1.
INTRODUCTION
Infectious
diseases are caused by pathogenic microorganisms, such as bacteria, viruses,
parasites or fungi; the diseases can be spread, directly or indirectly, from
one person to another. Infectious diseases kill more people worldwide than any
other single cause. Infectious diseases are caused by germs. Germs are tiny
living things that are found everywhere - in air, soil and water. You can get
infected by touching, eating, drinking or breathing something that contains a
germ. Germs can also spread through animal and insect bites, kissing and sexual
contact.
The most common infectious diseases
throughout the world today are difficult to determine, especially because so
many of these diseases are endemic to developing countries, where many people
do not have access to modern medical care. Approximately half of all deaths
caused by infectious diseases each year can be attributed to just three
diseases: tuberculosis, malaria, and AIDS. Together, these diseases cause over
300 million illnesses and more than 5 million deaths each year. Tuberculosis
causes nearly 2 million deaths every year, and WHO estimates that nearly 1
billion people will be infected between 2000 and 2020 if more effective preventive
procedures are not adopted. The TB bacteria are most often found in the lungs,
where they can cause chest pain and a bad cough that brings up bloody phlegm.
Other symptoms include fatigue, weight loss, appetite loss, chills, fever, and
night sweats. Tuberculosis is one of the most prevalent infections of human
beings. Mycobacterium tuberculosis (MTB) is a pathogenic bacterium in
the genus Mycobacterium and the causative agent of most cases of
tuberculosis (TB).
It was first discovered in 1882 by Robert Koch, M. tuberculosis has an
unusual, waxy coating on its cell surface (primarily mycolic acid), which makes
the cells impervious to Gram staining The physiology of M. tuberculosis is
highly aerobic and requires high levels of oxygen . It does not retain any
bacteriological stain due to high lipid content in its wall, and thus is
neither Gram-positive nor Gram-negative; hence Ziehl-Neelsen staining or
acid-fast staining, is used. M. tuberculosis divides every 15-20 hours,
which is extremely slow compared to other bacteria.
In
1995, an outbreak occurred in a rural area in the United States considered to
be at low risk for TB. The strain, designated CDC1551 or CSU 93 by the Centers
for Disease Control and Prevention (CDC) attracted special attention because of
an unusually high rate of transmission as evaluated by skin test conversion.
Infection with CDC1551 was not associated with an obvious increase in active TB
cases, nor did any patients have extra pulmonary disease. M. tuberculosis CDC1551
is not unusually virulent but rather more immunogenic and that it induces a
rapid and vigorous cytokine response.
As
per the WHO Global TB Report 2011, there were an estimated 8.8 million incident
cases of TB globally in 2010. Though India is the second-most populous country
in the world, India has more new TB cases annually than any other country
contributing to a fifth of the global burden of TB. It is estimated that about
40% of Indian population is infected with TB bacillus (http://www.tbcindia.nic.in).
Even though varieties of anti-tuberculosis drugs and effective
chemotherapy and Bacille-Calmette –Guerin (BCG) vaccine are available,
tuberculosis is highly infectious and treatment options are often limited, due
to the emergence of multi-drug (MDR) and extreme drug resistant (XDR) TB
strains. This has prompted an urgent need of new drugs and the identification
of new drug targets.
Bacterial
cell wall is made up of peptidoglycon; it is an important target for the
development of anti- infectious agents. Mycobacterium
tuberculosis genomic information was used to identify and validate targets
as the basis for the development of new anti-tuberculosis agents (Abraham,
1987). Antimicrobial agents that target the bacterial cell wall or cell
membrane have been used effectively for the past 70 years (Bush and Jacoby, 2010). Antimicrobial agents that function by
interacting with bacterial cell walls or cell membranes will serve as a potent drugs
for the treatment of M. tuberculosis.
GlmU bifunctional N-acetylglucosamine-1-phosphate uridyltransferase /glucosamine-1-phosphate
acetyltransferase. GlmU is a trimeric bifunctional enzyme involved in cell wall
synthesis, that catalyzes the last two sequential reactions in the de novo
biosynthetic pathway for UDP-GlcNAc. The X-ray crystal structure of Escherichia
coli GlmU in complex with UDP-GlcNAc and CoA has been determined to 2.1 Ao
resolutions and reveals a two-domain architecture that is responsible for these
two reactions. Present study focused on the Mycobacterial proteome analysis
using in silico tools suggested that GlmU to be a potential drug target (San
Diego,1995). The gene glmU has been identified as essential for
optimal growth of M.
tuberculosis (Sassetti et al.,, 2003) and is not present in humans; hence, it is of interest
as a drug target.
Our
research on Mtb biology aims at elucidating basic capacities of this pathogen
during different stages of infection. In particular, we are interested in the
metabolic and regulatory networks that are operative during exponential growth,
dormancy and resuscitation. Our research analyzes the mechanisms that control
the “shift down” of Mtb to dormancy and the resuscitation of the quiescent
pathogen to resume replication and active metabolism. A few organisms remain
dormant and therefore phenotypic ally drug-resistant, explaining the long
treatment time required to cure active TB. Probably these dormant Mtb are also
more resistant to immune effector mechanisms. Current TB drugs exclusively act
on active Mtb, whereas dormant Mtb is highly resistant to drug treatment.
Whilst active Mtb is being eradicated, dormant Mtb “wakes up” and becomes
active. In the active stage, Mtb can induce major damage but is also
susceptible to drug treatment. In contrast, while contained, dormant Mtb does
not cause significant damage but resists drug treatment. A new TB drug that
acts on dormant Mtb could therefore shorten treatment time significantly. A
given Mtb is therefore drug-resistant in the dormant stage and susceptible
during growth. In analogy, it is also likely that dormant Mtb is more resistant
to host defence than active Mtb(Stefan H. E. Kaufmann).
We
have joined two multidisciplinary consortia, which aim at understanding Mtb
from a systems biology view, and we are confident that these collaborations
will accelerate the elucidation of Mtb biology through a combination of biomics
profiling, high-throughput promoter mapping and computational modeling
(http://www.systeMtb.org/&http://www.broadinstitute.org/annotation/tbsysbio/home.html).
This work includes analysis of the regulatory networks of transcription
factors, cell cycle control as well as metabolic and proteomic pathways
operative during Mtb infection. As an added value, we hope that this research
will reveal an Achilles heal of Mtb, which can be exploited for the design of
new intervention measures for TB. Finally, we are confident that our increasing
experience with systems biology of Mtb in combination with our immunology
expertise will provide a basis for an integrative infection biology platform of
TB.
Different subtypes of Mycobacterium
tuberculosis (MTB) may induce diverse severe human infections, and some of
their symptoms are similar to other pathogens (Liu et al., Journal of Clinical Bioinformatics2014). Non tuberculosis
mycobacteria (NTM) are a diverse group of organisms that are ubiquitous in both
natural and manmade environments. Since the genomes of different mycobacteria
have been sequenced, it is now possible for us to generate a novel DNA bar
coding technology for genotyping of mycobacteria. Clinically, mycobacteria were
traditionally characterized based on acid-fastness, smear and culture
morphology, growth rate, pigment production and various biochemical tests.
These parameters provide a useful tool to aid MTB diagnosis. However, a higher
degree of differentiation, including the ability to distinguish between species
and subspecies has become a requirement in both epidemiological and clinical
settings. Thus, molecular based techniques could allow faster species
identification and phylogenetic analyses. There are numerous published methods
for mycobacteria genotyping, including insertion sequence (IS) 6110 restriction
fragment length polymorphism (RFLP) analysis, PCR-based techniques, such as
mycobacterial interspersed repetitive unit-variable number of tandem repeat
(MIRU-VNTR) analysis, and so on.
In
this study, we identified nucleotide fragments that contain both the genome
barcode information and inter-specific differences. We then utilized these
fragments to perform genomic typing of mycobacteria. This study de-scribes a
novel tool that can be used to analyze different genomes leading to identification
of subtypes of mycobacteria and can be implemented for future clinical use or
epidemiological studies. Mycobacterium tuberculosis, a causative agent of
tuberculosis infected one-third of population globally. The long treatment time
and high use of antibiotics leads to the emergence of resistance strains of Mtb
like multiple drug-resistant (MDR), extensive drug-resistant (XDR). Emerging
resistance and HIV/Mtb co-infection imposes burden to scientific as well as
pharmaceutical companies. Therefore, efforts are now focusing on searching new
drug-target as well as new chemical entity.
Once nearly vanquished by antibiotics, at least in the developed world,
tuberculosis (TB) resurged in the late 1980’s and now kills more than 2 million
people a year – second only to AIDS among infectious diseases. Thirty mil-lion
people most of them living in Africa are at risk of dying from TB in the next
10 years. Mycobacterium tuberculosis, the causative agent of TB, is a
slow-growing bacillus that is primarily transmitted via the respiratory route,
mostly causing pulmonary TB. Estimates are that one third of the world’s
population is infected with this tenacious and re-mark ably successful
pathogen, but infection usually does not lead to the active disease.
Reactivation of this latent infection can be caused by immunodeficiency, HIV
infection, use of corticosteroids, aging, and alcohol and drug abuse. In HIV
infected patients, TB has become the main cause of death. Next to
HIV-infection, demographic factors, socio-economic trends and the neglected TB
control in many countries contributed to this vast epidemic (S. Van Calenbergh)
Antibiotic resistance has become a major
hurdle to overcome bacterial diseases and thus there is always a need to find
new drug targets or inhibitors or both. At present very few drugs are available
in the market for treatment of M. tuberculosis infection as evolution of drug-resistant strains
have resulted in little efficacy and some of them have shown undesired
side-effects in host. Studies suggest that the
prevalence of Multi Drug Resistant tuberculosis (MDR-TB) ranged from 6.7% for
three drugs to 34% for four drugs and has caused an annual loss of around $4 -
$5 billion. Keeping in mind the rapidly changing pathogenesis of this
lethal micro-organism, identification of novel inhibitors for recently
discovered targets has become pressing need of the hour. GlmU is one such
target which is essential for the survival of the pathogen. Recent studies on the Mycobacterial proteome using in-silico analysis
suggested GlmU to be a potential drug target. This
protein is a bi-functional enzyme that catalyzes a two steps reaction.
Initially, catalytic conversion of glucosamine-1-phosphate to
N-acetyl-glucosamine-1-phosphate takes place at the C-terminal domain followed
by conversion of N-acetyl-glucosamine-1-phosphate to UDP-GluNAc at the
N-terminal domain. Though the
second step is present in prokaryotes as well as in humans, the first step is
present only in prokaryotes. The absence of the first
step in human makes it suitable for designing non-toxic inhibitors. The three
dimensional structure of the GlmU enzyme has been reported from Escherichia coli, Mycobacterium
tuberculosis, Streptococcus pneumoniae, Haemophilus influenzae, Yersinia pestis in apo
and holo-forms. These structures have missing coordinates for the
C-terminal intrinsically disordered regions.
The Mycobacterium
tuberculosis is a serious hazard to human health of the three diseases.
World Health Organization (World Health Organization, WHO) report shows that
one-third of the world's population infected with Mycobacterium tuberculosis,
the world every 20 seconds a person died of tuberculosis. Because MDR (multi
drug resistant, MDR) and even extensively drug-resistant (extensive drug
resistant, XDR) Mycobacterium
tuberculosis appears that many first-line anti-TB drugs and even some
second-line treatment of tuberculosis cannot be effective, so the search for
new anti-TB drugs imminent. Cell wall of Mycobacterium
tuberculosis in their survival and proliferation plays an important role in
its core structure from the peptidoglycan, arabinogalactan, mycolic acid three
components connected components. Where in the mycolic acid, arabinose and
galactose polymer bridging disaccharides (L-rhamnose-DN-acetylglucosamine)
polysaccharide covalently linked to the peptide molecule. UDP-N-acetyl
glucosamine is N-acetyl glucosamine glycosyl donor, the UDP-N-acetylglucosamine
biosynthesis, glmU GlmU genes encoding proteins glucamine 1 - phosphate acetyl
transferase activity and N-acetylglucosamine 1 - phosphate transferase activity
of uridine, the last two steps catalyzed reactions. Zhang Wenli this laboratory
by gene knockout method proved glmU
gene necessary for growth of
mycobacterial genes, therefore, GlmU can be used as a good target for anti-TB
drugs. This experiment has been established for the determination GlmU
enzymatic activity, and as a screening anti GlmU inhibitors molecular model.
Therefore, we need to build up-regulated expression GlmU cell model to further
filter anti GlmU inhibitor. In order to further study the decrease in GlmU
expression of mycobacterial cell wall structure, we need to build GlmU protein
can decrease the system to decreased protein levels of GlmU and prepared in
this experiment using a polyclonal antibody anti-GlmU glmU Expression of
antisense RNA expression system GlmU level testing. This thesis aims to: (a) in
the host strain mc2155 highly expressed Tb GlmU, with detection GlmU enzyme
activity approach to identify Tb GlmU in smegmatis The high expression; (2)
optimization by tetracycline-induced glmU antisense RNA expression system,
prepared in our laboratory using polyclonal antibodies GlmU GlmU protein level;
(3) with a scanning electron microscope GlmU decreased expression of
Mycobacterium smegmatis morphological changes. The results obtained in this
study are as follows: 1. Expression vector construction of pVV2-Tb glmU Nde I
and BamH I with digested plasmid pET16b-Tb glmU, recovery and purification glmU
gene, connect it to the expression vector pVV2 Nde I and BamH I sites, build
pVV2-Tb glmU expression plasmid. GlmU proteins with N-terminal histidine tag on
the plasmid to form fusion proteins. 2. Tb GlmU proteins in M. smegmatis mc2155.
The high expression and identification will pVV2-Tb glmU mc2155 plasmids were
transformed into competent cells to express GlmU proteins. Broken by ultrasonic
method of carrying pVV2-Tb glmU mc2155 bacteria, supernatant and pellet
fraction by SDS-PAGE and Western blotting analysis showed that Tb GlmU soluble
protein expression in mc2155. And use its enzymatic activity detection method
to identify GlmU in Mycobacterium smegmatis high expression. 3 antisense RNA
expression vector pMind-Sm GlmU (360bp)-AS build with Nde I and BamH I,
pMD18-Sm glmU (360bp) plasmid glmU recovery and purification of gene fragments,
connect it to pMind antisense expression vector Nde I and BamH I sites, build
pMind-Sm glmU (360bp)-AS antisense expression plasmid. 4. GlmU inducible
expression of antisense RNA and protein detection GlmU respectively 0ng/ml,
5ng/ml, 20 ng / ml of tetracycline-inducible carrying pMind-Sm glmU (360bp)-AS
expression vector for expression of antisense RNA strain mc2155 to draw
bacterial growth curve, and prepared in our laboratory using polyclonal
antibodies GlmU GlmU protein levels. The results showed that 20 ng / ml
tetracycline for mc2155 / pMind-Sm glmU (360bp)-AS significantly inhibited the
growth, Western blotting results showed that after 20ng/ml tetracycline-induced
mc2155 / pMind-Sm glmU (360bp)-AS GlmU protein expression was significantly
reduced, proved glmU antisense RNA reduced expression of Mycobacterium
smegmatis GlmU level. 5 tetracycline induced by 20ng/ml mc2155 / pMind-Sm glmU
(360bp)-AS strain changes in cell morphology will by 20ng/ml
tetracycline-induced mc2155 / pMind-Sm glmU (360bp)-AS strain cells with
scanning electron microscopy the cell morphology. The results show the growth
after 48 hours, cell surface projections apparent even swelling, cell contour
not clear, it seems that stick together. Description of the experiment
optimized antisense RNA expression system has played a role in the expression
of down GlmU, further illustrates the protein on the cell wall integrity GlmU
influential. Conclusions: In Mycobacterium smegmatis high expression of soluble
Tb GlmU protein expression constructs can increase GlmU strains for screening
inhibitors provide Tb GlmU cell model. 2 glmU optimized antisense RNA
expression system, the expression construct can down GlmU strains studied GlmU
reduce the expression of Mycobacterium
smegmatis cell wall structure.
Fig.1. M. tuberculosis
Objectives:
1. Identification potential drug target in Mycobacterium tuberculosis.
2.
Insilico analysis of identified drug target.
3.
Screening and Identification of lead compound for target protein.
4.
Insilico prediction of ADMET properties of drug like compounds.
5.
Docking of potential lead compound with target protein.
2.
REVIEW OF LITERATURE
Mycobacterium tuberculosis is a pathogenic bacterial species in the family Mycobacteriaceae and the
causative agent of most cases of tuberculosis. First discovered in 1882 by
Robert Koch, M. tuberculosis has an unusual, waxy coatingonits cell surface
(primarily due to the presence of mycolic acid), which makes the cells
impervious to Gram staining. The Ziehl-Neelsen stain, or acid-faststain, is
used instead. The physiology of M.
tuberculosis is highly aerobic and requires high levels of oxygen.
Primarily a pathogen of the mammalian respiratory system, it infects the lungs.
The most frequently used diagnostic methods for tuberculosis are the tuberculin
skin test, acid-fast stain, and chest radiographs. The M. tuberculosis genome
was sequenced in 1998.
2.1
Evolution of drug resistance in tuberculosis
The development of resistance to anti-TB drugs
began shortly after the initial introduction of antibacterial drugs for the
treatment of TB. Already, during the first randomized clinical trial (RCT) in
the 1940s, resistance to streptomycin was detected in a large majority of
patients treated with that drug. The spread of drug-resistant strains was soon
recognized and, despite the introduction of combination drug regimens
throughout the world many years ago, the presence of drug resistance has been
documented with increasing frequency from an ever wider geographic area.
Drug-susceptible TB is treated for 6 months with a combination of four drugs –
rifampicin, isoniazid, ethambutol and pyrazinamide. However, most treatment
courses for MDR-TB last 20 months or longer, and require daily administration
of drugs that are less effective and have more side-effects than those used to
treat drug-susceptible forms of TB. Extensively drug-resistant TB is the most
resistant variant.
2.2 Intrinsic and acquired drug resistance
Intrinsic resistance refers to the innate ability of a bacterium
to resist the activity of a particular antimicrobial agent through its inherent
structural or functional characteristics. Intrinsic drug resistance in M.
tuberculosis has been attributed to its unique cell wall properties,
including the presence of mycolic acids, which are high-molecular-weight Alpha-alkyl, beta-hydroxy fatty acids covalently attached to
arabinogalactan, and which constitute a very hydrophobic barrier responsible
for resistance to certain antibiotics (Bishai et al.,, 2004). In addition, M. tuberculosis possesses beta-lactamase enzymes, which confer intrinsic
resistance to beta-lactam
antibiotics, while efflux mechanisms appear to play an important role in
resistance to antibiotics such as tetracycline and the aminoglycosides.
2.3 Acquired drug resistance
Occurs when a microorganism obtains the
ability to resist the activity of a particular antimicrobial agent to which it
was previously susceptible. Acquired drug resistance in M. tuberculosis is
caused mainly by spontaneous mutations in chromosomal genes, and the selective
growth of such drug-resistant mutants may be promoted during suboptimal drug
therapy (Vareldzis et al., 1993). The
rate of genetic mutations leading to resistance varies somewhat among
anti-tuberculosis drugs, from a frequency of ~10-5-10-6 organisms for isoniazid
to ~10-7-10-8 organisms for rifampin (Karakousis 2009). Since the bacterial
burden typically present in pulmonary cavities does not exceed 1012 bacilli
(Canetti. 1965), combination therapy is highly effective for drug susceptible
disease, and the risk for development of acquired drug resistance is minimized.
2.4
Classification of Mycobacteria
Mycobacteria belongs to
Kingdom -Bacteria
Phylum
-Actinobacteria
Order-
Actinomycetales
Family-
Mycobacteriaceae
Genus - Mycobacterium
Mycobacteria
are classified based on the production of pigments, they can be classified as
scotochromogens (produce yellow pigment in the dark) for example M. scrofulaceum, M. gordonae;
photochromogens (produce an orange pigment in the light) for example M. kansasii, M. marinumand
achromogens (do not produce any pigment) for example M. avium, M. intracellularae and M. ulcerans. The
cultivable members of the genus can be divided into two main groups on the basis
of growth rate, they can be grouped into fast growers, which include M. fortuitum, M. kansasii, M. smegmatisand
slow growers, comprising mostly the pathogenic mycobacteria, including M.tuberculosis, M. bovis and M. leprae.
TUBERCULOSIS CLASSIFICATION
|
Class
|
Type
|
Description
|
|
TB-0
|
- No TB exposure
- Not infected
|
No history of exposure. Negative reaction to tuberculin
skin test.
|
|
TB-1
|
- TB exposure
- No evidence of
infection
|
History of exposure. Negative reaction to tuberculin
test skin test.
|
|
TB-2
|
- TB infection
- No disease
|
Positive reaction to tuberculin skin test. Negative
bacteriologic studies (if done). No clinical or radiographic evidence of TB.
|
|
TB-3
|
- Current TB
disease
|
M. tuberculosis cultured
(if done) or both a positive reaction to tuberculin skin test and clinical
and/or radiographic evidence of current disease.
|
|
TB-4
|
- Previous TB
disease
|
History of episode(s) of TB, abnormal stable radiographic
findings in a person with a positive reaction to the tuberculin skin test,
negative bacteriologic studies (if done) and no clinical or radiographic
evidence of current disease.
|
|
TB-5
|
- TB suspect
|
Diagnosis pending (a patient should not be in this
class for more than 3 months).
|
San Mateo County Health Department Public
Health Reporting Guidelines II.A.5.
Tuberculosis Classification April 2007
2.5
Strain variation
M. tuberculosis is genetically diverse, which results in
significant phenotypic differences between clinical isolates. Different strains
of M. tuberculosis are associated
with different geographic regions. However, phenotypic studies suggest the
strain variation never has implications for the development of new diagnostics
and vaccines. Micro evolutionary variation does affect the relative fitness and
transmission dynamics of antibiotic-resistant strains (Gagneux
S 2009).
Typing of strains
is useful in the investigation of tuberculosis outbreaks, because it gives the
investigator evidence for-or-against transmission from person to person.
Consider the situation where person A has tuberculosis and believes he acquired
it from person B. If the bacteria isolated from each person belong to different
types, then transmission from B to A is definitively disproved; how-ever, if
the bacteria are the same strain, then this supports (but does not definitively
prove)the theory that B infected A.
Until the early 2000s, M. tuberculosis strains were typed by pulsed field gel
electrophoresis (PFGE). This has now been superseded by variable numbers of
tandem re-peats (VNTR), which is technically easier to perform and allows
better discrimination between strains. This method makes use of the presence of
repeated DNA sequences within the M.
tuberculosis genome.
Three generations of VNTR typing for M.tuberculosis are noted. The first
scheme, called exact tandem repeat, used only five loci, but the resolution
afforded by these five loci (Frothingham R, Meeker-O'Connell W A
at al.,1998) was not as good as
PFGE (Mazars E, Lesjean S, Banuls AL et
al.,2001).The second scheme, called mycobacterial interspersed
repetitive unit, had discrimination as good as PFGE. The third generation
(mycobacterial interspersed repetitive unit - 2) added a further nine loci to
bring the total to 24. This provides a degree of resolution greater than PFGE
and is currently the standard for typing M.
tuberculosis (Supply P, Allix C, Lesjean S
et al.,2006).
However, with regard to archaeological remains, additional evidence maybe
required because of possible contamination from related soil bacteria (Müller, Romy; Roberts, Charlotte A et al.,2015).
2.6 TB/HIV co-infection
HIV is responsible for the increase in
active TB cases in sub-Saharan Africa and increases the risk of rapid TB
disease progression.(Corbett E L, Watt C J, Walker N, et al.,2003),(Bates I, Fenton C, Gruber
J, et al.,(2004) Worldwide,
more than 13 million individuals are co-infected with HIV and TB, or about
one-third of the 40 million people currently living with HIV. Approximately 70%
of those co-infected reside in sub-Saharan Africa (WHO report 2005).
The incidence of active TB is more
than eight times higher in HIV-positive than HIV-negative Africans.(WHO
report 2005, Corbett EL, Marston B 2006).
In Africa, the countries with the
largest numbers of coinfected adults are South Africa (2 million) and Nigeria
(0.9 million). Between 30-50% of those infected with HIV will develop active
TB, while up to 75% of those infected with TB are HIV-positive.
(WHO 2004),
(Mukadi YD, Maher D, Harries A 2001).
In many individuals infected with HIV,
development of active TB disease is the first sign of AIDS. Active TB often
occurs at a higher CD4+ lymphocyte count than other HIV-related illnesses. TB
is a potent factor in the progression of HIV disease.(WHO report2005) Both diseases accelerate the progress of the other. Active TB often
decreases the number of CD4+ lymphocytes, increases HIV viral replication and
shortens the lives of HIV-positive persons.( HIV infection
1999).,(Mukadi YD, Maher D, Harries A 2001) The case fatality rate for HIV-related
TB has been estimated to be over 50% in developing countries(Corbett EL,
Watt CJ, et al.2003).
HIV infection impairs cell-mediated
immunity, increasing the risks of TB infection and the reactivation of latent
TB in adults and children.(Harries AD 1998) When TB infection occurs early in the
course of HIV disease, before the immune system has been compromised, its
characteristic features are similar to those in HIV-negative patients. However,
when TB occurs among persons with advanced immune deficiency, the majority of
patients (~70%) present with atypical pulmonary disease and about 30% present
with extra-pulmonary (or disseminated) TB.(Harries AD 1998),(De Cock KM,
Soro B, Coulibaly IM, Lucas SB1992).
Fig.2. M.tuberculosis aerosol transmission and progression to
infectious TB or non-infectious
(latent) disease.
A sizeable pool of latently infected people may relapse into active TB,
years after their first exposure to the bacterium. Latent TB is commonly
activated by immune suppression, as in the case of HIV. In cases of
drug-susceptible (DS)-TB (denoted by an asterisk), 95% of patients recover upon
treatment, whereas 5% relapse. If untreated (denoted by two asterisks), high
mortality results.
2.7
Symptoms of TB Disease
Tuberculosis (TB) is a
disease caused by bacteria that are spread through the air from person to
person. The TB bacteria are put into the air when a person with TB disease of
the lungs or throat coughs, sneezes, speaks, or sings. People nearby may breathe
in these bacteria and become infected. The TB bacteria usually attack the
lungs, but can attack any part of the body such as the kidney, spine, and
brain. If not treated properly, TB disease can be fatal.
TB is NOT spread by
·
Shaking someone's hand
·
Sharing food or drink
·
Touching bed linens or toilet seats
·
Sharing toothbrushes
·
Kissing
Fig.3 Symptoms of TB Disease
2.8
The Difference Between TB Infection and TB Disease
Latent TB Infection
(LTBI) Latent TB infection (LTBI) does not cause a person to feel sick, and
there are no symptoms. Tuberculin skin tests (TSTs) or interferon gamma release
assay (IGRA) blood tests are used to diagnose LTBI. A positive result means
that TB infection is present. Treatment for LTBI is effective in preventing
persons with LTBI from developing TB disease. Nine in ten people with a normal
immune system with LTBI will never develop TB disease. However, one in ten of
those infected will develop TB disease at some time in their lives, though the
risk is lower if they receive preventive therapy. About half of these will develop
TB disease within the first 2 years after they become infected. There is no way
to determine which people with LTBI will later get sick with TB disease.
2.9
TB Disease
Most TB disease
occurs in the lungs, but about 15% occurs in other parts of the body (e.g.,
bone, eye, and brain). General symptoms of pulmonary TB include:
·
Fever
·
Night
sweats
·
Fatigue
·
Unexplained
weight loss
·
Hemoptysis
(bloody sputum)
·
A bad cough that lasts 3 weeks or longer
·
Pain in the chest
·
Coughing up blood or sputum (phlegm from deep
inside the lungs)
·
Weakness or fatigue
·
No appetite
·
Chills
·
Sweating at night
Without treatment, a person with TB disease will get sicker. A person
with untreated pulmonary TB disease of the lungs or larynx will also become
more contagious. Untreated TB can become a life-threatening disease; however,
with effective and complete treatment, TB can be cured.
A
person with a cough lasting 3 or more weeks along with any other symptoms of TB
disease (fever, night sweats, fatigue, unexplained weight loss, hemoptysis
[bloody sputum]) should be evaluated by a healthcare provider as soon as
possible (Frieden T R, Sterling T R, Munsiff S
S,2003) If TB disease is diagnosed or suspected,
treatment will be prescribed.
2.10 Global Tuberculosis (TB)
TB in the
United States reflects the global reality. TB is one of the most common infectious
diseases worldwide. While significant progress has been made toward the
elimination of TB in the United States, this disease remains an urgent public
health problem in many other parts of the world.
·
In
2013, 65% of all TB cases and 90% of multidrug–resistant TB cases in the United
States occurred among people born in other countries.
·
Nearly
50% of these individuals were born in just five countries
Many of CDC’s global TB control
activities are focused in these high-burden and origin countries. Investing in
TB control in high-burden settings reduces TB cases in the United States, costs
less than screening at U.S. entry points, and saves funds that would be spent
treating TB disease in the U.S.
CDC
collaborates with other U.S. Government (USG) agencies, local ministries of
health (MOH), multilateral organizations, and non-governmental organizations
(NGOs) to build strong national TB programs. CDC plays an important role
in finding the most effective ways to implement new tools and approaches in
resource-limited and high-burden settings through clinical and operations
research, technical assistance, program and policy design, demonstration
projects, and program monitoring and evaluation. CDC focuses on
supporting innovative approaches to screening, diagnosing, case-finding, and
curing TB to stop the spread of disease and prevent development of drug
resistance.
2.11 TB is one of the world’s deadliest
diseases
·
One third
of the world’s population are infected with TB.
·
In
2013, 9 million people worldwide became sick with TB disease, most of whom
(80%) live in one of the 22 high burden countries for TB.
·
TB is a
leading killer of people living with HIV (PLHIV).
This is the nineteenth global report on tuberculosis (TB)
published by WHO in a series that started in 1997. It provides a comprehensive
and up-to-date assessment of the TB epidemic and progress in implementing and
financing TB prevention, care and control at global, regional and country
levels using data reported by over 200 countries that account for over 99% of
the world's TB cases. The report is accompanied by a special supplement that
marks the 20th anniversary of the establishment of the Global Project on
Anti-TB Drug Resistance Surveillance. The supplement highlights the latest
status of knowledge about the epidemic of multidrug-resistant TB (MDR-TB) and
the programmatic response.
The three annexes of the report include an
explanation of how to access and use the online global TB database, one-page
profiles for 22 high TB-burden countries and one page regional profiles for
WHO’s six regions.
2.12
Epidemiology of tuberculosis
Globally, there were an estimated 9.27
million cases of tuberculosis in the year 2007. This is an increase from 9.24
million cases in 2006, 8.3 million cases in 2000 and 6.6 million cases in 1990
(WHO report, 2009). Fig. 1.2 illustrates the estimated tuberculosis incidence
rate worldwide. It has been estimated that one-third of the world’s population
is infected with M. tuberculosis and roughly 10% of these individuals will
develop active tuberculosis within their lifetime. With the rise in HIV
infections, tuberculosis has been on the rise and death due to tuberculosis in
HIV-infected people is two-fold higher in individuals with only HIV infection.
In addition, about one third of human
population is estimated to suffer from latent tuberculosis, which can be
reactivated even after several decades (Glassroth,2005). It is estimated that,
between 2000 and 2020 nearly one billion new cases will be identified and the active
disease will affect 200 million, with about 35 million deaths, if control
measures are not significantly improved (WHO report, 2009). India is classified
along with the sub-Saharan African countries with high burden tuberculosis. It
ranks first in the five most tuberculosis prevalent countries from the
estimated numbers of cases in the year 2007. India accounts for one-third of
the global TB burden, with 1.8 million developing the disease each year and
nearly 0.4 million dying due to TB annually (Chouhan, 2003).
Tuberculosis is not only a disease of humans,
but also has a devastating effect on cattle and livestock. Bovine tuberculosis
caused by M. bovis is a significant
public health problem and it causes great economic losses in countries with
infected livestock. Despite the control measures, the incidence of bovine
tuberculosis in some countries for example in New Zealand, United Kingdom and
Republic of Ireland has remained the same or increased due to the presence of
endemic wildlife reservoirs (Olsen & Anderson, 2003). Also, bovine
tuberculosis remains a significant problem in developing countries; indeed more
than 94% of the world population live in countries in which the control of
bovine tuberculosis is either limited or completely absent (Vordermeier et al.,, 2006).
Fig.4 Diagnostic tests for M. Tuberculosis
infection
The demonstration of M. Tuberculosis infection is a relevant part of the diagnosis of
both TB disease and latent TB infection (LTBI). LTBI is defined as M. Tuberculosis complex infection without
clinical features or radiographic findings of TB disease. Children with LTBI
are at increased risk of developing active TB and becoming infectious.
Therefore the identification of children latently infected with M. tuberculosis
represents an important issue of TB preventive strategies. Tuberculin skin test
(TST) and interferon-gamma release assays (IGRAs) are immune-mediated methods
currently available for identifying M. Tuberculosis infection. However, TST and
IGRA are not able to distinguish between latent TB infection and active
disease, and a negative result does not exclude the possibility of M. Tuberculosis infection. There-fore,
according to the AAP, both TST and IGRAs should not be considered a gold
standard for the diagnosis of LTBI.
The TST is based on the evidence that M. Tuberculosis infection promotes a
delayed-type hypersensitivity reaction to antigenic components isolated from
tubercle bacilli culture by protein precipitation (PPD), also known as
tuberculin. All guidelines recommend that TST should be performed only in
children who are at increased risk of M. tuberculosis infection (i.e. contact
with people with contagious TB, HIV-infected children) or children with
suspected TB disease. The preferred method of PPD administration is represented
by Mantoux technique, which consists of an intradermal injection of 5
tuberculin units of PPD (0.1 ml) into the volar surface of the fore-arm, using
a 27-gauge needle. The test should be read between 48 and 72 hours after the
injection, measuring the transverse diameter of the induration. However, the
interpretation of TST results is still controversial. The majority of the
guidelines affirms that TST should be considered positive if the diameter of
the induration is > 10 mm in any child and> 5 mm in high risk children,
including HIV-infected children and severely malnourished children.
Otherwise the American guidelines recommend
three different cut-points. Five millimeters should be considered positive in
high-risk children, including close contact with contagious people with TB,
suspect of TB disease (i.e. chest radiograph suggestive of active or previous
TB) and immunodeficiency (i.e. HIV-infected children, children receiving
immunosuppressive therapy). In children at increased risk of disseminated TB
disease (i.e. children younger than 4 years, children with other medical conditions)
and children with likelihood of being infected with M. tuberculosis (i.e.
children born in high prevalence regions or children who travel to high
prevalence regions) the cut-off proposed is 10 mm. Eventually, TST >15mm is
positive in children aged 4 years or older without any risk factors. All the
guidelines agree that the TST result should be interpreted as positive regardless
of Bacille Calmette-Guérin (BCG) vaccination, because there is no way of
distinguishing between a positive TST due to M. tuberculosis infection and that caused by BCG vaccination.
Fig.5
Diagnostic tests for M. Tuberculosis infection
2.13
Treatment
Evolution of modern multiple drug treatment .The
humankind had to wait for more than 60 years following Robert Koch’s momentous
announcement of the discovery Mycobacterium
tuberculosis for drug(s) that could cure TB to become available (Fig.8).
The first controlled clinical trial in the history of medicine conducted by the
British Medical Research Council (BMRC) demonstrated the activity of
streptomycin. The BMRC assessed the addition of PAS to streptomycin in a
controlled clinical trial which showed a lower rate of clinical deterioration,
higher rate of culture conversion, and a lower rate of streptomycin resistance
in patients receiving streptomycin plus PAS, suggesting that combination
treatment with PAS was helpful in preventing the emergence of drug resistance
to streptomycin. The first clinical trial with isoniazid was initiated in 1951.
Subsequent studies by BMRC further assessed the utility of using two of the
three drugs, namely, streptomycin, isoniazid and PAS in various combinations to
treat TB. A later clinical trial by BMRC established the duration of anti-TB
treatment that would effectively prevent relapse, to be 18-24 months.
Ethambutol, discovered in 1961 got added
to the armamentarium of anti-TB drugs and soon replaced PAS in the standard
regimens. In the 1960s seminal research conducted by Wallace Fox and co-workers
at the National Institute for Research in Tuberculosis(NIRT), Chennai [then
called as Tuberculosis Chemotherapy Centre, Madras; later renamed as
Tuberculosis Research Centre (TRC), Madras in 1978] showed that home or
ambulatory treatment was almost as effective as sanatorium treatment “provided
the regular use of anti-TB medication was well organized and supervised”, a
fact that is often neglected even pyrazinamide to rifampicin containing
regimens then ensued. The 6-month regimens containing rifampicin and
pyrazinamide were as effective as either rifampicin or pyrazinamide containing
regimens in the clinical trials by the British Thoracic Society heralding the
modern 6-month short-course chemotherapy endorsed by the WHO that has become
the standard of care world over. Since then, most countries have been using the
WHO endorsed standardized daily or thrice-weekly Intermittent treatment
regimens in National TB Control Programmes.
2.14
Stages Of Mycobacterium tuberculosis Infection
Mycobacterium
tuberculosis is
mainly considered to be an airborne pathogen. The infection process of Mtb can
be divided into three different but interrelated stages. The first stage is the
aerosol trans-mission of droplets containing M.tb from an infected individual
to a healthy individual. Once within the lungs, M.tb enters and resides within
alveolar macrophages (AMs) and dendritic cells (DCs; Cooper, 2009). Though the
AM ingests bacilli and often kills them, the bactericidal capacity of the AM is
still not very well defined. In a given M.tbinfection, the initial containment
of the infection depends partially on the genetics of the human population
(i.e., defined by the intrinsic microbicidal capacity of host phagocytes) and
also on the inhaled Mtb strain (i.e., defined by innate virulent factors in
each Mtb strain). In the primary infection Mtb multiplies in the lungs and
causes mild inflammation. Although AMs are thought to be an effective barrier
to contain pathogens, Mtb has evolved various mechanisms to evade the host
immune response and survive in these cells. These survival mechanisms include
triggering an anti-inflammatory response, blocking reactive oxygen and nitrogen
intermediate (ROIs and RNIs, respectively) production, and reducing the
acidification of the Mtb containing phagosome (Flynn and Chan, 2001; Fenton et al.,, 2005; Cooper, 2009).
The next stage of infection is
characterized by the emergence of cell-mediated immunity and the formation of granulomas
(described below). Mtb bacilli that escape the bactericidal effects of the AM ,will
multiply and result in destruction of AMs. This will in turn attract blood
monocytes and other inflammatory cells (i.e., neutrophils) to the site of
infection. Monocytes mature to become antigen presenting AMs and DCs and
ingest, but not effectively kill the bacteria. At this stage, Mtb grows under
limited tissue damage. By 6–8 weeks post-infection, antigen presenting DCs have
traveled to lymph nodes where T lymphocytes are activated and recruited.
Activated T lymphocytes that migrate to the site of infection proliferate
forming an early stage granuloma, where macrophages become activated to kill
intracellular Mtb (Ulrichs and Kaufmann, 2006).However, continuing T cell
activation leads to formation of granulomas that mark the persistence stage of
the infection (latency), where the growth and spread of bacteria into
additional tissue sites are limited. At this stage more than 90% of infected people
remain asymptomatic, but Mtb may survives within AMs.
The third and final stage is when latent and
controlled Mtb infection is reactivated. There are two main reasons described
for a reactivation event to occur, a decline in the host’s immunity due to
genetic or environmental cause; and a failure to develop and maintain immune
signals. Under these circumstances, the granuloma structure disrupts and
results in lung cavitation and pulmonary disease (Kaplan et al.,, 2003; Dheda et al,,
2005; Ulrichs and Kaufmann, 2006; Russell, 2007). Among the genetic causes
described that make a subject susceptible to TB are mutations in specific host
C-type lectins, cytokines, chemokines, and their specific receptors disrupting
critical signaling pathways involved in the immune response against Mtb Compromised
immune surveillance for reasons such as co-infection with HIV, where a host
becomes immuno compromised especially for CD4 T cells (the cell target for
HIV), is the most important environmental or exogenous cause of susceptibility
to TB (Geldmacher et al., 2010). The
reactivation of M.tbinfection can also be due to changes in host
cytokine/chemokine networks, implicated in the inflammatory response against
M.tbinfection, that are a consequence of stress and/or old age (Turner, 2011).
Earlier studies have also suggested that exogenous re-infection with another
strain of Mtb (Sonnenberg et al.,
2001; Behr, 2004) is an additional factor leading to active disease.
Fig.6
Stages of Mycobacterium tuberculosis Infection
2.15 Virulent
Mechanisms of TB
TB mechanism for cell entry
·
The
tubercle bacillus can bind directly to mannose receptors on macrophages via the
cell wall-associated mannosylated glycolipid (LAM)
TB
can grow intracellularly
·
Effective means of evading the immune system
·
Once TB
is phagocytosed, it can inhibit phagosome-lysosome fusion
·
TB can
remain in the phagosome or escape from the phagosome ( Either case is a
protected environment for growth in macrophages)
Slow generation time:
·
Immune
system cannot recognize TB, or cannot be triggered to eliminate TB
High lipid concentration in cell wall:
·
accounts
for impermeability and resistance to antimicrobial agents
·
Accounts
for resistance to killing by acidic and alkaline compounds in both the
inracellular and extracelluar environment
·
Also
accounts for resistance to osmotic lysis via complement depostion and attack by
lysozyme
2.16 Antibiotic Mechanisms
•
Inhibition
of mRNA translation and translational accuracy (Streptomycin and derivatives)
•
RNA
polymerase inhibition (rifampicin) – inhibition of transcript elongation
•
Gyrase
inhibition in DNA synthesis (fluoroquinolone)
•
Inhibition
of mycolic acid synthesis for cellular wall (isoniazid)
•
Inhibition
of arabinogalactan synthesis for cellular wall synthesis (ethambutol)
•
Sterilization
– by lowering pH (pyrazinamide)
2.17 Bioinformatics
UniprotKB
UniProt is
a comprehensive, high-quality and freely accessible database of protein sequence and functional information, many entries being derived
from genome sequencing projects. It contains a large amount of information about the
biological function of proteins derived from the research literature (Dayhoff,
Margaret O. (1965)).
Protparam
ProtParam is a tool which allows the computation of various physical
and chemical parameters for a given protein stored in Swiss-Prot or TrEMBL or for a user entered protein
sequence. The computed parameters include the molecular weight, theoretical pI,
amino acid composition, atomic composition, extinction coefficient, estimated
half-life, instability index, aliphatic index and grand average of
hydropathicity (GRAVY) (John M.
Walker 2005).
SOPMA
This improved SOPM method (SOPMA) correctly predicts 69.5% of
amino acids for a three-state description of the secondary structure
(alpha-helix, beta-sheet and coil) in a whole database containing 126 chains of
non-homologous (less than 25% identity) proteins. Joint prediction with SOPMA
and a neural networks method (PHD) correctly predicts 82.2% of residues for 74%
of co-predicted amino acids. https://npsa-prabi.ibcp.fr/
SWISS MODEL
SWISS-MODEL is a server for automated comparative modeling of
three-dimensional (3D) protein structures. It pioneered the field of automated
modeling starting in 1993 and is the most widely-used free web-based automated
modeling facility today (SchwedeT.2006)
(http://swissmodel.expasy.org/.)
SMART
Simple Modular Architecture Research Tool (SMART) is a biological database that is used in the identification and analysis of protein domains within protein sequences.[1][2] SMART uses profile-hidden Markov models built from multiple sequence alignments to detect protein domains in protein sequences (Schultz et al., (1998)) (http://smart.embl.de/).
PDB
The Protein
Data Bank (PDB) is a repository for
the three-dimensional structural data of large biological molecules, such as
proteins and nucleic acids. The data, typically obtained by X-ray crystallography or NMR
spectroscopy and submitted by biologists and biochemists from around the world, are freely accessible on the
Internet via the websites of its member organization
(http://www.rcsb.org) (Meyer EF 1997)
PDBSum
The PDBsum is a pictorial database that provides an
at-a-glance overview of the contents of each 3D structure deposited in the
Protein Data Bank (PDB).It shows the molecule(s) that make up the structure (i.e. protein
chains, DNA, ligands and metal ions) and schematic diagrams of their
interactions. Extensive use is made of the freely
available RasMol molecular graphics program to view the molecules and
their interactions in 3D (Laskowski R A (2009)).
CATH
CATH is a classification of protein structures downloaded from the
Protein Data Bank. We group protein domains into superfamilies when
there is sufficient evidence they have diverged from a common ancestor (Hadley
C, Jones DT (1999)).
CSA
The Catalytic Site Atlas (CSA) is a database
documenting enzyme active sites and catalytic residues in enzymes of 3D
structure. (www.csa.com)
DrugBank
The DrugBank database is a comprehensive, freely accessible,
online database containing information on drugs and drug targets. As both a bioinformatics and a cheminformatics resource, DrugBank combines detailed drug (i.e. chemical,
pharmacological and pharmaceutical) data with comprehensive drug target (i.e.
sequence, structure, and pathway) information (Wilson M 2014).
3.
MATERIALS AND METHODS
3.1 Retrieval
of GlmU protein sequence
The bifunctional GlmU protein
sequence was retrieved from UniprotKB
protein sequence database UniProt is a comprehensive, high-quality and freely
accessible database of protein sequence and functional information.
3.2
Analysis of physicochemical parameters of GlmU protein
The different physicochemical properties of
GlmU were computed using Expasy’s ProtParam tool according to the method of (John
Walker, 2005). The ProtParam includes the following computed parameters: Molecular
weight, theoretical pI, aliphatic index and GRAVY (http: //web. expasy
.org/protparam/).
3.3
Secondary structure prediction
The secondary structure was predicted by
SOPMA (Self- Optimized Prediction Method with Alignment) (Deleage 2006). It was
employed for calculating the secondary structural features of GlmU. SOPMA
correctly predicts the secondary structure α-helix, β-sheet and coil.
3.4
Identification of Domain and Signal peptides
The
domains and signal peptides of GlmU were analyzed using SMART (Simple Modular
Architecture Research Tool). SMART is an online resource
(http://smart.embl.de/) for the identification and annotation of protein
domains and analysis of protein domain architecture.
3.5
Retrieval
of 3D structure of GlmU and visualization
The 3D
structure of GlmU was retrieved from RCSB PDB (Baker,2008) . The Protein Data Bank (PDB) is a repository for
the three-dimensional structural data of large biological molecules, such as
proteins and nucleic acids. The data, typically obtained by X-ray crystallography or NMR spectroscopy (http://www.rcsb.org)
and visualized using RasMol.
3.6 PDBsum is a pictorial database 3D structure in the Protein Data Bank
PDBsum is a pictorial database that provides an at-a-glance overview of
the contents of each 3D structure deposited in the Protein Data Bank (PDB)
(Laskowski R A 2007).It is also used to analyze protein model quality using
Ramchandran plot.
3.7 Classification of GlmU protein using
CATH
CATH was used to classify GlmU protein
according to the method of (Hadley C, Jones DT (1999)
CATH is a classification of protein structures downloaded from the Protein Data Bank. It groups
protein domains into super families when there is sufficient evidence they have
diverged from a common ancestor.
3.8 Catalytic Site
Atlas (CSA)
The Catalytic
Site Atlas (CSA) database was used to document enzyme active sites and catalytic
residues in GlmU protein 3D structure. Classification of catalytic residues
which includes only those residues thought to be directly involved in some
aspect of the reaction catalyzed by an enzyme (www.csa.com).
3.9
Identification of lead compound
Several potential drugs were identified using
Drug Bank, PDBchem, Chemspidser, ZINC, PubChem databases for GlmU protein and
further screening of potential drugs were carried out.
3.10
Calculating compound properties
Several potential drugs were screened by compound
properties using Lipinski rule of five.
3.11
Prediction of bioavailability of lead compounds
ADMET Predictor is used to predict certain
ADMET (Absorption, Distribution, Metabolism, Elimination, and Toxicity)
properties of drug-like compounds. Various ADMET properties were calculated for
all compounds such as Physicochemical and biopharmaceutical properties include
ionization constants (pKa), LogP, solubility, qualitative blood-brain barrier
permeability, percent unbound to blood plasma proteins, and pharmacokinetic
volume of distribution in human model.
3.12
Docking Protocol
By
using Hex6.3 docking offline server we dock the GlmU protein. Hexá is
inherently a graphical program; all docking results are immediately available
for viewing in a user-friendly way. Then we observe the maximum energy of
ligand and maximum efficiency of drug (Chang, 2006).
3.13 Molecular docking
The methodology of protein-ligand
docking is inherited from earlier work in small molecule conformational
sampling and macromolecular energy calculations. Calculating the accurate
protein-ligand interactions is the key principle behind structure based drug
discovery. Predicting ligand conformation within the active site of a
protein/receptor is termed as molecular docking. In general, there are two key
components of molecular docking.
a. Accurate pose
prediction or binding conformation of the ligand inside the binding site of the
target protein.
b. Accurate binding free
energy prediction, which later is used to rank order the docking poses.
3.14
Receptor and ligand preparation
Protein and ligand preparation was performed
using the Hex 2.6 software from start menu, by using file menu load the PDB
file of both the receptor and ligand. Then select docking from controls menu
then by restoring default parameters activate the docking. Then there appears
docking progress from showing different stages of progress in docking in
graphical form. Then, it displays about 250 models of your docking between
ligand and receptor which can be viewed by related animation from graphical
menu. By using its fast flourier transform method it provides docking models
with least RMS values and energy.
4.
Results and Discussions
4.1 Primary
protein sequence analysis
The
sequence of GlmU having Uniprot ID (PMN9W3) contains 495 amino acids with
molecular weight 51452.8 Da and pI 5.64. The physicochemical properties of the
protein by Protparam tool were shown in Table 2. The number of positive
amino acids are (Arg+Lys) 39 and the number of negative amino acids are
(Asp+Glu) 51. The extinction coefficient of GlmU was 24535 and absorption was
0.1%, indicates how much a protein absorbs light at a certain wavelength. The
instability index provides an estimate of the stability of protein in a test
tube. The aliphatic index of GlmU protein was 94.55 and defined as the relative
volume occupied by aliphatic side chains (alanine, valine, isoleucine, and
leucine). It may be regarded as a positive factor for the increase of
thermostability of globular proteins.
Upgade Akhilesh et al.,
(2012) also used expasy’s ProtParam tool to calculate physiochemical parameters
of the three Herpes Simplex Viral proteins such as P04290, P06477 and P04486
and similarly characterization of PtpA protein of Mycobacterium tuberculosis
was done using ProtParam tool by Merly D. P et
al., (2012).
4.2 Secondary structure
prediction by SOPMA
The secondary structure of the GlmU protein was predicted using
SOPMA. The parameters considered were window size as 17 residues and similarity
threshold as 8 the number of alpha helix are 144, extended strands 129, beta
turns 54 and random coiles 168. The details of secondary structure were shown
in Fig.7.
Mao et al., (2005)
predicted the secondary structure of human metapneumovirus attachment (G)
protein by using SOPMA and Syyada Jafri et al (2012) used SOPMA to predict the
secondary structure Gp41 envelope glycoprotein of human.
|
Total
number of amino acid
|
495
|
|
Molecular
weight
|
51452.8
Da
|
|
pI
|
5.64
|
|
UniProt
ID
|
P9WMN3
|
|
The
number of positive amino acids (Arg+Lys)
|
39
|
|
The
number of negative amino acids (Asp+Glu)
|
51
|
|
Extinction
coefficient
|
24535
|
|
Aliphatic
index
|
94.55
|
Table.2.The
physicochemical properties of the protein by Protparam tool
Fig.7 a) Secondary
structure prediction by SOPMA
Fig.7 b) Secondary structure prediction by
SOPMA
4.3 Identification of Domain and Signal peptides
The
domains and signal peptides were predicted using SMART. There were two
overlapping domains in GlmU protein, IspD domain having sequence length
7-250,NTP_transferase domain having sequence length 8-277, NTP_transferase_3
having sequence length 9-261. IspD domain and NTP_transferase domain and their sequence
were shown in Fig.8 and Fig.9 respectively. NTP_transferase transfer
nucleotides from one compound to another. This domain is found in a number of
enzymes that transfer nucleotides onto phosphosugars. IspD domain is shown in
Fig.9. It starts at position 25 and ends at position 268 with E-value of
2.00e13. 4-diphosphocytidyl-2C-methyl-D-erythritol synthase, a bacterial IspD
protein, catalyzes the third step of the deoxyxylulose-5-phosphate pathway
(DXP) of isoprenoid biosynthesis; i.e. the formation of
4-diphosphocytidyl-2C-methyl-D-erythritol from CTP and 2C-methyl-D-erythritol
4-phosphate. The isoprenoid pathway is a well-known target for anti-infective
drug development.
Merly D. P et al (2012) used SMART and pfam to identify domains in
PtpA protein of mycobacterium tuberculosis and domain identified was Tyrosine
Phosphatase domains which has catalytic activity. Unlike this Reddy et al.,, (2009) used PRODOM to identify
domain in STK11 protein which was involved in PJ Syndrome.
4.4 Retrieval of 3D structure of GlmU and
structure validation
The ligand free 3D structure of GlmU was
downloaded from RCSB PDB having pdb id 3D98. The structure was visualized in
RasMol as shown in Fig. 10. The structure validation was done using PROCHECK by
Ramchandran plot. The model quality was 86.8% as shown in Fig.11.
Fig.10
Retrieval of 3D structure of GlmU and structure validation
Fig.11a)The
structure validation was done using PROCHECK by Ramchandran plot.
Fig.11
b) The model quality was 86.8%
4.5 Classification of GlmU protein using
CATH
CATH
classification of GlmU protein revealed that the proteins belongs to the
superfamily of Spore coat polysaccharide biosynthesis protein spsA chain A and
belongs to the functional family of bifuctional protein GlmU as shown in
Fig.12.
4.6 Prediction of Catalytic residues in
GlmU protein
Catalytic
residues in Glmu protein were predicted by using Catalytic Site Atlas tool
which revealed that the residue Arg present at position number 19 in GlmU
protein as shown in Fig.13.
Fig.12 Classification of GlmU protein
using CATH
Fig.13 Prediction of Catalytic residues in
GlmU protein
4.7 Identification
of lead compound
Lead
compounds were identified for GlmU target protein using several chemical databases.
Total hits in DrugBank (43), PUBChem(19) and drug like compound were screened
according to Lipinski rule of five. The compound which fulfilled the Lipinski
rule of five was Quinapril having Lipinski type properties molecular weight
438.5, LogP 2.57, number of hydrogen bond donor 2, number of hydrogen bond
acceptor 7, TPSA 95.54, number of rotatable bonds 10.
Physical properties like molar refractivity
119.51± 0.3 cm3 molar volume 360.1± 3.0 cm3 surface tension
52.2± 3.0 dyne/cm as shown in Fig14.
ADME properties for Quinapril were oral bioavailability
less than 30%, good solubility electrolyte, LogSw=-3.51, stability (pH < 2)
chemically stable at acidic conditions, passive absorption-good (>70%)
passive absorption across intestinal barrier, significant (> 50% of absorbed
fraction) first pass metabolism in liver and/or intestine, active transport
across intestinal barrier. Blood brain barrier- Main physico-chemical
determinance: LogP 2.57 pKa (Acid 2.90), fraction unbound in plasmai0.0759, pKa
(Base 5.40), BBB transport parameters: Rate of brain penetration-LogPS -3.2,
Extent of brain penetration: LogPB -0.61,Brain/plasma equilibration rate:
Log(PS*fu,brain) -3.7 as shown in Fig,15.
Toxicity test of Quinapril : Ames
test: 0.05 reliability: high (RI=0.8),Genotoxicity hazards-no genotoxicity
hazards was found, hERG inhibitor probability 0.06 ,toxicity catagories- Non
Toxic (LD50 > 5000 mg/kg), Endocrine Disruption-No binding to Estrogen
receptor alpha (LogRBA < -3) probability of
estrogen receptor binding LogRBA : > -3: 0.05 and No Health effects
on Blood 0.21, Cardiovascular system 0.61, Gastro intestinal system 0.65
,Kidney 0.55,Liver 0.58, Lungs 0.09 as shown in Fig,16.
Fig14.
Lipinski rule of five
Fig.15
ADME properties for Quinapril
Fig.16
a) Toxicity test of Quinapril
Fig.16
b) Toxicity test of Quinapril
5.
Similarity Search
Similarity describes how two compounds are
structurally similar to each other. Thus if two compounds are highly similar to
each other they should have similar chemical as well as biological properties.
Using this concept, we tried to find relationship between actual and predicted
inhibitory activity values. In order to predict the activity of a compound, we
took the average of pIC50 value for all hits (except self hit) that have high
similarity with query compound. We used software JC Search [29] for searching
similar compounds using different similarity cutoff value. A poor correlation
among the actual and predicted pIC50 values was observed, so this was not
pursued further.
In-silico ADMET property calculations are
useful in predicting the drug likeliness properties of a new chemical entity as
it helps to identify the potential lead compound with minimal unwanted effects.
We have carried out In-silico ADMET studies on Quinapril and its analogues
isolated form peroxydisulfate oxidation of quinapril to develop them as
potential antimycobacterium agent. The results of present study are very
encouraging and indicate that the analogue Q1 can be a good clinical compound.
ADMET result: Ames test=0.05,it is non
toxic and no binding to estrogen
receptor alpha.
Fig3.Quinapril docked with GlmU protein
6.
Conclusions:-
Quinapril is a nonpeptide prodrug that is
deesterified to quinaprilat (Quinapril diacid), its major active metabolite
following oral administration. Quinapril may be used to treat Mycobacterium tuberculosis in future. it
having good quality, I have obtained potential drug for MTB.
This study describes the development of a
freely available web server for screening chemical compounds library against
GlmU protein. The docking approach also provides valuable information about
protein-ligand interaction and help in further ligand based drug designing.
This server will be useful to narrow down the time and cost required to screen
a chemical library.
7.
Summary
1. The GlmU protein was retrieved from
Uniprot database and it is having length of 495. The primary protein sequence
analysis carried out using protparam tool and it retrieval that having a Molecular
weight - 51452.8 Da, pI - 5.64, The number of positive amino acids (Arg+Lys) -
39, The number of negative amino acids (Asp+Glu)-51 respectively., Aliphatic
index-94.55, GRAVY-…….
2.
Secondary structure of GlmU protein was predicted by SOMPA having alpha helix-144, beta turns 54,
extended strands 129, random coiles 168 so protein was
highly stable.
3. The Domain and signal peptide using SMART there were to
overlapping domain in GlmU protein.
4. 3D structure of GlmU protein was downloaded from RCSB PDB in
having 3D98 .Structure was validated using PROCHECK having model quality was 86.8%.
5. Classification protein was done with using
CATH and belongs to superfamily of Spore
coat polysaccharide biosynthesis and functional family of bifuctional protein.
6. Catalytic residues in Glmu
protein were predicted by using Catalytic Site Atlas tool which revealed that
the residue Arg present at position number 19 in GlmU protein.
7. Lead
compounds were identified for GlmU target protein using several chemical
databases. Total hits in DrugBank (43), PUBChem(19) and drug like compound were
screened according to Lipinski rule of five.
8. Physical properties like molar
refractivity 119.51± 0.3 cm3 molar volume 360.1± 3.0 cm3 surface
tension 52.2± 3.0 dyne/cm.
9. ADME properties for Quinapril such as LogP
2.57 pKa (Acid 2.90), fraction unbound in plasmai0.0759, pKa (Base 5.40), BBB
transport parameters: Rate of brain penetration-LogPS -3.2, Extent of brain
penetration: LogPB -0.61,Brain/plasma equilibration rate: Log(PS*fu,brain) -3.7.
Ames test=0.05,it is non toxic and no
binding to estrogen receptor alpha.
Structure of Quinapril 3D
Structure of GlmU
