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
http://www.irsaps.org
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
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and attractive option for the younger generation. In
our limited scope, the singular aim of IRSAPS will
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IRSAPS Bulletin 2011, Vol. 1, Issue 3 © IRSAPS
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
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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.
IRSAPS Bulletin 2011, Vol. 1, Issue 3 © IRSAPS
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
Department of Cancer Biology
and Biochemistry
Hubbard Hospital Bldg-
CAHDR
Meharry Medical College
School of Medicine
1005 Dr. DB Todd Jr Blvd,
Nashville, TN 37208, USA
E–mail: cdash[at]mmc.edu
4. Prof. Deben C Baruah
Professor
Department of Energy
Tezpur University
Tezpur 784028
E–mail:
baruahd[at]tezu.ernet.in
5. Dr. Jadab Sharma
Cookson India Research
Centre, Cookson Electronics
Bangalore, India
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6. Dr. Lakshmi Swarna Mukhi
Pidugu
5277 Rivendell lane Apt#6
Columbia MD-21044
7. Dr. Manish C. Pathak
Emory University
School of Medicine
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
E-mail:suresh[at]arci.res.in
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)
E–mail: ramesh[at]tezu.ernet.in
11. Dr. Sanjeev Malik
Department of Mathematics,
Indian Institute of Technology,
Roorkee, India
E-mail: malikdma[at]gmail.com
12. Dr. Sonika Saddar
Pulmonary and Vascular
Biology
Department of Pediatrics
UT Southwestern Medical
Center
5323 Harry hines Blvd
Dallas, TX 75235 USA
E-mail:
sonikasaddar[at]gmail.com
13. Dr. T. Govindaraju
Assistant Professor
Bioorganic Chemistry Lab
New Chemistry Unit
Jawaharlal Nehru Centre for
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
E-mail:
ujjalgautam[at]gmail.com
15. Dr. Vijayakumar H. Doddamani
Associate Professor
Dept. of Physics
Bangalore University
Bangalore-560056, India,
Phone (Off): 91-80-
22961484/1471,
E-mail: drvkdmani[at]gmail.com
International Advisory Board
16. Dr. Alberto Vomiero
CNR-IDASC SENSOR Lab
Via Branze, 45 , 25123 BRESCIA ,
Italy
Publication and Distribution*
1. Dr. Amit Sharma
Unite de Catalyse et de Chimie du
Solide (UCCS)
UMR CNRS 8181
Ecole Centrale de Lille, Cité
Scientifique, BP 48
Villeneuve d'Ascq, Lille, Nord,
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,
Tamilnadu 613 402 INDIA
E-mail: naren_pr[at]yahoo.com
3. Mr. Qureshi Ziyauddin
Institute of Chemical Technology
Nathalal Parekh Marg,
Matunga, Mumbai 400019,
Maharashtra, India
E-mail: qureshi.ziya[at]gmail.com
4. Dr. Rupam Jyoti Sarma
Department of Chemistry
Gauhati University
Gopinath Bordoloi Nagar
Guwahati, Assam, India
E-mail: [email protected]
5. Dr. Santosh B. Chavan
Jay Biotech, Pune, India
E-mail:
sbchavan23[at]gmail.com
* List is incomplete
IRSAPS Bulletin 2011, Vol. 1, Issue 3 © IRSAPS
A. iv
IRSAPS Bulletin 2011, Vol. 1, Issue 3 © IRSAPS
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.
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. Juli´an Fern´andez 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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International, South Hanley Rd., St. Louis, Missouri,
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Chem. Soc. (1984) 106, 765.
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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
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)
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.
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
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. Gavlasov´a A. and Proch´azka 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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