IRSAPS Bulletin Vol 1, Issue 3

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Vol. 1, Issue 3

IRSAPS Bulletin

Sep-Dec 2011 http://www.irsaps.org

(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 (37th) position in the anticodon loop of tRNA

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sciences, the association will endeavor to encourage budding researchers by encouraging them at school and higher secondary levels through the network of volunteers. Though the association is a nonprofit organization, it will encourage entrepreneurship among the members through scientific innovations.

Scope and Aim of Indian Research Scholars’ Association for Promoting Science (IRSAPS) Indian Research Scholars’ Association for Promoting Science (IRSAPS) is created to spread brotherhood through scientific research to every part of our country! The aim is to develop a spirit of healthy scientific discussions that could aid and advance ideas through scientific knowledge exchange as well as acting as a communion of mundane necessities. The association will strive to facilitate the research in basic sciences and augment infrastructure facilities with renewed effort to make career in basic sciences a viable and attractive option for the younger generation. In our limited scope, the singular aim of IRSAPS will be dissemination and decentralization of knowledge to all remote corners of the country that could help materialize dreams of the deserving ones who are focusing on science based careers. Since India’s future equally depends on the knowledge pool in basic

IRSAPS also endeavors to provide a platform of knowledge exchange dedicated, but not limited to Indian science research scholars. We are sure that this will ignite new thinking and aspirations from all sections of people from the various parts of the globe. The association will take appropriate steps to highlight and encourage communications from talented research scholars across the world to create a universal knowledge society. The scope of the forum will change with time depending on the requirements of the forum members. No discrimination will be tolerated in terms of regional, ethnic, or any other means. The association will have a governing body constituted by at least one member from each state of India (depending on the availability of volunteers), however, there is no limitation on foreign memberships. Any organizational dispute arising in due course will be sorted out through a democratic voting process among the governing body members.

Date of establishment: 14 August 2010 Total number of members (December 2011): 360

IRSAPS Bulletin 2011, Vol. 1, Issue 3

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IRSAPS Bulletin Volume 1, Issue 3 Issue Editor: Prof. A. K. Gade Release date: 30th January 2012 This journal is published by Indian Research Scholars’ Association for Promoting Science. To join IRSAPS, please visit: http://www.irsaps.org 1st Issue: January-April 2nd Issue: May-August 3rd Issue: September-December Statement on current journal’s policy: IRSAPS Bulletin does not have a peer review policy for articles. Authors are solely responsible for the authenticity of content and correctness of all articles. It is a free open source online journal. Nevertheless, readers and authors are referred to the announcement for a change in Journal’s policy. Authors are requested to follow ethical guidelines, failing to which may lead to rejection of manuscripts and withdrawal of published articles.

Cover page details: Three-dimensional structure of tRNA-enzyme complex, anticodon stem loop (ASL) of tRNA containing hypermodified nucleoside, hn6Ade at 3'-adjacent (37th) 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: kds_biochem@unishivaji.ac.in.

©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

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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

Pidugu 5277 Rivendell lane Apt#6 Columbia MD-21044 7. Dr. Manish C. Pathak Emory University School of Medicine USA

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

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

3. Prof. Chandravanu Dash Center for AIDS Health Disparities Research Department of Cancer Biology and Biochemistry Hubbard Hospital BldgCAHDR 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 E-mail: jadab.s[at]gmail.com 6. Dr. Lakshmi Swarna Mukhi

IRSAPS Bulletin 2011, Vol. 1, Issue 3

9. Dr. Prakash Bhosale, Senior Scientist, Bioprocess Research and Development DowAgrosciences Indianapolis, USA.

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

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

15. Dr. Vijayakumar H. Doddamani Associate Professor Dept. of Physics Bangalore University Bangalore-560056, India, Phone (Off): 91-8022961484/1471, E-mail: drvkdmani[at]gmail.com

11. Dr. Sanjeev Malik Department of Mathematics, Indian Institute of Technology, Roorkee, India E-mail: malikdma[at]gmail.com

International Advisory Board

12. Dr. Sonika Saddar Pulmonary and Vascular Biology Department of Pediatrics UT Southwestern Medical

16. Dr. Alberto Vomiero CNR-IDASC SENSOR Lab Via Branze, 45 , 25123 BRESCIA , Italy

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Publication and Distribution*

1. Dr. Amit Sharma Unite de Catalyse et de Chimie du Solide (UCCS) UMR CNRS 8181 Ecole Centrale de Lille, CitĂŠ Scientifique, BP 48 Villeneuve d'Ascq, Lille, Nord, FRANCE 59651 E-mail: amitfrance[at]gmail.com 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: rup.sarma@gmail.com 5. Dr. Santosh B. Chavan Jay Biotech, Pune, India E-mail: sbchavan23[at]gmail.com

* List is incomplete

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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.

IRSAPS Bulletin 2011, Vol. 1, Issue 3

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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

7.

25

Science cartoons

IRSAPS Bulletin 2011, Vol. 1, Issue 3

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Few lines from the editorial desk……………! All over the world is celebrating the year

acid

base

N6-(3-

2011 as the international year of chemistry, with the

hydroxynorvalylcarbamoyl) adenine.

The

third

motto for the occasion “Chemistry – our life, our

article discusses a new microbial genomics tool for

future”, which signifies the importance of chemistry

the codon usage analysis while fourth article is on

for our existence and in our life. Chemistry is

the application of wavelet and Radon for the rotation

considered as the Central Science among the three

and translation invariant image transform analysis

branches of science. IRSAPS has been doing its bit in

and their use for image enhancement and feature

the promotion of chemistry by publishing articles,

extraction. The fifth article provides a detail study of

conducting webinars and more emphasis is been

the protease inhibitors based insect resistance

given to popularizing science as a whole. In this issue

management strategies.

IRSAPS has aptly decided to amalgamate the From the next issue, IRSAPS Bulletin will go chemistry

and

life

together

to

focus

on

“Biochemistry” for the current issue along with the

international and it will be released as „Journal of Interdisciplinary Science‟. Accordingly, we plan to

articles from other branches of science as well. This publish articles covering broader subject areas. We journal is providing a platform to promising young hope the current issue will give a glimpse of what researchers from all fields of science, engineering journal

is

aiming

to

bring

the

flavor

of

and medicine for publishing their research work and interdisciplinary science into a single platform. We ideas, for the cause of promoting science. hope everyone will enjoy reading it and appreciate it In this issue there are five scientific articles

as a source of promoting science. We look forward

covering magnetic nanocomposite films, Radon and

for the active participation from the scientific

Wavelength

community.

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

-Aniket K. Gade

applications, while the second article is about molecular modeling studies of hypermodified nucleic

1


Magnetic nanocomposite films Hardeep Kumar Institute of Physics, University of São Paulo, São Paulo 05508-090, Brazil Email: hsehgal_007@yahoo.com

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.

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

1. Introduction Nearly all natural and synthetic materials are

700 AD) are of particular interest. The multifunctional

heterogeneous, i.e. they are microscopically built by

properties of the NCs are often complex relations defined

different components or phases. In nanocomposite (NC)

by varying sizes, shapes and relative fractions of the

materials, one of the solid constituents traditionally exhibits

constituent components. The possibility of realizing unique

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

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

concept

technological applications ranging from aerospace [1], gas

of

enhancing

properties

and

improving

characteristics of materials through creation of multi-phase

(a)

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

(b)

A B A B A

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

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

civilization started and humanity began producing efficient

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

materials for functional purposes. Typical examples of

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

naturally evolved nanocomposites (NCs) can be found in

size technological devices/components the research in thin

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

films is under more attention. In NC thin films the

human made NCs, the tempera colours used in the Ajanta

constituent components can be arranged principally in two

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

ways:

2


Fe Cr Fe

H= 0

Fe Cr Fe

H = - 40 KG

Fig. 2 Schematic illustration of GMR effect in Fe /Cr multilayer [9] (i) Multilayer NCs:

Layer by layer arrangement of

Magnetoresistance or GMR. The physical origin of GMR

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

can be attributed to the influence of the electron spin the

nanometer range (Fig.1 (a)).

electronic transport in ferromagnetic conductors i.e. spin

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

dependent scattering at the interfaces and on bulk of the

nanometer range i.e. zero dimensional or one dimensional

multilayer structures.

or two dimensional (see Figure 1(b).

The tunnel magnetoresistance (TMR), which is

2. Magnetic Nanocomposite films

the newest type of the magnetoresistance effect, has

2.1. Magnetic Multilayers

attracted more interest than AMR and GMR because of its

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

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=

2P 1 P 2 1− P1 P 2

, where

Pi =

Di  Di 

Di + Di 

3


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]. The

D1  (  )

and D2

 (  ) 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

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

`

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] ferromagnetic electrodes in anti-parallel and parallel

to high tunneling conductance/current.

configurations. In the antiparallel configuration, majority

2.2. Magnetic granular films

(minority) electrons from the first electrode would seek minority (majority) empty states in the second electrode

Magnetic granular films are the nanocomposte films with a typical combination of magnetic nanoparticles

which would lead to low tunneling conductance/current. On

4


(MNPs) embedded at random in an immiscible non-

applications of FM-I granular films include high coercivity

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

that is required for information storage, high permeability,

novel properties associated with MNPs. First, MNPs can

high resistivity for shielding and bit writing at high

respond to an external magnetic field without physical

frequencies, MR sensors and read heads, high sensitivity

contact, making them attractive for remote applications.

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

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

reported to be potential candidates for field emission and

the nanoscale, different magnetic properties, compared with

solar energy applications also.

their bulk counterparts, can be obtained. When particle size

It is very important to prepare FM-I granular

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

films with controlled MNP size, uniform composition and

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

uniform thickness for most of the applications. A large

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

number of physical techniques like sputtering (radio

the coercivity of MNPs is largest and will decrease as

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

particle

the

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

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

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

various materials, below which the coercivity is zero for all

prepare FM-I granular films of different materials.

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

Amongst these techniques, sputtering is the best in terms of

Superparamagnetism is a unique property of single domain

film thickness and composition uniformity, and large area

MNPs, and is determined by size, temperature and

deposition. In the following sections the important areas of

measurement time. Finally, and more intriguingly, the

application of FM-I granular films will be discussed.

properties of MNPs are tunable as a function of particle

3. Applications of FM-I granular films

size, particle size distribution and interparticle interactions.

3.1 Tunneling Magnetoresiatance

size

decreases,

until

it

reaches

Depending upon nature of non-magnetic matrix, two types of granular films can be considered:-

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

(1) Ferromagnetic metal-Metal (FM-M) granular films, where immiscible matrix is a noble metal eg. Au, Cu and Cr etc.

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

(2) Ferromagnetic metal-Insulator (FM-I) granular films, where immiscible matrix is an insulator (I) eg. SiO2, Al2O3, MgO, ZrO2 etc. 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,

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

The work on FM-I granular films was pioneered

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

TMR was studied first in Ni-SiO2 granular films by

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

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 5


The Hall effect in semiconductors is the basis of many

With the development of telecommunication technology

devices in measuring magnetic fields. In nonmagnetic

and highly integrated electronic devices, electromagnetic

metals, the ordinary Hall coefficient is low because of the

shielding has been intensively studied in the past years to

high carrier density. The stronger effect that Hall

satisfy

discovered in ferromagnetic conductors came to be known

electromagnetic

as the extraordinary Hall effect (EHE) or anomalous Hall

components from possible electromagnetic interference. It

effect (AHE). Hall resistivity for magnetic materials is

is well known that a highly permeable material can increase

expressed as:

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

ρ xy = R0 B + Rs μ0 M

the

requirements

of

radiation

reducing

and

undesirable

protecting

delicate

relative permeability of the material. Thus a substantial

(1)

increase in inductance and hence in the quality factor can where B is the magnetic induction, M is the magnetization,

be obtained if no extra losses are produced by the magnetic

(b)

(a)

Fig. 5 (a) HRTEM micrograph, and (b) The dependence of complex permeability (FeCo)57:(SiO2)43 granular film.

     i 

on frequency f for the

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

material. The two main loss mechanisms in an inductive

ordinary Hall coefficient and Rs is the extraordinary Hall

material at high frequencies are the ferromagnetic

coefficient. The first term represents the ordinary Hall

resonance FMR frequency and eddy current losses.

effect

while

the

second

term,

coming

from

the

(2)

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

Whereas,

μ'r =

Ms +1 μ0 H k

(3)

interaction present in a ferromagnet. Rs obey a power law

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

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

the anisotropy field and the gyromagnetic factor. It is thus

where α is a constant. Smit’s classical asymmetric

in general necessary to maximize MS and pick a reasonable

scattering gives the exponent n=1 while the quantum

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

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

achieve a high FMR frequency. Eddy current losses are

reported that both ordinary and extraordinary Hall

minimized by having a high resistive material and a small

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

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

'

high relative permeability μr is desirable, since μr is directly related with the level of the output signals of the RF magnetic devices. The possible material candidates for high frequency applications are:-

6


'

(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

μ'r

small =>

is large. But small resistivity (ρ) value

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

implies Large eddy current losses. Therefore FM metals are

electronic devices employed

not suitable for practical use in high frequency applications.

applications for electromagnetic shielding.

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

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.

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 M s 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

in

near 1GHz range

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

7


Molecular modeling study of hypermodified nucleic acid base 3hydroxynorvalylcarbamoyl 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: kds_biochem@unishivaji.ac.in Conformational

preferences

of

hypermodified

nucleic

acid

N6-(3-

base

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 hn 6Ade 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.

position in anticodon loop of tRNA of hyperthermophilic

1. Introduction The hypermodified nucleosides naturally occur at th

th

34 and 37 positions in the anticodon loop of tRNA from all domains of life.

1-3

bacteria

and

archaea.4

The

anticodon

3'-adjacent

modifications help define reading frame for the codon-

These modified components are

anticodon interaction by preventing extended Watson-Crick

derivatives of the four common ribonucleosides. Most of

base pairing whereas, the modifications present at 34th

the modifications involve simple alkylation, hydrogenation,

position may restrict or enlarge the scope of wobble base

thiolation or isomerization of these four common

pairing.5-7

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

Transfer RNA which recognizes codons starting

ribose. However, some modifications involve complex

by U contain hydrophilic modified nucleosides such as

chemical modifications which are characterized by the

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

presence of diverse functional groups in base substituents,

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

such tRNA components are referred as hypermodified

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

N6-(3-

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

hydroxynorvalylcarbmoyl) adenine, hn Ade and its 2-

adenine ring) in the crystal structure9 as well as predicted

N6-(3-hydroxynorvalylcarbmoyl)

theoretically by using quantum chemical PCILO method.10-

nucleosides.

Hypermodified

nucleosides 6

methylthio

derivative

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

11

In these modifications the N(6) substituent spreads away

8


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

away from the five membered imidazole moiety of adenine

ring and becomes inaccessible for participation in the usual

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

Watson-Crick base pairing with codons and thus help

base pairing.

define the proper reading frame for the codon-anticodon

1.

interaction during protein biosynthesis process.

Nomenclature, Conventions and procedure Figure 1 depicts

the

atom

numbering

and

The previous studies on the conformational

identification of the various torsion angles describing

preferences of the hypermodified bases i 6Ade and its

rotations around the respective acyclic chemical bonds. In

2 6

mS i Ade along with its hydroxylated derivatives like cis6

6

2

6

2

6

io Ade, trans-io Ade, cis-mS -io Ade and trans-mS io Ade 2

along with various forms of the lysidine (k C) have been studied computationally energetic

conformations

12-13

. Recently, multiple iso-

of

wybutine 2

(yW)

14

and

2

the

N(6)

substituent

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-

conformational preferences of m G and m 2G have also

hand sence of rotation. Likewise,

been reported.15

angles [C(6)N(6)C(10)N(11)],

The structural significance of hn6Ade has not

the

the

torsion

[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)],

been investigated by any experimental methods. Hence,

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

present study has been performed to understand the

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

conformational preferences of hypermodified nucleic acid

φ2[C(12)C(16)O(16a)H] define

the

rotation

of the

base, N6-hydroxynorvalylcarbamoyl, hn6Ade using various

successive chemical bonds along with the main extension

energy calculation and MD simulation methods. It is also of

of the substituent. The extended conformation with the

interest to find out the structural role of hydrophobic –

adopted convention has been chosen initially as a reference

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

point in the energy calculations. The standard bond length

found that 3-hydroxynorvalylcarbamoyl substituent spreads

and bond angle values are retained from the earlier

9


Table 1 Torsion angle values of the starting structures obtained by PCILO method (Conformer I and II) for hn 6Ade molecules Sr. No hn6ade molecule:

Torsion Angle (degree)

Relative Energy

I

01800θ=300, 0, 0, ω60 φ1= 90 φ2=180.

0.0

III

01800θ=300, 0, 0, ω0φ1=300φ2=180.

2.0

investigation

t6Ade9

on

hydroxynorvalylcarbamoyl)

N6-(3-

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

is

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

because 6

adenine,

hn Ade

an

analogue of N6-threonylcarbonyl adenine, t6Ade.

of hypermodified nucleoside, hn6Ade obtained by HF-SCF

2.1. Conformational search and geometry optimization

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

The conformational space has been searched for

V1.1.0 software.

6

the modified nucleic acid base hn Ade using quantum chemical PCILO method.

16-18

2.2. Molecular dynamics simulation study

This method has been found

To investigate the hydration effect on the

useful in conformational analysis of many bio-organic

modified base hn6Ade we performed molecular dynamics

molecules including nucleic acid constituent.

19-20

In PCILO

(MD) simulation study using Sybyl 7.3 commercial software

optimized

energy

optimized preferred conformations of modified base

calculation and energy correction terms up to third order

hn6Ade used as a starting geometry for molecular dynamic

are retained for each conformation. In logical selection of

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

grid points approach is used for searching the most stable

Marsilli charges and TIP3P model water has been chosen

21

for molecular dynamics simulation study. Minimal cubic

Conformational search by PCILO method resulted into two

periodic boundary conditions of diameter 35.968Å have

structure

throughout

and

the

the

conformational

alternative

stable

structure.

from

Tripos,

Inc.25

method polarities of each bond in the molecule are

The

PCILO-RM1-HF

Table 2 Full geometry optimization calculation using semi-empirical RM1and PM3 methods over the PCILO starting conformer I and II of hn6Ade Torsion Angle (degree) Conformer hn6Ade RM1

PM3

conformations

Relative Energy

α

β

δ

θ

ψ1

ψ2

ω

φ1

φ2

I II

2 1

357 337

171 171

281 284

295 293

188 187

181 180

71 72

48 233

186 175

0.00 1.06

I III

12 11

336 335

160 162

279 283

308 303

202 201

181 180

56 54

71 251

184 172

22.62 22.92

(conformation I

and

II)

and

these

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

conformations are then used as starting structures for the

The constant temperature (canonical ensemble) simulation

full geometry optimization calculations using PM3

22

and

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

RM123 methods in order to find out the most stable

dielectric function ‘constant’ held at 1. For temperature

6

structure of hn Ade. The lowest energy stable structure is

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

10


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

using semi empirical PCILO method. The relative energy

used. The other usual conditions applied includes 1 fs time

difference between conformations I and II found below 2.0

step, initial Boltzmann velocity distribution, and shake

Kcal/mol

algorithm for hydrogen atoms, 10 fs non-bonded update

considered as starting conformers in this study. The

with scaled velocities. To remove steric clashes initially,

structural properties of hn6Ade are not studied by

5000 cycles of steepest descent minimization steps were

crystallographically or by using NMR. Hence, in order to

applied to the whole system. This minimized system

search the whole conformational space of hn6Ade, the full

considered for 200 ps equilibration protocol followed by

geometry optimization has been performed over the

5000 cycles of steepest descent energy minimization.

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

Finally system is subjected for 1ns of production run time

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

(Table 1). These two conformations are

Table 3 Geometrical parameters for hydrogen bonding interactions in the PCILO-RM1-HF of hn6Ade (Figure 2). Atom involved Distance Distance Angle 1-2-3 atom Pair atom Pair 1-2-3 1-2 A° 2-3 A° degree N(1)...HN(11) 2.026 0.996 132.21 O(16b)...HO(13) 2.441 0.945 121.01 O(13)...HC(15) 2.600 1.083 95.00

optimized stable conformations Figure Ref. 2 2 2

by maintaining all parameters as described above. All

relative energies of geometry optimized conformations

calculations were performed on HP xw8600 workstation.

using RM1 and PM3 methods are then compared to

3. Results and Discussion

indentify energetically stable conformer of hn6Ade. It has

3.1. Conformational search by PCILO method

been revealed that conformation I obtained by PCILO-RM1

Table 1 depicts the torsion angle values of conformation I,

optimization is found energetically stable conformation as

and II of hypermodified nucleoside hn6Ade obtained after

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

the multidimensional conformational search carried out

RM1 optimized conformation I (Table 2) is then subjected

11


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)  θ=277, , , ωφ1=54 φ2=239.   θ=179, , , ω φ1=127φ2=340  θ=181, , , ωφ1=287 φ2=15

0-300

350

550

Atoms involved (1-2-3)

Distance atom pair 1-2 (A)

Distance atom pair 2-3 (A)

Angle 1-2-3 (degree)

Figure No.

N(1)...HN(11) O(10)...HO(13)

2.248 1.701

1010 0.960

119.41 166.13

3a

N(1)...HN(11)

2.401

1.010

127.79

3b

N(1)...HN(11) O(13)...HN(11)

2.058 2.574

1.010 1.010

132.22 92.27

3c

torsion angles describing the conformation

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

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

basis set to find out preferred most stable conformation of

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

hypermodified nucleoside, hn6Ade.

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

3.2. Geometry optimized stable conformation of hn6Ade

analogue

The predicted most stable structure of the hypermodified

hydroxynorvalylcarbamoyl side chain spreads away ‘distal’

of

hn6Ade.

The

are

α=1º,

to full geometry optimization with the help of Hartree-Fock

N(6) substituent

3-

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. nucleic

acid

base

N6-(3-hydroxynorvalylcarbamoyl)

adenine, hn6Ade obtained by PCILO-RM1-HF (6-31G**) optimization is depicted in Figure 2. The optimized

from the five membered imidazole moiety of adenine ring as observed in N(6)–threonylcarbamoyl adenine t6Ade 6 6

2 6

9-10

,

11

m t Ade and mS t Ade. This kind of orientation prevents

12


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

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

adjacent (37th) position and thus avoid misrecognition of

of hydrophobic –CH2CH3 group observed the in most

codons. The intramolecular interactions (Table 3) between

stable and alternative stable conformations of hn6Ade could

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

play an important role during codon-anticodon interactions

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

in hyperthermophiles. This extra hydrophobic group

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

present in hn6Ade as compared to t6Ade may be helpful for

partial double bond character from the adenine ring through

the growth of hyperthermophilic bacteria and archaea at

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

elevated temperatures.4

are essentially constrained to adopt planar cis or trans

3.3. MD simulation of PCILO-RM1-HF optimized

orientation. In addition to this the strong steric repulsion

stable structure of hn6Ade

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

Molecular dynamics (MD) simulation has also

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). ruled out the trans orientation of α torsion angle. The 6

been performed to explore the conformational space of

hydrophobic –CH2CH3 group of hn Ade prefers extended

hypermodified nucleic acid base hn6Ade using Sybyl7.3

conformation forming an intramolecular interactions within

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

13


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

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

simulation study. The results of torsion angle and

maintained the uriedopurine ring as well as hydrogen

geometrical parameters for the average structure and

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

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

and C). The geometrical parameters for torsion angle

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

values and hydrogen bonding interaction for the snapshot

snapshot structures at 350 ps and 550 ps to compare the

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

conformational preferences of most stable structure

above discussed snapshot structure taken at 350 ps (Figure

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

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

average

‘distal’

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

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

and

snapshot

structures

maintain

results are in close agreement with experimental study of

away from the five membered imidazole moiety of adenine

modified base t6Ade.27

ring as observed in the most stable structure of hn6Ade

The fluctuations in the torsion angle   Figure

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

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

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

whereas torsion angle  Figure 4C fluctuates between

maintained during 1ns molecular dynamics simulation

180 which indicates that the uriedopurine ring as well as

period as observed in the PCILO-RM1-HF optimized

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

structure (Figure 2).

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

The average structure (0-300 ps) having torsion

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

angle values are (θ=

angles show fluctuations over 1ns MD simulation period

277, , , ω23φ1=54φ2=239). The

(Figure 4). The torsion angle  Figure 4Dand θ  Figure

torsion angle values for α, β, γ, φ1 and  show small

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

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

fluctuates between 120-180after ps till end of the

about 30 whereas φ2 shows large variation as compared to

simulation period. The next torsion angle φ1  Figure

stable structure (Figure 2). The average structure is

4Ifound well maintained up to 0-300 ps after that it

stabilized from the hydrogen bonding interactions between

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

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

angles

N(11)-C(12) are possible in case of hn6Ade also similarly as explained in the crystal structure of t6Ade.27 The average

4FFigure

4GFigure

4HandFigure 4J show fluctuations between 180. The fluctuations of torsion angles as shown in (Figure 4DJ) 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

6

an analog of hn Ade 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

hydrogen bonding from 3'-adjacent site of anticodon loop of tRNA. This proves that the rotations around the bond

Figure

Conformational preferences of modified base, 6

hn Ade performed using PCILO method followed by semi empirical RM1-HF optimization along with molecular dynamics simulation study shows that N(6) substituted 3-

14


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

chain

side

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 hn 6Ade 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 6

N(1)….HN(11) and O(13)….HN(11) of hn Ade 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/LS028/2007) 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

References 1. Motorin Y., Bec G., Tewari R., and Grosjean H. RNA. (1997) 3, 721. 2. Morin A., Auxilien S., Senger B., Tewari R., and Grosjean H. RNA. (1998) 4, 24. 3. Persson B. C. Mole Microbiology. (1993) 8, 1011. 4. Reddy D. M., Crain P. F., Edmonds C. G., Gupta R., Hashizume T., Stetter K. O., Widdel F., and McCloskey, J. A. Nucleic Acids Res. (1992) 20, 5607. 5. Agris P. F. Prog. Nucleic. Acid. Res. Mol. Biol. (1996) 53, 79. 6. Chheda G. B., Hall R. H., Magrath D. I., Mozejko J., Schweizer M. P., Stasiuk L., and Taylor P. R. Biochemistry. (1969) 8, 3278. 7. Schweizer M. P., Chheda G. B., Baczynskyj L., and Hall R. H. Biochemistry. (1969) 8, 3283. 8. Davis D. R. in Modification and Editing of RNA, Grosjean H., Benne. R Eds.: ASM Press: Washington, (1998) P 85. 9. Parthasarathy R., Ohrt J. M., and Chheda G. B. Biochemistry (1977) 16, 4999. 10. Tewari R. Ind. J. Biochem. Biophys. (1987) 24, 170. 11. Tewari R. J. Biomol. Struct. Dyn. (1990) 8, 675. 12. Sonawane K. D.; Sonavane, U. B. and Tewari R. J. Biomol. Struct. Dyn. (2002) 19, 637. 13. Sonawane K. D. and Tewari R. Nucleos. Nucleot. Nucleic. Acids. (2008) 27, 1158. 14. Kumbhar N. M. and Sonawane K. D. J. Mol. Graphics. Modell. (2011) 29, 935. 15. Bavi R. S., Kamble A. D., Kumbhar N. M., Kumbhar. B. V., and Sonawane K. D. Cell Biochem. Biophys. (2011) 61, 507. 16. Masson A., Levy B., and Malerieu J. P. Theor. Chim. Acta. (1970) 18, 193. 17. Diner S., Malrieu J. P., and Claverie P. Theor. Chim. Acta. (1969) 13, 1. 18. Diner S., Malrieu J. P., Jordan F., and Gilbert M. Theor. Chim. Acta. (1969), 15, 100. 19. Pullman B., and Pullman A. Adv. Protein. Chem. (1974) 28, 347. 20. Pullman B., and Saran A. Prog. Nucleic. Acid. Res. Mol. Biol. (1976) 18, 215. 21. Tewari R. Int. J. Quant. Chem. (1987) 31, 611. 22. Stewart J. J. P. J. Comp. Chem. (1991) 12, 320. 23. Rocha G. B., Freire R. O., Simas A. M., and Stewart J. J. P. J. Comp. Chem. (2006) 27, 1101. 24. Hehre W. J., Radom L., Schleyer P. V. R., and Pople J. A. In Ab Initio Molecular orbital Theory, Wiley, New york, (1986). 25. Tripos International (2006) SYBYL 7.3, Tripos International, South Hanley Rd., St. Louis, Missouri, USA 26. Weiner C. J., Kollman P. A., Case D. A., Singh U. C., Ghio C., Alaqom G., Protera S., and Weiner P. J. J. Am. Chem. Soc. (1984) 106, 765. 27. Murphy F. V., Ramakrishnan V., Malkiewicz A., and Agris P. F. Nat. Struct. Mol. Biol. (2004) 11, 1186.

University Grants Commission, New Delhi for providing fellowship as a Project Fellow under the scheme UGC SAP DRS-I.

15


Microbial Genomics Tool (MGT 1.0) for Bacterial Codon Usage Analysis Rajendra Verma1, Ragini Gothalwal1, Kamalraj Pardasani2, Anil Prakash, Kishor Shende1* 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: kishor556@hotmail.com

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. an organism helps in understanding the basis of molecular

1. Introduction

biology of gene regulation and gene expression. This can Over the past three years, parallel DNA sequencing platform have reduced the cost of DNA sequencing. Next

indirectly help in understanding the morphology, physiology and phylogeny of organisms2-8.

generation sequencing has the potential to dramatically accelerate biological and biomedical research, by enabling the 1

The controversial ideas of Kimura, Kings and Jukes on

comprehensive genome sequencing and analysis . More than

natural evolution led some early detractors to postulate that

7000 sequenced bacterial genomes are available at NCBI ftp site

usage of synonymous codon in protein coding genes is not

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

necessarily random and that codon composition could be biased

RNA viruses) genetic information to carry out cellular processes

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

and same is transferred generation after generation. Three

organism. This prediction was partially confirmed by Grantham

important

and

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

translation are meant to transfer the genetic information to form

sequences genes available at that time and proposed that each

functional assembly, a cell. Translation is the process where

genome has a particular codon usage signature that reflects

signals in nucleotide sequence form is converted to sequence

particular evolutionary forces acting with that genome2-4.

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

Consequently they proposed „Genome Theory‟. According to

hence the code is redundant and multiple codons can specify the

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

same amino acid according to Wobble hypothesis given by

characteristic of an organism”. Organism specific codon usage

Crick. Multiple codons coding for single amino acid are called

pattern suggested that the variation in codon usage pattern might

synonymous codons. The correspondence between codons and

be correlated with variation in tRNA abundance2-3, which

amino acids is nearly universal among all known living

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

organisms with a few variations. Different organisms often

codon usage pattern showed remarkable variation in strongly

show different preferences for synonymous codons called as

and weakly expressed genes4. A modulation of the coding

codon usage bias. The codon usage patterns differ significantly

strategy according to expression was proposed such that codons

depending upon several factors such as mutational bias, natural

found in abundant mRNA were under selection for optimal

selection for translation optimization. Codon usage analysis in

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

processes

viz.

replication,

transcription

16


variation in codon usage is dependent on translational level and

of them can calculate only CAI (Codon Adaptation Index)

the codon usage of abundant protein genes could be

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

distinguished from other genes9.

application doesn‟t work.

Codon usage pattern analysis is a method to understand the

This project was undertaken to develop a software tool

bacterial genome. Various codon usage indices are formulated

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

to understand the codon usage bias and factor that affect this

will provide the output format suitable for statistical analysis in

biasing. Codon usage indices such as RSCU (Relative

most of the Windows based statistical analysis software tool.

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

The provision is also made to implement the inclusion of new

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

applications which will be developed in future. MGT was

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

planned for open access.

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

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

also calculates standard indices of codon usage. It has both menu and command line interface10. Lacks of user friendly

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

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

calculated as the observed codon usage divided by the average

11

software can calculate most

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

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

obtained if all codons for a particular amino acid are used

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

equally. RSCU removes the influence of amino acid

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

composition that is present in raw codon usage data.

for codon usage analysis. ACUA

analysis algorithms and also it is not updated since long. EEq. (2.2.1)

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

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

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

acid, encoded by in synonymous codons.

14

15

java. Other software such Jemboss and BioEdit 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.

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.

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

Eq. (2.2.2)

17


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

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

codons of that form

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

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

into two parts. First part is „Codon Analysis‟ that contains 3 tabs corresponding to 3 different applications. „Codon Table‟ tab

number of codons provides a way to quantify differential codon

calculate codon usage frequency, „RSCU‟ tab calculate RSCU

usage of a particular gene to the equal use of synonymous

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

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

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

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

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

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

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.

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.

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. 3.2. Program input MGT accepts nucleotide sequence as input text file with ORFs nucleotide sequences in fasta format as shown in Fig-2.

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

The sequence file is loaded through „Browse‟ tab. Multisequence 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

Eq. (2.2.3.3) Where, S - frequency of G+C (i.e. GC3) 3. Result and Discussion

BioEdit. 3.3. Program output MGT software calculates average and percentage codon frequency; codon usage indices such as AT3, GC3, RSCU, Nc

3.1. Microbial Genomics Tool (MGT) interface

and ENc value. Nucleotide composition calculation includes frequency of nucleotides (A, T, G and C), AT-percent and GC-

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

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

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

18


Fig. 2 Input file with Fasta formatted nucleotide sequence. and also can be saved in MS Excel file format (Fig-3). As the

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

result file is tab delimited it can be further processed for

source software. This software is at infancy stage and future

advanced statistical analysis by suing any window based

plan includes addition of applications for statistical analysis of

Statistical software.

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. 2. 3. Fig. 3 Result window showing results in (A) Result Bar (B) Result in MS-Excel file. 4. Conclusion

4. 5. 6. 7.

version is provided with user-friendly graphical interface.

8. 9. 10. 11.

Multiple sequences can be passed to tool and tabulated result for

12.

Microbial Genomic Tool (MGT 1.0), the first version is developed for In-silico research bacteria genomic study. This

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 multiapplication software for bacterial genome analysis. MGT1.0 is open access and can be obtained from sourceforge site

13.

14. 15. 16. 17.

Shendur J. and Hanlee J. Nature Biotechnol. (2008) 26, 1135. Grantham R. C., Gautier and Gouy M. Nucleic Acids Res. (1980) 8, 1892. Grantham R. C., Gautier, Gouy M., Mercier R. and Pave A. Nucleic Acids Res. (1980) 8, r49. Grantham R. C., Gautier, Gouy M., Jacobzone M. and Mercier R. Nucleic Acids Res. (1981) 9, r43. Sharp P. M. and Li W. H. J. Mol. Evol. (1986) 24, 28. Sharp P. M. and Li W. H. Nucleic Acid Res. (1987) 15, 1281. Sharp P. M., Bailes E., Grocock R. J., Peden J. F. and Sockett R. E. Nucleic Acids Res. (2005) 33, 1141. McInerney J. O. Bioinformatics (1998) 14, 322. Gouy, M. and Gautier C. Nucleic Acids Res. (1982) 10, 7055. Peden J. http://www.sourceforge.net/ (2005). Umasanker V., Vijay K., Arun K. and Dorairaj S. Bioinformation (2007) 2, 62. Garcia-Vallve S., Puigbo P., Bravo I. G. BMC Bioinformatics (2008) 9, 65. Grete A., Hiller K., Monice, Much R., Nortemann B., Dietmar C., Hempel and John D. Nucleic Acid Res. (2005) 33, W536. Carver T. and Bleasby A. Bioinformatics (2003) 19, 1837. Hall T. A. J. Nuclic Acid SYMP (1999) 41, 95. Wright, F. Gene (1990) 87, 23. Rice P., Longden I. and Bleasby A. Trends Genet. (2000) 16, 276.

19


An application of radon and wavelet transforms for image feature extraction Heena Patel*, Saurabh dave, Himanshu Patel, and Chintan dave Ganpat University, Kherva, India

*hkp01@ganpatuniversity.ac.in 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.

preprocessing.

1.1 Introduction

Individual

features

are

obtained

by

connection of these blocks using a wavelet decomposition 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

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

forming

a

very

important

mathematical tool used in tomography is based upon works of Johann Radon born in 1887 Litomerice. His doctoral

graphically and in the tables.

(a)

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:

(b)

R(θ,r)R(θ,r)[f(x,y)]= 

  f ( x, y) (r  x cos   y cos  )dxdy



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

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.

of Radon transform, wavelet transform and blocks of image

20


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

(a)

different values of r are presented in Figure 1(a). The parallel lines are used for the integration of the given

20

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

40

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

60 80

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

100

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

120

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

20 40 60 80 100120

different intensity of color, depending on its value, having value 0 corresponding to black and 1 corresponding to

(b)

white color presented in Figure 3(b). A discrete Radon transform called Hough transform has been introduced in 20

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

40 60 80 100 120 20 40 60 80 100120

(c)

60

Fig.2 Block diagram of the proposed technique

x'

-50

40

0

extraction. 20

50

1.2 Simulation of Radon Transform

0

50

100

150

 (degrees)

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

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.

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.

• Choice to Collapse 2-D results to 1-D 1.3 Radon Transform to Detect Lines The Matlab function or M-file use every ”Matlab function” block for processing of the input value. Figure 2

The Radon transform is closely related to a common

presents block diagram of the direct and inverse Radon

computer vision operation known as the Hough transform.

transform of MR image and visualization of Radon

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

transform image. Input image is loaded as a constant and

Hough transform used to detect straight lines adjusted to

21


function that limits the duration of the analyzed signal

uses fixed size windows that cannot be suite the speed of

segment.

the changing phenomena observed in the input signals.

(a)

Wavelets solve this problem by using the so called

50

100

150 50

100

150

(b)

50

Fig. 5 Wavelet decomposition mother wavelet which can be scaled and translated to

100

achieve both time localization and multi-resolution. The 150

decomposition stage results in this way in four images 50

100

150

representing all combinations of low-pass and high-pass initial image matrix. The reconstruction stage includes row

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

(a) 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 W ( (t  b)) a a

Eq. 2

For discrete parameters of dilation a=2m and translation

(b)

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

Fig. 6 (a) 2-Level and 4-Level Decomposition. (b) 2level Decomposition of reference image

22


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

”Transpose” enables matrix transposition. In our diagram it

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

enables matrix transposition after column downsampling to

assumes column

with

proceed row decomposition. We transpose matrix after the

reconstruction filters followed by summation of the results

row decomposition to visualize matrix right. Diagram for

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

one decomposition and reconstruction levels is presented in

steps D.2 and R.1 are omitted.

Figure 5.

upsampling and

convolution

Table 1 STD computed from rotated MR image features. STD of MR Image Features

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

Feature-1

Feature-2

DWT

0.0013

0.0254

diagonal DWT transform coefficients in the first and the

RT-DWT

2.97 X 10-5

0.0023

second decomposition levels using MR images obtained by

the standard deviation (STD) of the sum of squared

rotation from 0 to 180 degrees with step 10◦ using (i) diagram with the plain DWT,(ii) diagram for the Radon. 1.5 Simulation of Wavelet Transform in Simulink Environment

2. Results

Wavelet transform diagram was created with blocks of

Thanks to the objective confrontation of STDs, Table 1 is

Simulink library. Block”DWT” computes the discrete

the bright example that the Radon transform is a powerful

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

wavelet transform using a filter bank with specified

tool expressively contributing to image analysis. The

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

improvement of the STD between the plain DWT and RT-

or formed by wavelets of the Wavelet Toolbox. The output

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

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

small improvement by denoising of the magnetic resonance

a frame-based vector or matrix. The common block

image. Therefore image enhancement is very desirable

23


(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 translation invariant feature selection using appropriate

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

image transforms.

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

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.

functions and different rotation steps in case of the Radon

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

transform.

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.

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

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.

transforms to form fundamental mathematical tools. It is

7. Wikipedia. Johann Radon.

assumed that further studies will be devoted to feature

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

based image segmentation and further methods of rotation

24


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: mpmoharil@gmail.com 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. Plant synthesizes various proteinaceous compounds

1. Introduction Helicoverpa

armigera,

(Lepidoptera:

against an insect attack, among the several plant defense

Noctuidae), a highly devastating polyphagous crop pest,

proteins. Proteinase inhibitors (PIs) are abundantly

has a broad host spectrum causes a significant yield losses

present in seeds and storage tissues represents up to 10

in many agriculturally important crops like cotton,

per cent of the total protein (Casaretto and Corcuera 5).

chickpea,

pigeonpea,

okra,

PIs act as antimetabolic proteins, which interfere with

sorghum,

pearl

corn,

maize,

groundnut

the digestive process of insects. PIs are particularly effective against phytophagous insects and micro-

worldwide are directed against H. armigera which

organisms. The defensive capabilities of PIs rely on

resulted into high levels of insecticide resistance in this

inhibition of proteinases present in insect guts or

pest. Insecticide resistance in H. armigera is widespread

secreted by micro-organisms, causing a reduction in the

problem in India, Pakistan, China, Australia, Thailand

availability of amino acids necessary for their growth

Indonesia

sunflower

tomato,

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

and

millet,

Hubner

2

(Ahmad ).

Bacillus

and development. Most PIs interact with their target

thuringiensis (Bt) either in the form of formulation and

proteinases by contact with the active (catalytic) site of

transgenic plant may lead to develop resistance in insect in

the proteinase resulting in the formation of a stable

a short period of time. Since many insect pests have

proteinase-inhibitor complex that

is incapable of

developed resistance to Bt like chemical pesticides (Oppert

enzymatic

and

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

Preliminary studies on presence of proteinase inhibitors

alternative

and

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

natural plant

okra inhibited both bovine trypsin and chymotrypsin,

defense system, promises to lead in this aspect in near

which are typical digestive enzymes. This study showed

methods

The

and

of controlling

use

of

these

proteinase inhibitors (PIs), constituent of

pest

activity

(Lawrence

Koundal 6).

4

future (Mosolov and Valueva ).

25


that okra seeds contain PIs of trypsin, chymotrypsin

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

which constitute the defense machinery.

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

In the present work, different okra genotypes and

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

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

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

Several potent and high potential PIs were identified in

coomassie brilliant blue R-250. HGPs bands were revealed

wild relatives of okra. Bioassays were performed to

as white bands with dark blue background.

ascertain the potency of the okra inhibitors in inhibiting

2.4 Proteinase and PI assays

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

Total proteinase activity was measured by azocaceinolytic

exploited for planning the strategies for developing

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

insect resistance transgenic plants in future.

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

2. Material and Methods

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

2.1 Seed material and PI extraction

added and incubated for 1 hr at 370 C. The reaction was stopped by adding 500 µl of 5% ice cold trichloroacetic

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).

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-AlaPro-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.

2.2 Extraction of HGPs

For the inhibitory assays, a suitable amount of inhibitor and

Late third or early fourth instar larvae, from homogenous

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

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

the

0

addition

of

substrate.

H.

armigera

trypsin

isolated and stored frozen at -78 C. Required gut tissue was

,chymotrypsin and total gut proteinase inhibitory activities

homogenized in 1 volume of 0.2M glycine-NaOH buffer

were estimated by using substrate BApNA, SAApLNa and

o

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

azocasin. 30 µl proteinase inhibitor and 50 µl gut extract

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

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

was used as a source of HGPs.

substrate were added to each well after 1 min incubation at

2.3 Electrophoretic visualization of HGPs

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

HGPs were detected by using by SDS-polyacrylamide

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

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

total gut proteinase inhibitory activity, after adding 5%

larvae was diluted and electrophoresed on 12% SDS-

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

polyacrylamide gels along with treatment buffer 60mM

and absorbance was estimated at 405 nm. One proteinase

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

unit was defined as the amount of enzyme that increases

26


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.

Fig.1 Electrophoretic visualization of H. armigera gut proteinase isoforms .

armigera

absorbance by 1 OD/ min and one PI unit was defined as the amount of inhibitor that causes inhibition of 1 unit of

Total gut proteinase (azocaseinase), trypsin like proteinases (BApNAase)

and

chymotrypsin

like

proteinases

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

proteinase activity under the given assay conditions. 2.5 Electrophoretic visualization of TIs , CIs and HGPIs

assayed (Table 1). Total Proteinases activity was observed

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

Table 1: H. armigera gut proteinases activity

11

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

The respective gels were transferred to solutions containing

No.

0.1 % trypsin or 0.1 % Chymotrypsin or HGP extract of equivalent activity, and incubated for 1 hrs with constant

1

Proteinases Total

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

Proteinase

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

activity

destained. Isoforms were revealed as blue bands against

2

white background.

(U/gut)a 2.15 ± 0.001

1.97 ± 0.003

activity

2.6 Bioassay of PIs against H. armigera larvae

3

Bioassay was carried out at insect rearing facility of Department of Entomology, Dr. PDKV, Akola.

Tryptic

Enzyme activity

Eggs,

Chymotryptic

1.84 ± 0.002

activity a

All figures are mean of triplicate ± SE.

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

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

80% relative humidity on fresh and soft seeds of pigeonpea

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

until further use. Bioassay was carried out according to

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

27


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. AKO37, 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-

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

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

proteinases in the gut of H. armigera.

total H. armigera gut proteinase activity was distributed in

The presence of proteinases of different specificities in

ten isoforms, ranging from molecular weight 118.0 kDa to

the midgut has great significance for the survival and

16.2 kDa. The apparent density of P 1, P2, P3, P7, P8 and P9

adaptation of phytophagous insects on several host plants.

found to be high, while that of P 4, P5, P6 and P10 were low.

The adaptation of pests to host plant PIs probably results

Earlier studies on proteolytic activity of lepidopeteran

from the selection pressure acting on an entire insect

insect gut showed that, insect gut comprises of many

population when they encounter PIs of their host plants

isoforms of proteinases having diverse properties and

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

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

present investigation supported the polyphagous nature of

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

Helicoverpa armigera.

revealed that H.armigera gut proteinase activity was

3.2 Electrophoretic profiles of TIs, CIs and HGPIs

distributed in six isoforms. Similarly, Potdar

15

studied

isoforms from different genotypes of okra and its wild relatives

28


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 (%)

1 2 3 4 5 6 7 8 9 10

A .tuberculatus 90396 A. tuberculatus 90515 A .tuberculatus 90400 A.. tuberculatus 140957 A. tuberculatus 90402 A. fiulneus 140986 A. tetraphyllus 90398 A .tetraphyllus 90461 A. tetraphyllus 90386 A. fiulneus 41748

Wild relatives of okra 71.8±0.001 68.4±0.004 69.2±0.003 66.2±0.004 62.4±0.005 59.3±0.005 67.0±0.006 62.7±0.003 60.4±0.006 60.2±0.004 54.4±0.005 50.2±0.003 49.4±0.002 51.7±0.003 48.0±0.001 50.5±0.005 51.2±0.005 51.4±0.005 46.1±0.002 44.1±0.003

70.2±0.002 68.3±0.003 61.4±0.001 60.6±0.004 62.2±0.002 46.1±0.002 43.1±0.005 46.9±0.002 45.0±0.003 38.1±0.003

11 12

A. fiulneus 141042 A. tetraphyllus 92503

39.9±0.003 44.5±0.001

42.5±0.003 41.8±0.003

42.7±0.002 43.8±0.002

13 14

A. fiulneus 210361 A. tetraphyllus 90404

47.0±0.006 44.5±0.004

42.6±0.002 43.3±0.002

40.5±0.004 46.9±0.003

15 16

A. fiulneus 140947 A. angulossus 203832

43.4±0.003 65.3±0.004

41.8±0.002 55.5±0.006

43.5±0.002 59.1±0.003

17

A. angulossus 203863

53.0±0.002

50.9±0.003

46.1±0.001

18 19

A. angulossus 470751 A. manihot 141019

50.2±0.002 47.3±0.002

49.4±0.003 48.7±0.003

47.3±0.003 43.8±0.002

20 21

A. manihot 141045 A. angulossus 203833

42.4±0.002 51.5±0.003

47.9±0.001 45.6±0.003

42.9±0.003 51.5±0.001

22

A. angulossus 203834

48.0±0.003

42.6±0.004

46.5±0.003

23 24

A. manihot 141012 A. moschatus 140985

57.9±0.002 49.4±0.001

45.2±0.003 49.8±0.001

45.4±0.003 44.3±0.002

PIs 25 were isolated from ten genotypes of okra45.9±0.003 and 29 A. moschatus 141056 26 A. moschatus 141065 50.1±0.002 wild relatives by the method given by Felicioli et al,11. Gel

weight 25.1 kDa to 6.3 kDa) with43.5±0.002 variable intensities. A. 44.1±0.002 51.7±0.004 43.3±0.003 tuberculatus (90396, 90515, 90400, 140957 and 90402)

27 A. moschatus 470737 54.0±0.003 co-polymerized with 1 percent gelatin was used for the 28 A. moschatus 470747 52.4±0.004 detection of TIs, CIs and HGPIs bands. 29 A. manihot 329394 42.7±0.004 All wild relatives of okra showed variability in terms Genotypes of okra

54.7±0.002 48.4±0.003 reported maximum (five) CIs isoforms. While, A.ficulneus 52.0±0.002 50.7±0.003 (140986, 141042, 210361, 140947), A. tetraphyllus 40.3±0.002 41.9±0.003 (92503), A. moschatus (141065) and A. manihot (329394)

of the number intensities 30 and Arka bahar of TIs bands. A. tuberculatus 53.7±0.004 31 Parbhani krantihighest (six) TIs isoforms, 63.8±0.005 90396 and 90515 exhibited 32 AKO -107 53.9±0.004 A.angulossus (203832) showed four TIs isoforms, whereas, 33 Arka anamika 55.1±0.001 A. ficulneus 141042, 210361 and 140947)50.5±0.004 and A. 34 (41748, AKO-37 tetraphyllus exhibited the minimum (one) TIs 35 (90404) Pusa A-4 51.9±0.002

exhibited46.0±0.002 only one CIs isoform. Different okra genotypes 46.6±0.002 62.1±0.003 58.4±0.004 exhibited maximum number of (four) of CIs isoforms, 50.1±0.003 56.8±0.003 except Arka bahar which showed only one CIs isoform. 51.7±0.003 52.6±0.002 Results clearly indicate the potentiality of A .tuberculatus, 48.6±0.002 51.5±0.001 to search45.6±0.004 new and potent proteinase inhibitors. This is also 54.5±0.002

AKO-111 57.9±0.003 isoforms.36All okra genotypes showed monomorphoic PIs 37 AKO-102 65.6±0.002 profile i.e. four TIs isoforms were detected in all genotypes 38 Adunika 60.0±0.003 of okra 39 with dark intensity, except Arka bahar63.9±0.002 which VRO-3

50.9±0.005 62.2±0.003 confirmed by our studies on TIs and HGPIs isoform. 56.7±0.003 60.3±0.001 To determine specificities of PIs towards HGP 55.7±0.001 63.3±0.003 isoforms,61.9±0.002 PIs extract were 62.9±0.002 resolved on gelatin-

showed less intense TIs isoforms.

polyacrylamide gel. Further, it incubated with HGP extract

Similarly, gelatin co-polymerized polyacrylamide gel

obtained from mid gut of Helicoverpa larvae (equal TI

electrophoresis showed wide range of CIs (molecular

units), HGPI bands were visualized as described in

29


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,

Fig. 3 Effect of okra PIs on the growth and development of H. armigera larvae (Plate 3).

whereas, diverse proteinase inhibitory profiles in pigeonpea wild relatives. Patankar et al,18 also observed significant

genotypes. The variation observed in the wild relatives of

variation in the TIs isoforms from wild Cicer species.

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

However, they have observed great conservation of TIs

defense proteins against herbivores (Ryan21).

isoforms in the mature seeds of the chickpea cultivars. A

Wild relatives of okra A.tuberculatus (90396 and

similar observation exists in pigeonpea where TIs and

90515) showed eight HGPIs isoforms with high intensity,

chymotrypsin inhibitors are conserved in matured seeds of

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

the cultivated pigeonpea, whereas, a high level of diversity

maximum (five) HGPIs isoforms while, Arka bahar

19

exsist in uncultivated species of Cajanus (Kollipara et al, , Pichare and Kachole

20

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

). The variation observed in wild

clearly showed that PIs from wild relatives of okra A.

Cicer species is considered significant, as the TIs are

tuberculatus (90396 and 90515) exhibited strong inhibitory

known to serve as a defense proteins against herbivores

potential against HGP. Earlier studies on electrophoretic

(Ryan 1990). Cicer reticulatum and Cicer arietinum

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

showed similar TIs band patterns, which suggests that

Rhynchosia group showed presence of high activity HGPIs

Cicer reticulatum is genetically closer to Cicer arietinum.

bands (5) as compared to pigeonpea and other wild

Thus, this studies can also be used for karyotyping the

Cajanus species (Chougule et al, 17).

30


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 1 2 3 4 5 6 7 8 9 10 11 12 13 Pupa Mean F-test

Weight of larvae (mg) when fed with A. tuberculatus (90396) A. tuberculatus Control (without PI) (90515) 23.8 25.3 27.3 28.2 31.0 31.7 38.3 40.0 43.3 47.1 50.1 55.0 54.7 56.7 72.3 71.0 89.7 98.7 91.0 120.3 138.0 116.0 130.0 162.7 121.3 141.3 186.7 127.6 156.0 221.3 144.7 180.0 259.7 156.3 211.7 297.7 176.3 224.7 330.3 178.0 225.7 329.3 98.2 120.2 161.0 Age Variety Interaction significant significant significant

SE CD at5% a

1.78 4.96

3.72 10.32

Meanb

25.5 30.3 40.5 50.7 61.2 86.4 116.4 136.2 149.8 168.3 194.8 221.9 243.8 244.3

6.45 17.88

DAI- Days after ingestion of proteinase inhibitor, bMean of all the survival larvae

3.3 Inhibitory potential of PIs from different genotypes

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

of okra and its wild relatives against Helicoverpa gut

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

proteinases.

tryptic activity was observed in PIs of A. tuberculatus

Several genotypes of okra and its wild relatives were

(141042) and maximum tryptic inhibitory potential

analyzed for their inhibitory potential against HGP activity.

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

Inhibition capacity of okra PIs towards HGP was evaluated

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

by in-vitro micro plate adopted enzyme assays. Low

trend of inhibition was observed in case of Helicoverpa gut

concentration of proteinase inhibitors (30Âľg) was used to

chymotryptic activity and Helicoverpa gut total proteinase

obtain inhibition of tryptic, chymotryptic and total gut

activity.

proteinase activity. Control was maintained without any PIs and its activity was considered as 100%.

Earlier studies on wild relatives of pigeonpea showed more than 70 percent inhibition, whereas, cultivars showed

Helicoverpa gut consist of both tryptic and

around 50 percent inhibition of HGP (Chougule et al,

17

).

chymotryptic activity. Tryptic activity was slightly higher

Moreover, the proteases from H. armigera were inhibited

than chymotryptic activity. Therefore, inhibitory potential

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

of PIs towards trypsin as well as chymotryptic activity was

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

considered to be useful potent PIs.

PIs inhibited more than 60 percent total proteolytic activity

Table 2 summarizes the inhibitory potential of PIs

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

obtained from various okra genotypes and its wild relatives against

Helicoverpa

tryptic

activity,

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

Helicoverpa

H. armigera is a polyphagous pest and possesses

chymotryptic activity and total proteinase activity. A close

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

examination of data revealed that different okra genotypes

the effectiveness of okra wild PIs offers good gene pool for

possessed tryptic inhibitory activity ranges from 50.5%

the development of H. armigera (Bhendi fruit borer)

31


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

blackish malformed pupae, which the normal pupal were Table 4: Effect of okra PIs on the growth and development of H.armigera . Growth and developmental parameters

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)

Larval mortality % Larval wt. reduction % (Control= 330.3mg larval Wt.) Reduction in pupal wt. % (Control 329.3mg) Malformed pupae %

Proteinase inhibitors A. tuberculatus (90396)

A. tuberculatus (90515)

40

30

53.4

68.0

54.1

68.5

60

50

10

10

30

20

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

Pupal Mortality % Malformed adult %

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

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.

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

32


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, Shukla et al,

25

and Bhavani et al,

24

, Tamhane et al,

23

,

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 (50Âľl) 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

5. References 1.Volpicella M L, Ceci R, Cordewener J., America T., Gallerani R., Bode W., Jongsma M. A. and Beekwilder J. Eur. J. Biochem (2003) 270,10. 2. Ahmad M. J. Agric. Res (2007) 45, 56. 3. Oppert B., Kramer K. J., Beeman R. W., Johnson. D. and Mc Ganghay W. H. J. Biol. Chem. (1996) 272, 23473. 4. Mosolov V. V. and Valueva T. A. Appl. Biochem. Microbiol (2008) 44, 233. 5. Casaretto J. A. and Corcuera L. J. Biol. Res. (1995) 28, 239. 6. Lawrence P. K. and Koundal K. R. Electron. J. Biotechnol (2002) 5, 93. 7. Ogata F., Imamura H., Hirayama K. and Makisumi S. Agric. Biol. Chem (1986) 50, 2325. 8. Bradford M. Anal Biochem (1976) 72, 248. 9. Gujar G. T. and Chandrashakar K. Indian J. Expt. Biology (2004) 42, 164. 10. Marchetti S., Delledonne M., Fogher C., Chiaba C., Chieja F., Savazzini F. and Glordano A. Theor. Appl. Genet (2000) 101, 519. 11. Felicioli R., Garzelli B., Vaccari L., Melfi D. and Balestreri E. Anal Biochem (1997) 244, 176. 12. Bhavani P., Chumki B. and Theertha P. ArthropodPlant Interactions (2007) 1, 255. 13. Johnston K. A., Lee M. J., Gatehouse J. A. and Anstee, J. H. Insect Biochem (1991) 21, 389. 14. Harsulkar A. M., Giri A. P., Gupta V. S., Sainani M. N., Deshpande V. V., Patankar A. G. and Ranjekar P. K. Electrophoresis (1998) 19, 1397. 15. Potdar S. S. (2008) M. Sc. Thesis (unpublished), Dr P. D. A. U., Akola. 16. Harsulkar A. M., Giri A. P., Patankar A. G., Gupta V. S., Deshpande V. V., Ranjekar P. K. and Sainani M. N. Plant physiology (1999) 121, 497. 17. Chougule N. P., HivraleV. K., Chhabda P. J. and Giri A. P. Photochemistry (2003) 64, 681. 18. Patankar A. G., Giri A. P., Harsulkar A. H., Sainani M. N., Deshpande V. V., Ranjekar P. K. and Gupta V. S. Insect Biochem. Mol. Bio. (1999) 31, 453. 19. Kollipara K. P., Singh L. and Hymowit Z. T. Theor Appl Genet (1994) 88, 986. 20. Pichare M. M. and Kachole M. S. J. Biochem Biophys Methods (1994) 28, 215. 21. Ryan C. A. Annual Rev Physiol (1990) 28, 425. 22. Zhou J. Y., Liao H., Zhang N. H., Tang L., Xu Y. and Chen, F. Biotechnol. Lett (2008) 30, 1495. 23. Tamhane V. A., Chougule N. P., Giri A. P., Dixit A. R., Sainani M. N. and Gupta V. S. Bichimica ET Biophysica Acta (2004) 1722, 156. 24. Kranthi K. R., Jadhav D. R., Wanjari R., Kranthi S. and Russel D. J. Econ. Ent. (2002) 94, 253. 25. Shukla S., Arora R. and Sharma H. C. Plant Biotechnology (2005) 22, 1. 26. Franco O. L., Dias S. C., Magalhaes C. P., Monteiro A. S., Melo F., Oliveira Neto O. B., Monnerat R. G. and Grossi-de-sa M. F. Pytochemistry (2004) 65, 81.

for developing PIs based management strategies of lepidopteran pest general and H. armigera in particular.

33


Science cartoons by Sumanta Baruah (Contact: sumanta.baruah@gmail.com)

IRSAPS Bulletin 2011, Vol. 1, Issue 3

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