tetraphyllus 90461 48.0±0.001 50.5±0.005 46.9±0.002

9 A. tetraphyllus 90386 51.2±0.005 51.4±0.005 45.0±0.003

10 A. fiulneus 41748 46.1±0.002 44.1±0.003 38.1±0.003

11 A. fiulneus 141042 39.9±0.003 42.5±0.003 42.7±0.002

12 A. tetraphyllus 92503 44.5±0.001 41.8±0.003 43.8±0.002

13 A. fiulneus 210361 47.0±0.006 42.6±0.002 40.5±0.004

14 A. tetraphyllus 90404 44.5±0.004 43.3±0.002 46.9±0.003

15 A. fiulneus 140947 43.4±0.003 41.8±0.002 43.5±0.002

16 A. angulossus 203832 65.3±0.004 55.5±0.006 59.1±0.003

17 A. angulossus 203863 53.0±0.002 50.9±0.003 46.1±0.001

18 A. angulossus 470751 50.2±0.002 49.4±0.003 47.3±0.003

19 A. manihot 141019 47.3±0.002 48.7±0.003 43.8±0.002

20 A. manihot 141045 42.4±0.002 47.9±0.001 42.9±0.003

21 A. angulossus 203833 51.5±0.003 45.6±0.003 51.5±0.001

22 A. angulossus 203834 48.0±0.003 42.6±0.004 46.5±0.003

23 A. manihot 141012 57.9±0.002 45.2±0.003 45.4±0.003

24 A. moschatus 140985 49.4±0.001 49.8±0.001 44.3±0.002

25 A. moschatus 141056 45.9±0.003 44.1±0.002 43.5±0.002

26 A. moschatus 141065 50.1±0.002 51.7±0.004 43.3±0.003

27 A. moschatus 470737 54.0±0.003 54.7±0.002 48.4±0.003

28 A. moschatus 470747 52.4±0.004 52.0±0.002 50.7±0.003

29 A. manihot 329394 42.7±0.004 40.3±0.002 41.9±0.003

Genotypes of okra

30 Arka bahar 53.7±0.004 46.0±0.002 46.6±0.002

31 Parbhani kranti 63.8±0.005 62.1±0.003 58.4±0.004

32 AKO -107 53.9±0.004 50.1±0.003 56.8±0.003

33 Arka anamika 55.1±0.001 51.7±0.003 52.6±0.002

34 AKO-37 50.5±0.004 48.6±0.002 51.5±0.001

35 Pusa A-4 51.9±0.002 45.6±0.004 54.5±0.002

36 AKO-111 57.9±0.003 50.9±0.005 62.2±0.003

37 AKO-102 65.6±0.002 56.7±0.003 60.3±0.001

38 Adunika 60.0±0.003 55.7±0.001 63.3±0.003

39 VRO-3 63.9±0.002 61.9±0.002 62.9±0.002

PIs were isolated from ten genotypes of okra and 29

wild relatives by the method given by Felicioli et al,11. Gel

co-polymerized with 1 percent gelatin was used for the

detection of TIs, CIs and HGPIs bands.

All wild relatives of okra showed variability in terms

of the number and intensities of TIs bands. A. tuberculatus

90396 and 90515 exhibited highest (six) TIs isoforms,

A.angulossus (203832) showed four TIs isoforms, whereas,

A. ficulneus (41748, 141042, 210361 and 140947) and A.

tetraphyllus (90404) exhibited the minimum (one) TIs

isoforms. All okra genotypes showed monomorphoic PIs

profile i.e. four TIs isoforms were detected in all genotypes

of okra with dark intensity, except Arka bahar which

showed less intense TIs isoforms.

Similarly, gelatin co-polymerized polyacrylamide gel

electrophoresis showed wide range of CIs (molecular

weight 25.1 kDa to 6.3 kDa) with variable intensities. A.

tuberculatus (90396, 90515, 90400, 140957 and 90402)

reported maximum (five) CIs isoforms. While, A.ficulneus

(140986, 141042, 210361, 140947), A. tetraphyllus

(92503), A. moschatus (141065) and A. manihot (329394)

exhibited only one CIs isoform. Different okra genotypes

exhibited maximum number of (four) of CIs isoforms,

except Arka bahar which showed only one CIs isoform.

Results clearly indicate the potentiality of A .tuberculatus,

to search new and potent proteinase inhibitors. This is also

confirmed by our studies on TIs and HGPIs isoform.

To determine specificities of PIs towards HGP

isoforms, PIs extract were resolved on gelatin-

polyacrylamide gel. Further, it incubated with HGP extract

obtained from mid gut of Helicoverpa larvae (equal TI

units), HGPI bands were visualized as described in

30

Fig. 3 Effect of okra PIs on the growth and

development of H. armigera larvae (Plate 3).

materials and methods. Plate 2 (a, b, c) represents the

electrophoretic profile of HGPIs in seed extracts of okra

and it’s wild relatives (Figure 2). The tuberculatus group

showed presence of high activity HGPIs bands as compared

to okra and other wild relatives.

Among the wild relatives of okra A.tuberculatus

(90396 and 90515) showed eight HGPIs isoforms, whereas,

A.tuberculatus (90402) exhibited seven HGPIs band

followed by A.tuberculatus (90400 and 140957) showed six

HGPIs isoform and five HGPIs isoform was found to be in

A.angulossus (203832), whereas, A. ficulneus (41748 and

210361) and A.manihot (329394) showed that only one

HGPIs isoform Plate 2 (a, b). Different genotypes of okra

showed variable number of HGPI isoforms with different

intensities. AKO-111, AKO-102, Addunika, VRO-3

reported maximum (five) HGPIs isoforms with high

intensity. Also Parbhani Kranti AKO-107, Arka anamika,

AKO-37 possessed four HGPIs isoforms, whereas, Arka

bahar consists only one HGPIs isoform Plate 2(c). These

results clearly showed that PIs from wild relatives of okra

A. tuberculatus (90396 and 90515) exhibited strong

inhibitory potential against HGP.

A similar observation were also reported in pigeonpea

by Choughule et al,17 showed that pigeonpea cultivars

exhibited monomorphism in TIs and CIs isoforms,

whereas, diverse proteinase inhibitory profiles in pigeonpea

wild relatives. Patankar et al,18 also observed significant

variation in the TIs isoforms from wild Cicer species.

However, they have observed great conservation of TIs

isoforms in the mature seeds of the chickpea cultivars. A

similar observation exists in pigeonpea where TIs and

chymotrypsin inhibitors are conserved in matured seeds of

the cultivated pigeonpea, whereas, a high level of diversity

exsist in uncultivated species of Cajanus (Kollipara et al,19,

Pichare and Kachole 20). The variation observed in wild

Cicer species is considered significant, as the TIs are

known to serve as a defense proteins against herbivores

(Ryan 1990). Cicer reticulatum and Cicer arietinum

showed similar TIs band patterns, which suggests that

Cicer reticulatum is genetically closer to Cicer arietinum.

Thus, this studies can also be used for karyotyping the

genotypes. The variation observed in the wild relatives of

okra is considered significant, as TIs are known to serve as

defense proteins against herbivores (Ryan21).

Wild relatives of okra A.tuberculatus (90396 and

90515) showed eight HGPIs isoforms with high intensity,

whereas, AKO-111, AKO-102, Addunika, VRO-3 reported

maximum (five) HGPIs isoforms while, Arka bahar

consists only one HGPIs isoform (Plate 2). These results

clearly showed that PIs from wild relatives of okra A.

tuberculatus (90396 and 90515) exhibited strong inhibitory

potential against HGP. Earlier studies on electrophoretic

profiles of HGPIs of pigeonpea and it’s wild relatives.

Rhynchosia group showed presence of high activity HGPIs

bands (5) as compared to pigeonpea and other wild

Cajanus species (Chougule et al, 17).

31

Table 3 Day-wise reduction in weight of H.armigera larvae feed with okra PIs of A. tuberculatus (90396) and A.

tuberculatus (90515).

Age

(DAI)a

Weight of larvae (mg) when fed with Meanb

A. tuberculatus (90396) A. tuberculatus

(90515)

Control (without PI)

1 23.8 25.3 27.3 25.5

2 28.2 31.0 31.7 30.3

3 38.3 40.0 43.3 40.5

4 47.1 50.1 55.0 50.7

5 54.7 56.7 72.3 61.2

6 71.0 89.7 98.7 86.4

7 91.0 120.3 138.0 116.4

8 116.0 130.0 162.7 136.2

9 121.3 141.3 186.7 149.8

10 127.6 156.0 221.3 168.3

11 144.7 180.0 259.7 194.8

12 156.3 211.7 297.7 221.9

13 176.3 224.7 330.3 243.8

Pupa 178.0 225.7 329.3 244.3

Mean 98.2 120.2 161.0

Age Variety Interaction

F-test significant significant significant

SE 1.78 3.72 6.45

CD at5% 4.96 10.32 17.88 aDAI- Days after ingestion of proteinase inhibitor, bMean of all the survival larvae

3.3 Inhibitory potential of PIs from different genotypes

of okra and its wild relatives against Helicoverpa gut

proteinases.

Several genotypes of okra and its wild relatives were

analyzed for their inhibitory potential against HGP activity.

Inhibition capacity of okra PIs towards HGP was evaluated

by in-vitro micro plate adopted enzyme assays. Low

concentration of proteinase inhibitors (30µg) was used to

obtain inhibition of tryptic, chymotryptic and total gut

proteinase activity. Control was maintained without any PIs

and its activity was considered as 100%.

Helicoverpa gut consist of both tryptic and

chymotryptic activity. Tryptic activity was slightly higher

than chymotryptic activity. Therefore, inhibitory potential

of PIs towards trypsin as well as chymotryptic activity was

considered to be useful potent PIs.

Table 2 summarizes the inhibitory potential of PIs

obtained from various okra genotypes and its wild relatives

against Helicoverpa tryptic activity, Helicoverpa

chymotryptic activity and total proteinase activity. A close

examination of data revealed that different okra genotypes

possessed tryptic inhibitory activity ranges from 50.5%

(AKO-37) to 63.9% (VRO-3). Amongst different wild

relatives of okra, minimum inhibitory potential (39.9%) of

tryptic activity was observed in PIs of A. tuberculatus

(141042) and maximum tryptic inhibitory potential

(71.80%) was observed in PIs of A. tuberculatus (90396)

followed by A. tuberculatus (90515) i.e. 69.2%. Similar

trend of inhibition was observed in case of Helicoverpa gut

chymotryptic activity and Helicoverpa gut total proteinase

activity.

Earlier studies on wild relatives of pigeonpea showed

more than 70 percent inhibition, whereas, cultivars showed

around 50 percent inhibition of HGP (Chougule et al, 17).

Moreover, the proteases from H. armigera were inhibited

upto 85 percent by AKTI at a concentration 45µg ml-1

(Zhou et al,22). Previous study showed that the C. annum

PIs inhibited more than 60 percent total proteolytic activity

(Tamhane et al,23 ). 72 percent total gut activity was

inhibited by chickpea PI (Harsulkar et al,16).

H. armigera is a polyphagous pest and possesses

different types of proteinases in its gut (Harsulkar et al.,16),

the effectiveness of okra wild PIs offers good gene pool for

the development of H. armigera (Bhendi fruit borer)

32

Table 4: Effect of okra PIs on the growth and

development of H.armigera .

Growth and

developmental

parameters

Proteinase inhibitors

A.

tuberculatus

(90396)

A. tuberculatus

(90515)

Larval mortality

% 40 30

Larval wt.

reduction %

(Control=

330.3mg larval

Wt.) 53.4 68.0

Reduction in

pupal wt. %

(Control

329.3mg) 54.1 68.5

Malformed pupae

% 60 50

Pupal Mortality

% 10 10

Malformed adult

% 30 20

resistant okra varieties, similarly it offers good source to

isolate PIs genes for developing insect resistance transgenic

plants against H.armigera.

3.4. Effect of okra PIs obtained from A. tuberculatus

(90396 and 90515) on fitness parameters of H. armigera

Bioassay results of PIs showed significant

reduction in weight of H. armigera larvae when fed on PIs

obtained from A. tuberculatus 90396 and 90515 (Table 3

and 4, Figure 3). Also, effects on different parameters of H.

armigera were recorded like viz. larval mortality, pupation

rate, reduction in pupal weight, malformed pupae, pupal

mortality and malformed adult.

3.4.1. Day-wise reduction in weight of H.armigera

larvae feed with okra PIs of A. tuberculatus (90396 and

90515)

The data (Table 3) on insect weight was affected by

feeding with PIs obtained from A. tuberculatus (90396) and

A. tuberculatus (90515), wild relatives of okra, indicated

significant difference among the treatments. The wild

relative A. tuberculatus (90396) was found most effective.

The mean of insect weight was 98.2mg at 13 DAI,

indicating significant reduction than the larvae fed on PIs

obtained from A. tuberculatus (90515) and artificial diet

without PIs.

The second factor i.e. age also showed significant

difference indicating that the weight of the insect was

directly proportional to the age of the insect. The

interaction studies reveled that there was significant

reduction in insect body weight, when larvae fed with A.

tuberculatus (90396) even at 12, 13 day old larva as well as

pupal stage.

3.4.2. Effect of okra PI on the growth and development

of H. armigera

53.4% and 68.0% weight reduction was observed in

larvae fed on A. tuberculatus (90396) and A. tuberculatus

(90515) PIs containing diet (Plate 3b). Larval mortality was

observed at 11 days after ingestion which on up to 40% in

A. tuberculatus (90396) and 30% in A. tuberculatus

(90515), whereas, in control no larval mortality was

recorded.

The larvae fed on proteinase inhibitor obtained from

A. tubercualtus (90396) and A. tubercualtus (90515) forms

blackish malformed pupae, which the normal pupal were

dark brown (Plate 3d). Pupation rate was lower in

population fed on PIs of A. tuberculatus 90396 (60 %)

followed by population fed on PIs of A. tuberculatus 90515

(70%) than control. In addition to this, significant decrease

in pupal weight 54.1% and 68.5% was also observed in

population fed on A. tuberculatus (90396 and 90515) as

compared to control (Plate 3). 60% and 50% malformed

pupae were found in population fed on PIs of A.

tuberculatus (90396) and A. tuberculatus (90515),

respectively compared to control. Whereas, pupal mortality

was only 10 per cent (Plate 3d and 3e). Okra PIs also

exhibited adverse effect on adult emergence. After

emergence adults were found to be malformed (Plate 3f).

53.4% and 68.0% weight reduction was observed in larvae

fed on A. tuberculatus (90396) and A. tuberculatus (90515)

PIs containing diet (Plate 3b). also larval mortality was

observed up to 40% on A. tuberculatus (90396) and 30%

on A. tuberculatus (90515), whereas, in control no larval

mortality was recorded. Pupation rate significantly

decreases and 60% and 50% malformed pupae were found

in population fed on PIs of A. tuberculatus (90396) and A.

33

tuberculatus (90515). Okra PIs also exhibited adverse

effect on adult emergence.

The disruption of amino acid by the inhibition of

protein digestion through PIs is the basis of PIs based

defense in plants, however, in nature it might be coupled

with other factors. To evaluate in vivo effects of okra PIs on

H. armigera feeding assays were conducted with added

inhibitor protein in the diet. Larval growth and

development were dramatically reduced when larvae fed on

okra PIs diet. Reduced feeding of larvae was observed in

case of PIs incorporated diet than control the adverse

effects were significant at a higher concentration of PIs

doses.

Significant difference in larval mortality was also

evident. This can be explained as larval stage is very

crucial for accumulating nutrients and energy, which is

used for pupal and adult development. Starvation and

added stress on gut proteinases expression system to

synthesize new and higher amounts of proteinases could be

the possible reason for arrested growth and mortality of H.

armigera larvae. Other researchers also observed growth

and retardation and mortality with PI doses to H. armigera

and other insects (Kranthi et al, 24, Tamhane et al, 23,

Shukla et al, 25 and Bhavani et al, 12). Another interesting

observation was that the inhibitor caused a high ratio of

deformities in pupae and adult (Plate 3 (d and f)), such

types of result were also shown by Franco et al,26. They

reported 50 % deformities in pupae and 81% in adult due to

SKTI inhibitor. The requirement of lower PIs (50µl) in diet

for maximum effect on H.armigera growth retardation

indicates its high specificity towards HGPs.

4. Conclusions

After extensive In vivo and in vitro screening of

PIs from several cultivated and wild relatives of okra in

present study, PIs from A. tuberculatus (90396 and 90515)

were found to possess potential, so as to explore it in future

for developing PIs based management strategies of

lepidopteran pest general and H. armigera in particular.

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