STRUCTURAL ANALYSIS OF PKNA PROTEIN IN MYCOBACTERIUM TUBERCULOSIS

IJBPAS, July, 2013, 2(7): 1513-1525

ISSN: 2277–4998

STRUCTURAL ANALYSIS OF PKNA PROTEIN IN MYCOBACTERIUM TUBERCULOSIS MERLY DP1, JAYACHANDRA SY1, REDDY KM1, ANIL KUMAR S1, DARLEY SP2 AND SULOCHANA MB1* 1: Department of PG Studies and Research in Biotechnology, Gulbarga University, Gulbarga, Karnataka, India 2: Head of Computer Centre, Gulbarga University, Gulbarga * Corresponding Author: E Mail: [email protected]; Mob. No: 09449618676 ABSTRACT Mycobacterium tuberculosis contains 11 serine threonine kinases which includes PknA and PknB. PknA is a secreted protein of M. tuberculosis. PknA is identified as one of the most interacting protein in our protein-protein interaction study between M. tuberculosis and human. Primary structure prediction and physicochemical analysis of PknA was carried out by ProtParam. Comparative study of the secondary structure of PknA using GORIV and SOPMA revealed a high percentage of alpha helix and coiled coils than beta sheets. In the present study, we predicted PknA tertiary structure by homology modelling because its structure is not yet revealed by any experimental methods. The structural analysis will help in the functional study of the protein. Key words: PknA, Homology Modeling, Serine/ Threonine Kinase, M. tuberculosis, Procheck Validation INTRODUCTION Mycobacterium tuberculosis, the causative

TB and approximately 2 million people died

agent of tuberculosis (TB), is one of the

from it, making this disease the second

world’s most devastating human pathogens.

leading cause of infectious disease mortality

In 2004, 49 million people developed active

worldwide [1]. TB is the leading cause of 1513

IJBPAS, July, 2013, 2(7)

Sulochana MB et al

Research Article

death in HIV-infected individuals. Infection

external stimulus. Kinases are attractive as

with HIV increases the risk of TB and also

drug targets due to the range of crucial

increases the risk of reactivating latent disease

cellular processes in which they are involved.

to over 20 times that in HIV-negative people

The M. tuberculosis (STPKs) are attractive

as immunosuppression worsens [2, 3].

targets

M. tuberculosis has an array of proteins to

importance

ensure its existence during the course of

phosphorylation

infection. In order to thrive and maintain its

tuberculosis is unique within the bacterial

homeostasis,

continuously

world in having a much higher number of

influences its surroundings mainly through

STPKs compared to the more common two-

surface-located sensor proteins. Extracellular

component signalling systems [8-11].

signals are communicated through the sensors

Protein phosphorylation mediated by receptor

to the cytosol leading to the appropriate cell

Ser/Thr protein kinases (STPKs) is widely

responses. Apparently, a large number of

used to transduce extracellular signals into

pathogens employ reversible phosphorylation

intracellular

of proteins by kinases and phosphatases as a

phosphorylation prompts numerous, broad

way

from

effects--including changes in transcription,

extracellular milieu which helps in their

metabolic flux, cell growth, cell division,

survival and pathogenicity [4-7]. Kinases

protein

carry out the phosphorylation by transferring

defense/pathogenesis [12, 13] each requiring

the phosphate moiety on target proteins and

precise regulation of kinases for accurate

phosphatases convert them back to the

signal transduction. PknB, a member of the

unphosphorylated

by

newly

by

serine/threonine

of

the

pathogen

transmitting

dephosphorylating

the

signals

state, the

either

substrate

or

partly

because of

of

the

inferred

serine/threonine

in M. tuberculosis:

responses.

localization,

described kinase

M.

Ser/Thr

and

immune

eukaryotic-like family

from M.

regulating the activity of kinases.

tuberculosis [14, 8]. M. tuberculosis encodes

The coordinated regulation of Ser/Thr protein

in its genome 11 putative serine/threonine

kinases (STPKs) and phosphatases is essential

kinases In contrast, E. coli and other bacteria

for maintaining the appropriate equilibrium of

whose

protein

Membrane

sequenced thus far do not contain this family

associated kinases and phosphatases are

of proteins. In M. tuberculosis, STPKs have

phosphorylation.

genomes

have

been

completely

known or hypothesized to be regulated by 1514 IJBPAS, July, 2013, 2(7)

Sulochana MB et al

Research Article

been estimated to phosphorylate several

cell division came from the data showing that

hundred proteins [15].

over-expression

While two of the 11 Mtb STPKs are soluble

in E. coli results in elongated cells [23] The

kinases, nine are predicted transmembrane

PknA-overexpressing

receptors with an N-terminal “eukaryotic-

broad, and in some cases branched bacilli,

like” kinase domain (KD) linked through a

with what appear to be incomplete septations,

single transmembrane helix to an extracellular

suggesting defects in cell wall synthesis

sensor

and/or

domain

[9].

the Mtb transmembrane

Orthologs receptor

of

Protein

cell

of M.

tuberculosis PknA

strains

division.

form

The

long,

pknB-over

expressing strain forms widened, bulging

Kinase B (PknB) are the most widely

cells

distributed

suggesting effects on cell wall synthesis or

STPKs

in

the

prokaryotic

of

non

kingdom. PknB is essential for Mtb growth

cell division.

[16, 17] and it phosphorylates diverse

Primary

substrates, including proteins involved in

uniform

again

prediction

and

physicochemical

characterization

were

peptidoglycan synthesis [18], cell division

performed

computing

[19], the stress response [12], transcription

isoelectric point (pI), molecular weight, total

[13], metabolic control [20], and other STPKs

number of positive and negative residues,

[21]. A key challenge is to understand how

extinction

bacterial receptor STPKs such as PknB

aliphatic index and grand average hydropathy

respond to extracellular signals.

(GRAVY). The amino acid sequence provides

The role of STPKs in post-translational

most

modification and their further impact on

determining and characterizing the molecule’s

regulating morphological changes associated

function

with cell division have become important to

properties.

use them as targets of pathogenesis. Changes

Homology modeling combines and requires

in morphology and the observation that the

the techniques from two sets of biocomputing

kinases

during

toolkits — sequence analysis and molecular

exponential growth than in stationary phase

modeling. We need at least a pair of

suggest a role for the kinases in regulating

sequences

active growth and shape determination [22].

Multiple sequence alignments make the

The first evidence that PknA may regulate

technique even more powerful. One member

are

highly

expressed

of

structure

diameter,

by

coefficient,

the

and

to

instability

information

physical

do

theoretical

index,

required

and

homology

for

chemical

modeling.

1515 IJBPAS, July, 2013, 2(7)

Sulochana MB et al

Research Article

of the dataset will be modeled, the others are

MATERIALS AND METHODS

reference

Primary Sequence Analysis of PknA

sequences

that

have

three-

dimensional coordinate data associated with

The sequence of Mtb PknA of accession

them upon which to base the model. Proteins

P65726

of the same function generally have similar

(http://www.uniprot.org/), a protein database,

structure and this is the base for homology

in FASTA format.

modeling. Homology modeling combines

The primary structure, the basic physico-

sequence analysis and molecular modeling to

chemical properties, of PknA was analysed

predict three dimensional structure. We have

using ProtParam

to choose a homologue of our query protein

(http://web.expasy.org/protparam/ (Gasteiger

that has not had its structure yet solved and

E).

use the Swiss Model WWW resource to

ExPASy server.

model the molecule. The theoretical structure

was

retrived

ProtParam is

from

available

UniProt

through the

Secondary Structure Prediction

is then visualized with Swiss-PDBViewer to

GORIV (http://npsapbil.ibcp.fr/cgibin/npsam)

gain insight into the way in which its structure

and

relates

bin/npsa_automat.pl?page=/NPSA/npsa_sop

to

its

function.

An

automated

SOPMA

(http://npsa-pbil.ibcp.fr/cgi-

homology modeling tool is available on

ma.html) was used to predict the secondary

Bairoch’s ExPASy server in Switzerland

structure of PknA. GOR method is based on

supported by Glaxo Smith Kline it is called

information

SwissModel

prediction method (SOPM) [26] has been

(http://www.expasy.org/swissmod/SWISS-

developed to improve the success rate in the

MODEL.html [24]. This has dramatically

prediction of the secondary structure of

changed the homology modeling process. It is

proteins.

a relatively painless way to get a theoretical

theory

[25]

self-optimized

Tertiary Structure Prediction

model of a protein structure. It won’t always

The modeling of the three dimensional

generate

your

structure of the protein was performed by

sequence, depending on how similar the

Swissmodel homology modeling programs

closest sequence with an experimentally

[27]. The SWISSMODEL depended on the

solved structure is to it; however, it is a very

quality of the sequence alignment by BLAST

reasonable first approach and will often lead

and template structure. Structural analysis

to remarkably accurate representation.

was performed and figures representations

a

homology

model

for

1516 IJBPAS, July, 2013, 2(7)

Sulochana MB et al

Research Article

were generated with Swiss PDB Viewer [24].

MSPRVGVTLSGRYRLQRLIATGGMGQV

Tertiary

using

WEAVDNRLGRRVAVKVLKSEFSSDPEFI

homology modeling by taking template PDB-

ERFRAEARTTAMLNHPGIASVHDYGESQ

3f69 and modelled protein energy were

MNGEGRTAYLVMELVNGEPLNSVLKRT

minimized. Validation of the tertiary structure

GRLSLRHALDMLEQTGRALQIAHAAGL

of PknA protein was done by PROCHECK.

VHRDVKPGNILITPTGQVKITDFGIAKAV

RESULTS AND DISCUSSION

DAAPVTQTGMVMGTAQYIAPEQALGHD

Primary Sequence Analysis of PknA

ASPASDVYSLGVVGYEAVSGKRPFAGD

The sequence ofPknA, as given below,

GALTVAMKHIKEPPPPLPPDLPPNVRELI

contains 431 AA with molecular weight of

EITLVKNPAMRYRSGGPFADAVAAVRA

45597 Da.

GRRPPRPSQTPPPGRAAPAAIPSGTTARV

structure

was

predicted

>sp|P65726|PKNA_MYCTU serine/threonine-protein

kinase

Probable

AANSAGRTAASRRSRPATGGHRPPRRTF

PknA

SSGQRALLWAAGVLGALAIIIAVLLVIKA

OS=Mycobacterium tuberculosis GN=PknA

PGDNSPQQAPTPTVTTTGNPPASNTGGT

(Table 1).

DASPRLNWTERGETRHSGLQSWVVPPTP

FASTA Sequence of PknA

HSRASLARYEIAQ

Table 1: Physicochemical Properties of PknA Shown by ProtParam No. of amino acids 431 Molecular weight 45597Da Total number of negatively 32 charged residues (Asp + Glu) Total number of positively charged 50 residues (Arg + Lys) Total no. of atoms 2470 Theoretical pI 10.63 Estimated half-life 30 hours Instability index 44.53 Aliphatic index 81.35 Grand average of hydropathicity -0.270 (GRAVY) NOTE: Ext. coefficient 33920; Abs 0.1% (=1 g/l) 0.744

The extinction coefficient indicates how

From the molar extinction coefficient of

much light a protein absorbs at a certain

tyrosine, tryptophan and cystine (cysteine

wavelength. It is useful to have an estimation

does not absorb appreciably at wavelengths

of this coefficient for following a protein

>260 nm, while cystine does) at a given

which a spectrophotometer when purifying it.

wavelength, the extinction coefficient of the 1517

IJBPAS, July, 2013, 2(7)

Sulochana MB et al

Research Article

native protein in water can be computed. The

regarded as a positive factor for the increase

half-life is a prediction of the time it takes for

of thermostability of globular proteins. The

half of the amount of protein in a cell to

GRAVY value for a peptide or protein is

disappear after its synthesis in the cell. The

calculated as the sum of hydropathy values of

instability index provides an estimate of the

all the amino acids, divided by the number of

stability of your protein in a test tube. A

residues in the sequence.

protein whose instability index is smaller than

Secondary Structure Prediction

40 is predicted as stable, a value above 40

The program gives two outputs, one giving

predicts that the protein may be unstable. The

the sequence and the predicted secondary

instability index of our protein is 44.53.So the

structure in rows, H=helix, E=extended or

protein is unstable. The aliphatic index of a

beta strand and C=coil; the second gives the

protein is defined as the relative volume

probability

occupied by aliphatic side chains (alanine,

structure at each amino acid position.

values

for

each

secondary

valine, isoleucine, and leucine). It may be

Prediction Result of GOR4 (Figure 1 & 2) 10 20 30 40 50 60 70 | | | | | | | MSPRVGVTLSGRYRLQRLIATGGMGQVWEAVDNRLGRRVAVKVLKSEFSSDPEFIERFRAEARTTAMLNH cccceeeeeccchhhheeeeccccchhhhhhhcccccceeeeeeccccccchhhhhhhhhhhhhhhhhcc PGIASVHDYGESQMNGEGRTAYLVMELVNGEPLNSVLKRTGRLSLRHALDMLEQTGRALQIAHAAGLVHR ccceeeccccccccccccchhhhhhhhccccccchhhhhcchhhhhhhhhhhhhhhhhhhhhhhhhhhhc DVKPGNILITPTGQVKITDFGIAKAVDAAPVTQTGMVMGTAQYIAPEQALGHDASPASDVYSLGVVGYEA cccccceeeecccceeeehhhhhhhhcccceeccceecchhhccchhhhcccccccccceeeeceeeeec VSGKRPFAGDGALTVAMKHIKEPPPPLPPDLPPNVRELIEITLVKNPAMRYRSGGPFADAVAAVRAGRRP ccccccccccchhhhhhhhcccccccccccccccchhhhhhhhcccccceecccccchhhhhhhhccccc PRPSQTPPPGRAAPAAIPSGTTARVAANSAGRTAASRRSRPATGGHRPPRRTFSSGQRALLWAAGVLGAL ccccccccccccccccccccchhhhhhhhccchhhhhccccccccccccccccccchhhhhhhhhhhhhh AIIIAVLLVIKAPGDNSPQQAPTPTVTTTGNPPASNTGGTDASPRLNWTERGETRHSGLQSWVVPPTPHS hhhhhhhhhhcccccccccccccceeeeccccccccccccccccccccccccceeecccccceecccccc RASLARYEIAQ hhhhhheeeec Sequence length: 431 GOR4: Alpha helix (Hh) : 151 is 35.03% 310 helix (Gg) : 0 is 0.00% Pi helix (Ii) : 0 is 0.00% Beta bridge (Bb) : 0 is 0.00% Extended strand (Ee) : 54 is 12.53% Beta turn (Tt) : 0 is 0.00% Bend region (Ss) : 0 is 0.00% Random coil (Cc) : 226 is 52.44% Ambigous states (?) : 0 is 0.00% Other states : 0 is 0.00%

1518 IJBPAS, July, 2013, 2(7)

Sulochana MB et al

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Figure 1: Prediction Result of GOR4

Figure 2: Prediction Result of GOR4

Prediction Result of SOPMA (Figure 3 & 4) 10 20 30 40 50 60 70 | | | | | | | MSPRVGVTLSGRYRLQRLIATGGMGQVWEAVDNRLGRRVAVKVLKSEFSSDPEFIERFRAEARTTAMLNH cccccceehtcceeeeeeecttcchhhhhhhhhhhhhhhhhhhhhhhcccchhhhhhhhhhhhhhhhccc PGIASVHDYGESQMNGEGRTAYLVMELVNGEPLNSVLKRTGRLSLRHALDMLEQTGRALQIAHAAGLVHR tteeeeeeccccccccccccheeeeehcttcchhhhhhttccccchhhhhhhhhhhhhhhhhhhtteeec DVKPGNILITPTGQVKITDFGIAKAVDAAPVTQTGMVMGTAQYIAPEQALGHDASPASDVYSLGVVGYEA cccttceeeettcceeeeehccchhhhhccccctteeeccccccccccccccccccccceeehhhhhhhh VSGKRPFAGDGALTVAMKHIKEPPPPLPPDLPPNVRELIEITLVKNPAMRYRSGGPFADAVAAVRAGRRP httccccccccchhhhhhhccccccccccccchhhhhhhhhhhhttttcccccccchhhhhhhhhttccc PRPSQTPPPGRAAPAAIPSGTTARVAANSAGRTAASRRSRPATGGHRPPRRTFSSGQRALLWAAGVLGAL ccccccccccccccccccccccceeccccccccccccccccccccccccccccctthhhhhhhhhhhhhh AIIIAVLLVIKAPGDNSPQQAPTPTVTTTGNPPASNTGGTDASPRLNWTERGETRHSGLQSWVVPPTPHS hhhhhheeeeccccccccccccccceeeccccccccccccccccceeeccccccccttccceeeeccccc RASLARYEIAQ chhhhhhhhhh Sequence length: 431 SOPMA: Alpha helix (Hh): 140 is 32.48% 310 helix (Gg) : 0 is 0.00% Pi helix (Ii) : 0 is 0.00% Beta bridge (Bb) : 0 is 0.00% Extended strand (Ee) : 54 is 12.53% Beta turn (Tt) : 29 is 6.73% Bend region (Ss) : 0 is 0.00% Random coil (Cc) : 208 is 48.26% Ambigous states (?) : 0 is 0.00% Other states : 0 is 0.00%

1519 IJBPAS, July, 2013, 2(7)

Sulochana MB et al

Research Article

Figure 3: Prediction Result of SOPMA

Figure 4: Prediction Result of SOPMA Parameters: Window width: 17; Similarity threshold:8;Number of states :4

The results revealed that random coil has the highest percentage and beta strand has the lowest percentage.

Tertiary structure prediction

structure of the query sequence, and on the

In protein structure prediction, homology

production of an alignment that maps residues

modeling,

also

known

in the query sequence to residues in the

modeling,

is

class

a

as of

comparative methods

for

template sequence. The sequence alignment

constructing an atomic-resolution model of a

and template structure are then used to

query or target protein from its amino acid

produce a structural model of the target. Since

sequence. In this study approximately all

protein structures are more conserved than

homology modeling techniques rely on the

protein

identification of one or more known protein

Sequence

(template) structures likely to resemble the

significant structural similarity.

sequences,

detectable

similarity

usually

levels

of

involve

1520 IJBPAS, July, 2013, 2(7)

Sulochana MB et al

Research Article

Modeled Structure of PknA (Figure 5)

Figure 5: Modeled Structure of PknA

Ramachandran

Plot

showing

Model

performed and figures representations were

validation (Figure 6)

generated with Swiss PDB Viewer [24]. In

Three dimensional structure of PknA was

the modeled structure 87.9% residues are in

successfully modeled using SWISS- MODEL.

most favored regions, 10.3%in additional

The predicted structure was further validated

regions and 1.8% in generously allowed

using prochek and checked for its reliability

regions. But no residues are in the disallowed

based on the Ramachandran plot, by fulfilling

regions.

the required rules. Structural analysis was

1521 IJBPAS, July, 2013, 2(7)

Sulochana MB et al

Research Article

Figure 6: Ramachandran Plot showing Model validation

CONCLUSION

presence of four different STPKs. STPKs also

Protein phosphorylation is a key mechanism

play a role in the virulence of many bacterial

by

pathogens

which

environmental

signals

are

such

as

streptococci,

transmitted to control protein activities in

Staphylococcus

aureus,

Mycobacterium

both

tuberculosis,

Yersinia

spp.,

eukaryotic

and

prokaryotic

cells.

and

P.

Prokaryotic STPKs regulate various cellular

aeruginosa., Bacterial pathogens containing

functions, such as developmental processes,

eSTKs

primary and secondary metabolism, stress

strategies to manipulate host

responses and biofilm formation. In-silico

pathways that aim to either weaken an

analysis

of

effective immune response, or to create a

Corynebacterium glutamicum predicted the

suitable environment in which the invading

of

the

genome

sequence

have

developed

a

diversity

of

signalling

1522 IJBPAS, July, 2013, 2(7)

Sulochana MB et al

Research Article

pathogen(s) can survive, propagate and

tuberculosis is

flourish. The modelled structure of pknA will

virulence

be helpful for the structural interaction study

1993, 730-732.

an

indispensable

determinant, Nature, 361,

.the interaction will help us to understand the

[6] Juris SJ, Rudolph AE, Huddler D,

interface residues between the host and the

Orth K and Dixon JE, A distinctive

pathogen, protein which will be helpful for

role for the Yersinia protein kinase:

the drug development against M. tuberculosis.

actin binding, kinase activation, and

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