to find a potent anti bacterial agent for cell wall protein using bioinformatics
DESCRIPTION
This work was performed to find a potent anti-bacterial agent for cell wall protein and glycerol kinase using natural agents targetting biological process important in endocarditis using BioinformaticsTRANSCRIPT
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ABSTRACT
Endocarditis is Infection and inflammation of the inner layers of the heart,
most commonly the valves cause by bacteria. This infection results in a serious
illness which requires prolonged treatment and on occasion produces injury to the
heart or even death. Streptococcus mitisB6 is Gram-positive bacteria are not
usually pathogenic but commonly cause bacterial Endocarditis. Here we present a
study find drug for Endocarditis from natural products. In that study, collection of
all proteins of Streptococcus mitis B6 through blast, then those proteins were tested
in DEG database and depending on their deg score some proteins are accepted.
Then by using CELLO prediction tool we find where the protein is present in
bacterial cell. Then we filter the proteins which are present in membrane. Then
collect pdb ids for filtered proteins by using CPH models tool. After that, we
collected 100 natural products. Using pubchem, values are collected for the
products and by passing Lipinski’s rule to the products we filtered the products to
84. Then we did docking between 2 targets (bacterial proteins) and 84 proteins
(natural products). This study has investigated that natural antibacterial compounds
like sanguinarine, Coptisine Columbamine, Berbarine, Yohimbine for 3KDS and
Coptisine, Diterpine, Kaempferol, Populene, Carnosic acid and Berbarine for
3H3N. Our results reveal that these compounds use less energy to bind with targets
and inhibit its activity. Their high ligand affinity to the target introduce the
prospect of their use in chemo preventive applications in addition they are freely
available natural products that can be safely used to cure Endocarditis.
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INTRODUCTION
ENDOCARDITIS:
Endocarditis is a disease characterized by inflammation or infection of the
inner surface of the heart (the endocardium). Endocarditis commonly affects heart
valves, but may also involve non-valvular areas or mechanical devices that are
implanted in the heart, such as artificial heart valves, pacemakers, or implantable
defibrillators.
Endocarditis is an infection of the inner surface of the heart or the heart
valves caused by bacteria usually found in the mouth, intestinal tract or urinary
tract. This infection results in a serious of illness which requires prolonged
treatment and on occasion produces injury to the heart or even death. Endocarditis
is a major concern in almost all unrepaired congenital heart defects as well as in
most repaired defects with a few exceptions. Endocarditis occurs when bacteria
grow on the edges of a heart defect or on the surface of an abnormal valve after the
bacteria enter the blood stream, most commonly from dental procedures but also
from procedures involving the gastrointestinal or urinary tract. Once the bacteria
infect the inner surface of the heart, they continue to grow producing large
particles called vegetation that may then break off and travel to the lungs, brain,
kidneys and skin. The continuing infection may also seriously damage the heart
valve on which the vegetations have grown.
Parents of children with a heart defect, repaired or unrepaired, should ask
their cardiologist or primary physician whether their particular child requires
protection from Endocarditis and inform the dentist or physician performing a
procedure of this requirement. All dentists should be aware of the type and dose of
antibiotic from standardized recommendations by the American Heart Association
and the American Dental Association. The American Heart Association provides a
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small card for parents listing the child's name, diagnosis, prescribing physician and
explaining the type and dose of antibiotics to prevent Endocarditis. The dentist or
operating physician should be able to prescribe the antibiotic but if there is
confusion the parent should check with the child's cardiologist or primary
physician and they will be able to clarify the situation. Since the most common
cause of bacterial Endocarditis is bacteria from gums (alpha-Hemolytic
streptococci), good dental and gum hygiene is particularly important for children
with congenital heart disease. This dental hygiene should be implemented by
periodic dental checks and by following your dentist's instructions in caring for
your child's teeth and gums.
1. TYPES OF ENDOCARDITIS :
Acute Endocarditis
Sub-acute Endocarditis
Bacterial Endocarditis
Infective Endocarditis
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1.1. ACUTE ENDOCARDITIS :
Figure 1 : Acute Endocarditis
Endocarditis can escalate to an acute case rapidly, especially when an
aggressive species of skin bacteria enters the bloodstream and attacks a normal,
undamaged heart valve. Once staph bacteria begin to multiply inside the heart, they
may send small clumps of bacteria called septic emboli into the bloodstream to
spread the infection to other organs, especially to the kidneys, Iungs and brain.
Unfortunately injecting drug users are at high risk for acute Endocarditis, as
aggressive staph bacteria have many opportunities to enter the blood through
broken skin and unhygienic drug paraphernalia. If untreated, this form of
Endocarditis can be fatal in less than two months.
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1.2. SUB-ACUTE BACTERIAL ENDOCARDITIS :
Sub-Acute Bacterial Endocarditis (SBE) is a bacterial infection that
produces growths on the endocardium (the cells lining the inside of the heart). Sub-
Acute bacterial Endocarditis usually (but not always) is caused by a viridans
streptococci (a type of bacteria); it occurs on damaged valves, and, if untreated,
can become fatal within six weeks to a year.
Figure 2 : Sub-Acute Endocarditis
DESCRIPTION OF SUBACUTE BACTERIAL ENDOCARDITIS:
Endocarditis has traditionally been classified as acute or subacute based
upon the pathogenic organism and the clinical presentation. This distinction has
become less clear, however, and the less specific term "infective Endocarditis" is
now more commonly used.
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Most patients who develop infective Endocarditis have underlying cardiac
disease, although this is frequently not the case with intravenous drug abusers and
hospital-acquired infections.
1.3. BACTERIAL ENDOCARDITIS:
Bacterial Endocarditis is a microbial infection of the endothelial surface of
the heart. Signs and symptoms of bacterial Endocarditis are diverse; therefore, the
practitioner must have a high degree of suspicion to make an early diagnosis. In
addition, classification that implicates the temporal aspect, etiology, anatomic site
of infection, and relevant pathogenic risk factors is essential in therapeutic and
prognostic considerations.
Figure 3 : Bacterial Endocarditis
MECHANISM:
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Figure 4: Mechanism of Bacterial Endocarditis
RISK FACTORS AND CAUSES:
In infective Endocarditis, the bacteria cluster on and around the heart valves;
this may impair their ability to function properly. Although bacterial Endocarditis
may occur in anyone at any time, it is unusual in persons who do not have valvular
heart disease.
Valves deformed by a previous attack of rheumatic fever were once a major
predisposing factor, but this is less so today since rheumatic fever has become
much less common.
Other predisposing factors include artificial heart valves, some congenital
heart disorders, hypertrophic cardiomyopathy, and mitral valve prolapse with
regurgitation. People with such risk factors are more likely to develop Endocarditis
when exposed to an infection from any source.
Dental surgery, urologic or gynecologic surgery, colonoscopy, and skin
infections increase the risk of Endocarditis, even if there is no pre-existing
anatomic valve deformity.
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Intravenous drug users are also at significant risk.
Bacteria are the leading cause of infective Endocarditis. Hence, infective
Endocarditis can more specifically be called Bacterial Endocarditis.
Bacterial Endocarditis, in turn, can be classified as either Sub-Acute
Bacterial Endocarditis (SBE) or Acute Bacterial Endocarditis (ABE). In
cases of Sub-Acute Bacterial Endocarditis, infection is often with less
virulent organisms, such as Streptococcus viridans. More invasive bacteria
such as staphylococci result in a more fulminate, faster developing or acute
bacterial Endocarditis.
Many types of organism can cause infective Endocarditis. These are
generally isolated by blood culture, where the patient's blood is removed,
and any growth is noted and identified. Alpha-hemolytic streptococci, that
are present in the mouth will often be the organism isolated if a dental
procedure caused the bacteraemia. If the bacteraemia was introduced
through the skin, such as contamination in surgery, during catheterization, or
in an IV drug user, Staphylococcus aureus is common.
A third important cause of Endocarditis is Enterococcus species. These
bacteria enter the bloodstream as a consequence of abnormalities in the
gastrointestinal or urinary tracts. Enterococcus species are increasingly
recognized as causes of nosocomial or Hospital-Acquired Endocarditis. This
contrasts with alpha-hemolytic streptococci and Staphylococcus aureus
which are causes of Community-Acquired Endocarditis.
SYMPTOMS OF BACTERIAL ENDOCARDITIS:
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The list of signs and symptoms mentioned in various sources for Bacterial
Endocarditis includes the 22 symptoms listed below:
Fever
Fatigue
Loss of appetite
Night sweats
Chills
Joint discomfort
Headache
Weakness
Aches
Back pain
Heart murmur
Weight loss
Shortness of breath on exertion
Swollen feet
Swollen legs
Swollen abdomen
Blood in urine
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Dark lines under nails caused by hemorrhage
Unusual urine color
Painful red nodes on finger pads
Painful red nodes on toe pads
Petechiae
Figure 5
Figure 5: Mitral valve vegetation shown by
echocardiogram. The vegetation is the mass seen in the
dark space between the left atrium (LA) and left
ventricle (LV). RA indicates right atrium; RV, right
ventricle.
When Endocarditis occurs, small masses called vegetations form at the site
of infection. When vegetations are viewed under a microscope, generally one
sees the microorganism that causes the infection embedded in a meshwork of
fibrin and other cellular material similar to that used by the body to form blood
clots. White blood cells that the body uses to fight infection are uncommon, a
finding which explains the need to give antibiotics over many weeks to kill the
infecting organism and cure Endocarditis. The absence of white blood cells in
vegetations is not fully explained but likely relates in part to the dense nature of
the vegetation tissue, which in turn restricts the migration of these cells. Also,
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the bacteria causing Endocarditis are buried in a non growing state deep in the
vegetation. In this state they do not generate the intense chemical signals that
usually promote the migration of white cells to a site of infection.
Figure 6
Figure 6: This figure shows one portion (called a
leaflet) of the mitral valve of the heart. The valve
has been excised surgically in the course of
treating Endocarditis. There is a large mass or
vegetation on the valve, and it is surrounded by
bleeding into the valve tissue that has resulted
from valve damage.
WHO GETS ENDOCARDITIS?
Endocarditis occurs when bacteria enter the bloodstream (bacteremia) and
attach to a damaged portion of the inner lining of the heart or abnormal heart
valves. Not all bacteria entering the bloodstream are capable of causing
Endocarditis. Only those bacteria that are able to stick to the surface lining of the
heart and to abnormal valves tend to cause Endocarditis. The ability of these
bacteria to stick to the surface lining is aided by a pre-existing microscopic clot that
often forms at these abnormal sites.
Endocarditis most often occurs in people with preexisting heart disease
(which may or may not be known to patients or their physicians) and less
commonly in people with normal hearts.
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PRE-EXISTING HEART CONDITIONS ASSOCIATED WITH
ENDOCARDITIS:
Previous cardiac valve surgery
Previous infective Endocarditis
Mitral valve prolapse with valve leakage
Abnormal valves caused by rheumatic fever and degenerative conditions
Certain congenital heart diseases
Some congenital heart defects (e.g., ventricular septal defect, atrial septal
defect, or patent ducts arteriosus) can be repaired surgically. Once repaired, they
are not associated with an increase in the risk of subsequent Endocarditis.
WHAT CAN HAPPEN TO PATIENTS WITH ENDOCARDITIS?
Untreated, most patients with infective Endocarditis will die. The infection
can lead to damage of the heart valve(s) that in turn causes severe leaking
(regurgitation) of blood back through the valve(s) and an inability of the heart to
efficiently pump blood to the body. This in turn may lead to congestive heart
failure and can cause symptoms such as shortness of breath or swelling of the
ankles. In addition, small pieces of the vegetation that we described in our
introductory paragraph can break off and travel through the blood vessels to other
parts of the body. These pieces, called emboli, can cause damage to organs such as
the brain (a stroke), eyes, lungs, kidneys, spleen, liver, and intestines. Endocarditis
can also cause heart rhythm changes that may require a pacemaker for correction.
COMPLICATIONS:
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The list of complications that have been mentioned in various sources for
Endocarditis includes:
Heart block
Heart embolism (see Heart symptoms)
Heart valve damage
Figure 7: Echo samples of Endocarditis
DIAGNOSIS AND TREATMENT FOR ENDOCARDITIS:
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DIAGNOSIS OF ENDOCARDITIS :
Diagnosis is usually suspected based upon the patient's history, symptoms,
and findings such as a new murmur. It may be confirmed by blood tests (blood
cultures) to identify an infectious organism. An echocardiogram (an ultrasound
study of the heart muscle and valves) may be helpful in identifying a clump of
bacteria on the heart valve.
TREATMENT OF ENDOCARDITIS :
Bacterial Endocarditis almost always requires hospitalization for antibiotic
therapy, generally given intravenously, at least at the outset. Occasionally, therapy
with oral antibiotics at home will be successful.
Antibiotic therapy usually must continue for at least a month. Most patients
respond rapidly to institution of appropriate antibiotics, with over 70 percent of
patients becoming afebrile (without a fever) within one week. In unusual cases,
surgery may be necessary to repair or replace a damaged heart valve.
Endocarditis is usually prevented by giving your child an antibiotic just prior
to a procedure that would release bacteria into the blood stream, and repeating a
smaller dose of the antibiotic six hours after the procedure. The most common
procedure causing Endocarditis is dental cleaning where bacteria in the gums are
released into the blood stream. Tonsillectomy and adenoidectomy may also be a
source of bacteria producing Endocarditis as well as previously mentioned urinary
and gastrointestinal tract procedures. On the other hand ear tube insertion, the most
common surgical procedure in children, presents less risk of Endocarditis and does
not require preventive antibiotics. Orthodontic procedures generally do not present
a risk, but the decision to use antibiotics is up to the orthodontist and related to the
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degree of manipulation during an orthodontic visit. The most common antibiotic
used to prevent Endocarditis is Amoxicillin but in the case of penicillin allergy
Erythromycin is used.
Parents of children with a heart defect, repaired or unrepaired, should ask
their cardiologist or primary physician whether their particular child requires
protection from Endocarditis and inform the dentist or physician performing a
procedure of this requirement. All dentists should be aware of the type and dose of
antibiotic from standardized recommendations by the American Heart Association
and the American Dental Association. The American Heart Association provides a
small card for parents listing the child's name, diagnosis, prescribing physician and
explaining the type and dose of antibiotics to prevent Endocarditis. The dentist or
operating physician should be able to prescribe the antibiotic but if there is
confusion the parent should check with the child's cardiologist or primary
physician and they will be able to clarify the situation. Since the most common
cause of bacterial Endocarditis is bacteria from gums (alpha-Hemolytic
streptococci), good dental and gum hygiene is particularly important for children
with congenital heart disease. This dental hygiene should be implemented by
periodic dental checks and by following your dentist's instructions in caring for
your child's teeth and gums.
EMPIRICAL THERAPY:
Bacterial Endocarditis (particularly prosthetic or Staphylococcus aureus
Endocarditis) may progress rapidly and in such cases antibiotic therapy must be
commenced as soon as all the appropriate specimens have been collected. If the
diagnosis of Endocarditis is in doubt, the patient is clinically stable and has already
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received antibiotics; we recommend stopping any antibiotics for 2–4 days and re-
culturing.
If empirical therapy is indicated, we recommend a combination of
flucloxacillin (8–12 g daily in 4–6 divided doses) plus gentamicin (1 mg/kg body
weight 8 hourly according to renal function) if the patient is acutely unwell, or
penicillin (or ampicillin/amoxicillin) plus gentamicin if the presentation is more
indolent. If the patient has intra-cardiac prosthetic material, or MRSA is suspected,
we recommend vancomycin (1 g 12 hourly according to renal function) plus
rifampicin (300–600 mg 12 hourly, orally) plus gentamicin (1 mg/kg 8 hourly iv).
Therapy should be reviewed as soon as the etiological agent is identified.
DURATION OF THERAPY:
Apart from the treatment of certain strains of penicillin-sensitive
streptococci, we recommend a minimum of 4 weeks therapy. There is evidence
from patients with enterococcal Endocarditis and some data from early studies of
streptococcal Endocarditis to suggest that patients who have had symptoms for
more than 3 months benefit from 6 weeks of penicillin. Often these individuals
have larger vegetations and mitral valve disease (also indicators of a poorer
response). These factors should be taken into consideration when determining
treatment length. Apparent failure to respond to treatment may indicate the need
for surgical intervention. There is no evidence to support the use of oral ‘Follow-
On’ therapy after completion of a course of treatment.
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HOME THERAPY:
Home therapy for Endocarditis has been described. Suitability for home
therapy will depend on the patient, the availability of the infrastructure to support
such therapy and the sensitivity of the infecting organism to antibiotics, which lend
themselves to home therapy.
Home treatment is often considered for Streptococcal Endocarditis, as it can
be less destructive, with fewer complications, than infection caused by other
organisms. Trials of home therapy have been reviewed. Antibiotics such as
ceftriaxone or teicoplanin, which can be given once daily iv or im, have been
advocated as the patient may not need a central venous catheter. Neutropenia is,
however, a well described side effect of ceftriaxone, occurring in two of 55
patients in one study. Teicoplanin also has side effects, including a high rate of
drug fever.
MEDICAL PROCEDURES FOR WHICH ANTIBIOTIC PREVENTION
(PROPHYLAXIS) IS RECOMMENDED:
Dental procedures likely to cause significant bleeding, including professional
teeth cleaning
Tonsillectomy or adenoidectomy
Certain types of surgery on the respiratory passageways, the gastrointestinal
tract, or the urinary tract
Surgery on infected tissues or structures
ANTIBACTERIAL:
An antibacterial is a compound or substance that kills or slows down the
growth of bacteria. The term is often used synonymously with the term
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antibiotic(s); today, however, with increased knowledge of the causative agents of
various infectious diseases, antibiotic(s) has come to denote a broader range of
antimicrobial compounds, including anti-fungal and other compounds.
The term "Antibiotic" was coined by “Selman Waksman” in 1942 to
describe any substance produced by a microorganism that is antagonistic to the
growth of other microorganisms in high dilution. This definition excluded
substances that kill bacteria but are not produced by microorganisms (such as
gastric juices and hydrogen peroxide). It also excluded synthetic antibacterial
compounds such as the sulfonamides. Many antibacterial compounds are relatively
small molecules with a molecular weight of less than 2000 atomic mass units.
Anti-Bacterial’s are commonly classified based on their mechanism of
action, chemical structure, or spectrum of activity. Most antibacterial antibiotics
target bacterial functions or growth processes. Antibiotics that target the bacterial
cell wall (such as penicillin’s and cephalosporin’s), or cell membrane (for example,
polymixins), or interfere with essential bacterial enzymes (such as quinolones and
sulfonamides) have bactericidal activities. Those that target protein synthesis, such
as the amino glycosides, macrolides, and tetracycline’s, are usually bacteriostatic.
Further categorization is based on their target specificity. "Narrow-spectrum"
antibacterial antibiotics target specific types of bacteria, such as Gram-negative or
Gram-positive bacteria, whereas broad-spectrum antibiotics affect a wide range of
bacteria. Following a 40-year hiatus in discovering new classes of antibacterial
compounds, three new classes of antibiotics have been brought into clinical use.
These new antibacterials are cyclic lipopeptides (including daptomycin),
glycylcyclines (e.g., tigecycline), and oxazolidinones (including linezolid).
ADMINISTRATION:
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Oral antibacterial are orally ingested, whereas intravenous administration
may be used in more serious cases, such as deep-seated systemic infections.
Antibiotics may also sometimes be administered topically, as with eye drops or
ointments.
SIDE EFFECTS:
Antibacterial are screened for any negative effects on humans or other
mammals before approval for clinical use and are usually considered safe and most
are well-tolerated. However, some antibacterial have been associated with a range
of adverse effects. Side-effects range from mild to very serious depending on the
antibiotics used, the microbial organisms targeted, and the individual patient.
Safety profiles of newer drugs are often not as well established as for those that
have a long history of use. Adverse effects range from fever and nausea to major
allergic reactions including photo dermatitis and anaphylaxis. Common side-
effects include diarrhea, resulting from disruption of the species composition in the
intestinal flora, resulting, for example, in overgrowth of pathogenic bacteria, such
as Clostridium difficile. Antibacterials can also affect the vaginal flora, and may
lead to overgrowth of yeast species of the genus Candida in the Volvo-vaginal
area. Additional side-effects can result from interaction with other drugs, such as
elevated risk of tendon damage from administration of a quinolone antibiotic with
a systemic corticosteroid.
SUBTRACTIVE GENOMICS:
Computational subtractive genomics approaches , based on the strategy that
an essential survival protein non-homologous to any human host protein is a
candidate drug target for a given parasite, have been successfully used to identify
putative drug targets in Pseudomonas aeruginosa, H. pylori B. pseudomallei, and
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A. hydrophila. In the presented report, a similar approach has been carried out to
screen L. donovaniproteome in order to identify its essential proteins and
subsequent drug and vaccine targets from various metabolic pathways. The online
availability of gene and protein sequence information of threatening human
parasites in the past decade and the completion of the human genome project has
revolutionised the field of insilico drug identification against parasites. The
methodologies for vaccine and drug development are progressively shifting from
the gene centric to genome centric. Bioinformatics, comparative genomics and
proteomics provide new opportunities to identify candidate drug targets performing
essential biological function. The search for potential drug targets is based on the
fact that the potential target must be unique i.e. must be only present in parasite
and play an essential role in the parasite's survival and constitute a critical
component in its metabolic pathway. At the same time, this target should non
homologous to the human host.
STREPTOCOCCUS MITIS B6 GENOME:
GENOME SUMMARY:
ORGANISM: Streptococcus mitis B6
TAXONOMY: Bacteria, Firmicutes, Lactobacillales, Streptococcaceae,
STREPTOCOCCUS(TAXID: 365659)
SIZE: 2.1 Mbp (2,146,611 bp)
STATUS: Complete
REPLICONS: 1
GENES: 2,098
PROTEINS: 2,004
GC CONTENT: 40.0%
GRAM STAIN: Positive
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SHAPE: Coccus
ARRANGEMENT: Chains
MOTILE: No
HABITAT: Host-associated
OXYGEN REQUIREMENT: Aerobic
TEMPERATURE RANGE: Mesophilic
PATHOGENIC - Yes: Human
DISEASE: Bacterial Endocarditis
DESCRIPTION:
Streptococcus mitis (strain B6) is a commensal Gram-positive normally
found in the human mouth, throat and nasopharynx. It is not usually pathogenic but
can be recovered from ulcerated teeth, sinuses and blood or heart lesions from
subacute Endocarditis (inflammation of the membrane lining the heart) patients. It
is an unusually high-level beta-lactam resistant and multiple antibiotic resistant
strains, which is part of the Mitis group of Gram positive bacteria that include one
of the major human pathogens Streptococcus pneumoniae. Most of the genes
involved in the pathogenicity such as pneumococcal virulence factors, the surface
proteins implicated in host cell interaction and choline containing teichoic acids
which are the anchor structure of choline binding proteins (CBPs) and host
pathogen interactions, appear to be absent from S. mitis.
PROPERTIES:
PRESENCE OF FLAGELLA: No
HUMAN PATHOGEN: No
INTERACTION: Animal commensal in Mammalia
NUMBER OF MEMBRANES: 1
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NUMBER OF INTEINS: 0
Streptococcus mitis is the closest relative of the major human pathogen S.
pneumoniae. The 2,15 Mb sequence of the Streptococcus mitis B6 chromosome,
an unusually high-level beta-lactam resistant and multiple antibiotic resistant
strain, has now been determined to encode 2100 genes. The accessory genome is
estimated to represent over 40%, including 75 mostly novel transposases and IS,
the prophase phiB6 and another seven phage related regions. Tetracycline
resistance mediated by Tn5801, and an unusual and large gene cluster containing
three amino glycoside resistance determinants have not been described in other
Streptococcus spp.
Comparative genomic analyses including hybridization experiments on a S.
mitis B6 specific microarray reveal that individual S. mitis strains are almost as
distantly related to the B6 strain as S. pneumoniae. Both species share a core of
over 900 genes. Most proteins described as pneumococcal virulence factors are
present in S. mitis B6, but the three choline binding proteins PcpA, PspA and
PspC, and three gene clusters containing the hyaluronidase gene, ply and lytA, and
the capsular genes are absent in S. mitis B6 and other S. mitis as well and confirm
their importance for the pathogenetic potential of S. pneumoniae. Despite the close
relatedness between the two species, the S. mitis B6 genome reveals a striking X-
alignment when compared with S. pneumoniae.
ECOLOGY:
S. mitis is a part of the normal mammal flora. They usually inhabit the
mouth, throat, and nasopharynx. Certain strains of S. mitis have the ability to
produce IgIA1 protease and bind salivary alpha-amylase, which are two properties
that are determinants for streptococcus viridans, which are a large group of
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generally non-pathogenic, commensal, streptococcal bacteria. Some S. mitis that
produce neuraminidase tend to colonize mucosal surfaces, although the production
of this enzyme is not required for successful colonization. However, neither
immunoglobulin A1 protease activity nor the ability to bind α-amylase from saliva
was a preferential characteristic of persistent genotypes. The major origin of new
clones occupied by S. mitis can be found in the respiratory tract.
DESCRIPTION AND SIGNIFICANCE:
Streptococcus mitis is prokaryotic because it is a bacterium that causes strep
throat. This is gram positive bacteria.
Streptococcus mitis are commensal bacteria that colonize hard surfaces in
the oral cavity such as dental hard tissues as well as mucous membranes and are
part of the oral flora. They are usually arranged in short chains in the shape of
cocci. These Gram-positive bacteria are not usually pathogenic but commonly
cause bacterial Endocarditis, which is the inflammation of an inner layer of the
heart. S. mitis are alpha hemolytic, meaning it can break down red blood cells. S.
mitis are not motile, do not form spores and lack group-specific antigens. S. mitis
live optimally at temperatures between 30 and 35 degrees Celsius, making them
mesophiles. They are facultative anaerobes, which is a bacterium that makes ATP
by aerobic respiration if oxygen is present but is also capable of switching to
fermentation in the absence of oxygen.
PATHOLOGY:
S. mitis is usually an etiologic agent in odontogenic infection and
Endocarditis and only in some cases have been acknowledged as respiratory
pathogens. The most common host is humans. The major interaction in the
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pathogenesis of infective Endocarditis is the direct binding of bacteria to platelets.
S. mitis is a commensal organism that is closely related to the pathogen
Streptococcus pneumoniae, the causative agent of otitis, pneumonia, sepsis and
meningitis. Homologous recombination between these species has been observed
and the transfer of genetic determinants from S. mitis to S. pneumoniae contributes
to penicillin resistance in the pathogen.
Numerous phages are known to carry determinants that increase virulence to
the bacterial host. These factors have been predominantly secreted toxins, such as
the streptococcal erythrogenic toxin, staphylococcal enterotoxin A, diphtheria
toxin, and cholera toxin. Other phage encoded virulence determinants include
extracellular enzymes such as staphylokinase and streptococcal hyaluronidase,
enzymes that alter the antigenic properties of the host strain, and outer membrane
proteins that increases serum resistance. It is likely that Pb1A and Pb1B bind
platelets directly, although the mechanism by which PblA and PblB mediate
platelet binding by S. mitis has not been illustrated. Thus, the encoding of PblA and
PblB by lysogenic SM1 may represent a class of phage-mediated virulence
determinants.
Streptococcus mitis is prevalent in the normal flora of the nasopharynx, the
female genital tract, gastrointestinal tract, and skin. Although it is usually
considered to have low virulence and Pathogenicity, Streptococcus mitis may
cause life-threatening infections, particularly Endocarditis. Meningitis with S.
mitis is rare, but has been described in individuals with previous spinal anesthesia,
neurosurgical procedure, malignancy, or neurological complications of
Endocarditis.
Streptococcus mitis found in the human mouth, throat, and nasopharynx;
ordinarily, it is not considered to be pathogenic, but this organism may be
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recovered from ulcerated teeth and sinuses, and blood and heart lesions in cases
of Sub-Acute Endocarditis.
Endocarditis is inflammation of the inside lining of the heart chambers and
heart valves (endocardium).
DOCKING:
INTRODUCTION:
In the field of molecular modeling, docking is a method which predicts the
preferred orientation of one molecule to a second when bound to each other to
form a stable complex. Knowledge of the preferred orientation in turn may be used
to predict the strength of association or binding affinity between two molecules
using for example scoring functions.
The associations between biologically relevant molecules such as proteins,
nucleic acids, carbohydrates, and lipids play a central role in signal transduction.
Furthermore, the relative orientation of the two interacting partners may affect the
type of signal produced (e.g., agonist vs. antagonism). Therefore docking is useful
for predicting both the strength and type of signal produced.
Docking is frequently used to predict the binding orientation of small
molecule drug candidates to their protein targets in order to in turn predict the
affinity and activity of the small molecule. Hence docking plays an important role
in the rational design of drugs. Given the biological and pharmaceutical
significance of molecular docking, considerable efforts have been directed towards
improving the methods used to predict docking.
MECHANISM OF DOCKING:
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To perform a docking screen, the first requirement is a structure of the
protein of interest. Usually the structure has been determined using a biophysical
technique such as x-ray crystallography, or less often, NMR spectroscopy. This
protein structure and a database of potential ligands serve as inputs to a docking
program. The success of a docking program depends on two components: the
search algorithm and the scoring function.
SEARCH ALGORITHM:
The search space in theory consists of all possible orientations and
conformations of the protein paired with the ligand. However, in practice with
current computational resources, it is impossible to exhaustively explore the search
space this would involve enumerating all possible distortions of each molecule
(molecules are dynamic and exist in an ensemble of conformational states) and all
possible rotational and translational orientations of the ligand relative to the protein
at a given level of granularity. Most docking programs in use account for a flexible
ligand, and several attempt to model a flexible protein receptor. Each "snapshot" of
the pair is referred to as a pose.
A variety of conformational search strategies have been applied to the ligand
and to the receptor. These include:
systematic or stochastic torsional searches about rotatable bonds
molecular dynamics simulations
genetic algorithms to "evolve" new low energy conformations
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LIGAND FLEXIBILITY:
Conformations of the ligand may be generated in the absence of the receptor
and subsequently docked or conformations may be generated on-the-fly in the
presence of the receptor binding cavity. Force field energy evaluation are most
often used to select energetically reasonable conformations, but knowledge-based
methods have also been used.
RECEPTOR FLEXIBILITY:
Computational capacity has increased dramatically over the last decade
making possible the use of more sophisticated and computationally intensive
methods in computer-assisted drug design. However, dealing with receptor
flexibility in docking methodologies is still a thorny issue. The main reason behind
this difficulty is the large number of degrees of freedom that have to be considered
in this kind of calculations. However, neglecting it, leads to poor docking results in
terms of binding pose prediction.
Multiple static structures experimentally determined for the same protein in
different conformations are often used to emulate receptor flexibility. Alternatively
rotamer libraries of amino acid side chains that surround the binding cavity may be
searched to generate alternate but energetically reasonable protein conformations.
SCORING FUNCTION:
The scoring function takes a pose as input and returns a number indicating
the likelihood that the pose represents a favorable binding interaction.
Most scoring functions are physics-based molecular mechanics force fields
that estimate the energy of the pose; a low (negative) energy indicates a stable
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system and thus a likely binding interaction. An alternative approach is to derive a
statistical potential for interactions from a large database of protein-ligand
complexes, such as the Protein Data Bank, and evaluate the fit of the pose
according to this inferred potential.
There are a large number of structures from X-ray crystallography for
complexes between proteins and high affinity ligands, but comparatively fewer for
low affinity ligands as the later complexes tend to be less stable and therefore more
difficult to crystallize. Scoring functions trained with this data can dock high
affinity ligands correctly, but they will also give plausible docked conformations
for ligands that do not bind. This gives a large number of false positive hits, i.e.,
ligands predicted to bind to the proteins that actually don’t when placed together in
a test tube.
One way to reduce the number of false positives is to recalculate the energy
of the top scoring poses using (potentially) more accurate but computationally
more intensive techniques such as Generalized Born or Poisson-Boltzmann
methods.
APPLICATIONS:
A binding interaction between a small molecule ligand and an enzyme protein
may result in activation or inhibition of the enzyme. If the protein is a receptor,
ligand binding may result in agonism or antagonism. Docking is most commonly
used in the field of drug design — most drugs are small organic molecules, and
docking may be applied to:
Hit identification – docking combined with a scoring function can be used
to quickly screen large databases of potential drugs in silico to identify
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molecules that are likely to bind to protein target of interest (see virtual
screening).
Lead optimization – docking can be used to predict in where and in which
relative orientation a ligand binds to a protein (also referred to as the binding
mode or pose). This information may in turn be used to design more potent
and selective analogs.
Bioremediation – Protein ligand docking can also be used to predict
pollutants that can be degraded by enzymes.
REVIEW OF LITERATURE
Streptococcus mitis are commensal bacteria that colonize hard surfaces in
the oral cavity such as dental hard tissues as well as mucous membranes and are
part of the oral flora. They are usually arranged in short chains in the shape of
cocci. These Gram-positive bacteria are not usually pathogenic but commonly
cause bacterial Endocarditis, which is the inflammation of an inner layer of the
heart. S. mitis are alpha hemolytic, meaning it can break down red blood cells.
S. mitis are not motile, do not form spores and lack group-specific antigens.
S. mitis live optimally at temperatures between 30 and 35 degrees Celsius, making
them mesophiles. They are facultative anaerobes, which is a bacterium that makes
1
ATP by aerobic respiration if oxygen is present but is also capable of switching to
fermentation in the absence of oxygen.
Streptococcus mitis is a bacterial species found in the human mouth, throat,
and nasopharynx; ordinarily, it is not considered to be pathogenic, but this
organism may be recovered from ulcerated teeth and sinuses, and blood and heart
lesions in cases of subacute Endocarditis.
Streptococcus mitis is the closest relative of the major human pathogen S.
pneumoniae. The 2,15 Mb sequence of the Streptococcus mitis B6 chromosome, an
unusually high-level beta-lactam resistant and multiple antibiotic resistant strain,
has now been determined to encode 2100 genes. The accessory genome is
estimated to represent over 40%, including 75 mostly novel transposases and IS,
the prophage φB6 and another seven phage related regions. Tetracycline resistance
mediated by Tn5801, and an unusual and large gene cluster containing three
aminoglycoside resistance determinants have not been described in other
Streptococcus spp.
Comparative genomic analyses including hybridization experiments on a S.
mitis B6 specific microarray reveal that individual S. mitis strains are almost as
distantly related to the B6 strain as S. pneumoniae. Both species share a core of
over 900 genes. Most proteins described as pneumococcal virulence factors are
present in S. mitis B6, but the three choline binding proteins PcpA, PspA and
PspC, and three gene clusters containing the hyaluronidase gene, ply and lytA, and
the capsular genes are absent in S. mitis B6 and other S. mitis as well and confirm
their importance for the pathogenetic potential of S. pneumoniae. Despite the close
relatedness between the two species, the S. mitis B6 genome reveals a striking X-
alignment when compared with S. pneumoniae.
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Streptococcus mitis is the closest relative of the major human pathogen S.
pneumoniae. The 2,15 Mb sequence of the Streptococcus mitis B6 chromosome,
an unusually high-level beta-lactam resistant and multiple antibiotic resistant
strain, has now been determined to encode 2100 genes. The accessory genome is
estimated to represent over 40%, including 75 mostly novel transposases and IS,
the prophage phiB6 and another seven phage related regions. Tetracycline
resistance mediated by Tn5801, and an unusual and large gene cluster containing
three aminoglycoside resistance determinants have not been described in other
Streptococcus spp.
Comparative genomic analyses including hybridization experiments on a S.
mitis B6 specific microarray reveal that individual S. mitis strains are almost as
distantly related to the B6 strain as S. pneumoniae. Both species share a core of
over 900 genes. Most proteins described as pneumococcal virulence factors are
present in S. mitis B6, but the three choline binding proteins PcpA, PspA and PspC,
and three gene clusters containing the hyaluronidase gene, ply and lytA, and the
capsular genes are absent in S. mitis B6 and other S. mitis as well and confirm their
importance for the pathogenetic potential of S. pneumoniae. Despite the close
relatedness between the two species, the S. mitis B6 genome reveals a striking X-
alignment when compared with S. pneumoniae.
The pneumococcal choline-containing teichoic acids are targeted by choline-
binding proteins (CBPs), major surface components implicated in the interaction
with host cells and bacterial cell physiology. CBPs also occur in closely related
commensal species, Streptococcus oralis and Streptococcus mitis, and many strains
of these species contain choline in their cell wall. Physiologically relevant CBPs
including cell wall lytic enzymes are highly conserved between Streptococcus
pneumoniae and S. mitis. In contrast, the virulence-associated CBPs, CbpA, PspA
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and PcpA, are S. pneumoniae specific and are thus relevant for the characteristic
properties of this species.
MOLECULE A: SANGUINARINE :
Sanguinarine is a quaternary ammonium salt from the group of
benzylisoquinoline alkaloids. It is extracted from some plants, including bloodroot
(Sanguinaria canadensis), Mexican prickly poppy Argemone mexicana,
Chelidonium majus and Macleaya cordata. It is also found in the root, stem and
leaves of the opium poppy but not in the capsule.
Sanguinarine is a toxin that kills animal cells through its action on the Na+-
K+-ATPase transmembrane protein. Epidemic dropsy is a disease that results from
ingesting sanguinarine.
Figure 8: Sanguinarine
MOLECULE B: COPTISINE:
Coptisine is an alkaloid found in Chinese goldthread (Coptis chinensis).
Famous for the bitter taste that it produces, it is used in Chinese herbal medicine
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along with the related compound berberine for treating digestive disorders caused
by bacterial infections.Also found in Greater Celandine and has also been detected
in Opium.
Coptisine has been found to reversibly inhibit Monoamine oxidase. In mice,
pointing to a potential role as a natural antidepressant. However, this may also
imply a hazard for those taking other medications or with a natural functional
disorder in Monoamine oxidase A.
Coptisine was found to be toxic to larval brine shrimp and a variety of
human cell lines, potentially implying a therapeutic effect on cancer or
alternatively a generally toxic character. The same authors illustrate a four-step
process to produce Coptisine from Berberine
Figure 9: Coptisine
MOLECULE C: BERBARINE:
Berberine is a quaternary ammonium salt from the protoberberine group of
isoquinoline alkaloids. It is found in such plants as Berberis (e.g. Berberis
aquifolium (Oregon grape), Berberis vulgaris(Barberry), and Berberis aristata
(Tree Turmeric), Berberis aquifolium,Hydrastis canadensis (Goldenseal),
Phellodendron amurense and Coptis chinensis and Tinospora cordifolia, and to a
1
smaller extent in Argemone mexicana (Prickly Poppy) and Eschscholzia
californica (Californian Poppy). Berberine is usually found in the roots, rhizomes,
stems, and bark.
Berberine is strongly yellow colored, which is why in earlier times Berberis
species were used to dye wool, leather and wood. Wool is still today dyed with
berberine in northern India. Under ultraviolet light, berberine shows a strong
yellow fluorescence. Because of this it is used in histology for staining heparin in
mast cells. As a natural dye, berberin has a Colour Index (CI) of 75160.
Figure 10: Berbarine
MOLECULE D: YOHIMBINE:
Yohimbine is an alkaloid with stimulant and aphrodisiac effects found
naturally in Pausinystalia yohimbe (Yohimbe). It is also found naturally in
Rauwolfia serpentina (Indian Snakeroot), along with several other active alkaloids.
Yohimbine has been used as both an over-the-counter dietary supplement in herbal
extract form and prescription medicine in pure form for the treatment of sexual
dysfunction. Yohimbine was explored as a remedy for type 2 diabetes in animal
and human models carrying polymorphisms of the α2A-adrenergic receptor gene.
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Figure 11: Yohimbine
MOLECULE E: KAEMPFEROL:
Kaempferol is a natural flavonol, a type of flavonoid, that has been isolated
from tea, broccoli, Delphinium, Witch-hazel, grapefruit, brussels sprouts, apples
and other plant sources. Kaempferol is a yellow crystalline solid with a melting
point of 276-278 °C. It is slightly soluble in water but soluble in hot ethanol and
diethyl ether.
Many glycosides of kaempferol, such as kaempferitrin and astragalin, have
been isolated as natural products from plants. Kaempferol consumption in tea and
broccoli has been associated with reduced risk of heart disease.
Kaempferol is what gives the flowers of Acacia decurrens and Acacia
longifolia their color. The compound has antidepressant properties.
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Figure 12: Kaempferol
MATERIALS AND METHODS
The complete proteome sequences of any bacteria or pathogen and Homo
sapiens were retrieved from the Uniprot protein resource (http://www.uniprot.org/).
Each protein sequence of pathogen was searched for sequence homology with
human proteome using BLAST program available at
NCBI(http://blast.ncbi.nlm.nih.gov/Blast.cgi)16, bit score cut off <100 and
minimum expectation value (E-value) cut off E-10 were taken to identify
homology exhibiting significant differences with their human counterpart. Proteins
sequences less than 100 amino acids in length were unlikely to represent essential
to parasite hence such sequences were excluded from analysis.
Non human homologs proteins were then searched against DEG
(http://tubic.tju.edu.cn/deg/) which is a database of essential genes and proteins
which are considered a foundation of life and therefore are likely to be common to
all cells. If we BLAST the protein sequences against DEG and homologous
proteins are found, it is possible that the queried proteins are also essential to an
organism. Non human homologs proteins of parasite, which are possibly unique to
pathogen, were then subjected to identify its homolog essential proteins using
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DEG, standard BLASTX program was used. The selection criterion for essential
homologs was that it should show similarity with any essential gene and proteins
present in DEG.
For short listing essential proteins, bit score cut off >100 and E-value <E-10
were considered. The function and sub cellular localization of each non
homologous protein is identified by using online sub cellular localization
prediction tools, CELLO (http://cello.life.nctu.edu.tw/), PSLpred
(http://www.imtech.res.in/raghava/pslpred/), and SOSUI server
(http://bp.nuap.nagoyau.ac.jp/sosui/). These tools utilize various protein properties
such as amino acids properties, dipepetide composition, physiochemical properties,
and evolutionary information using PSI BLAST. Membrane localized proteins
were identified and listed as putative candidate vaccine targets. By using prosite
database, functional domains are identified from non homologs proteins and
biological as well as molecular function is taken from Swissprot database by
querying protein name and accession no.
DATABASE:
NCBI (NATIONAL CENTRE FOR BIOTECHNOLOGICAL
INFORMATION):
The National Center for Biotechnology Information (NCBI) is part of the
United States National Library of Medicine (NLM), a branch of the National
Institutes of Health. The NCBI is located in Bethesda, Maryland(38°59′42″N
77°05′58″W / 38.994994°N 77.099339°W Coordinates : 38°59′42″N 77°05′58″W /
38.994994°N 77.099339°W ) and was founded in 1988 through legislation
sponsored by Senator Claude Pepper. The NCBI houses genome sequencing data
in GenBank and an index of biomedical research articles in PubMed Central and
1
PubMed, as well as other information relevant to biotechnology. All these
databases are available online through the Entrez search engine.
NCBI is directed by David Lipman, one of the original authors of the
BLAST sequence alignment program and a widely respected figure in
Bioinformatics. He also leads an intramural research program, including groups led
by Stephen Altschul (another BLAST co-author), David Landsman, and Eugene
Koonin (a prolific author on comparative genomics).
GENBANK:
The NCBI has had responsibility for making available the GenBank DNA
sequence database since 1992. GenBank coordinates with individual laboratories
and other sequence databases such as those of the European Molecular Biology
Laboratory (EMBL) and the DNA Data Bank of Japan (DDBJ).
Since 1992, NCBI has grown to provide other databases in addition to
GenBank. NCBI provides Online Mendelian Inheritance in Man, the Molecular
Modeling Database (3D protein structures), dbSNP a database of single-nucleotide
polymorphism s , the Unique Human Gene Sequence Collection, a Gene Map of the
human genome, a Taxonomy Browser, and coordinates with the National Cancer
Institute to provide the Cancer Genome Anatomy Project. The NCBI assigns a
unique identifier (Taxonomy ID number) to each species of organism.
The NCBI has software tools that are available by WWW browsing or by
FTP. For example, BLAST is a sequence similarity searching program. BLAST
can do sequence comparisons against the GenBank DNA database in less than 15
seconds.
BLAST (BASIC LOCAL ALIGNMENT SEARCH TOOL):
1
In bioinformatics, Basic Local Alignment Search Tool or BLAST, is an
algorithm for comparing primary biological sequence information, such as the
amino-acid sequences of different proteins or the nucleotides of DNA sequences.
A BLAST search enables a researcher to compare a query sequence with a library
or database of sequences, and identify library sequences that resemble the query
sequence above a certain threshold. Different types of BLASTs are available
according to the query sequences. For example, following the discovery of a
previously unknown gene in the mouse, a scientist will typically perform a BLAST
search of the human genome to see if humans carry a similar gene; BLAST will
identify sequences in the human genome that resemble the mouse gene based on
similarity of sequence. The BLAST program was designed by Eugene Myers,
Stephen Altschul, Warren Gish, David J. Lipman, and Webb Miller at the NIH and
was published in the Journal of Molecular Biology in 1990.
INPUT:
Input sequences are in FASTA format or Genbank format.
OUTPUT:
BLAST output can be delivered in a variety of formats. These formats
include HTML, plain text, and XML formatting. For NCBI’s web-page, the default
format for output is HTML. When performing a BLAST on NCBI, the results are
given in a graphical format showing the hits found, a table showing sequence
identifiers for the hits with scoring related data, as well as alignments for the
sequence of interest and the hits received with corresponding BLAST scores for
these. The easiest to read and most informative of these is probably the table.
If you are searching a proprietary sequence or simply one that is unavailable
in databases available to the public through sources such as NCBI, there is a
BLAST program available for download to any computer, at no cost. This can be
1
found at BLAST+ executables. There are also commercial programs available for
purchase. Databases can be found from the NCBI site, as well as from Index of
BLAST databases (FTP).
FIGURE 13: HOME PAGE OF NCBI
DEG DATABASE:
Essential genes are those indispensable for the survival of an organism, and
therefore are considered a foundation of life. DEG hosts records of currently
available essential genes among a wide range of organisms. For prokaryotes, DEG
contains essential genes in more than 10 bacteria, such as E. coli, B. subtilis, H.
pylori, S. pneumoniae, M. genitalium and H. influenzae, whereas for eukaryotes,
1
DEG contains those in yeast, humans, mice, worms, fruit flies, zebra fish and the
plant A. thaliana. Users can Blast query sequences against DEG, and can also
search for essential genes by their functions and names. Essential gene products
comprise excellent targets for antibacterial drugs. Essential genes in a bacterium
constitute a minimal genome, forming a set of functional modules, which play key
roles in the emerging field, synthetic biology.
Essential genes are genes that are indispensable to support cellular life.
These genes constitute a minimal gene set required for a living cell. We have
constructed a Database of Essential Genes (DEG), which contains all the essential
genes that are currently available. The functions encoded by essential genes are
considered a foundation of life and therefore are likely to be common to all cells.
Users can BLAST the query sequences against DEG. If homologous genes are
found, it is possible that the queried genes are also essential. Users can search for
essential genes by their function or name. Users can also browse and extract all the
records in DEG. Essential gene products comprise excellent targets for
antibacterial drugs. Analysis of essential genes could help to answer the question
of what are the basic functions necessary to support cellular life. DEG is freely
accessible from the website http://tubic.tju.edu.cn/deg/ .
Essential genes are genes that are indispensable to support cellular life.
These genes constitute a minimal gene set required for a living cell. Therefore, the
functions encoded by this gene set are essential and could be considered as a
foundation of life itself. The definition of the minimal gene set needed to sustain a
living cell is of considerable interest not only because it represents a fundamental
question in biology, but also because it has much significance in practical use. For
example, since most antibiotics target essential cellular processes, essential gene
products of microbial cells are promising new targets for antibacterial drugs.
1
The determination of the minimal gene set for bacteria has only been
possible with the advent of the completion of many whole genome sequencing
projects and the genome-scale gene inactivation technology. Consequently,
essential genes have been determined in a number of different organisms. Essential
genes have been determined in Staphylococcus aureus by an antisense RNA
technique, in Mycoplasma genitalium by transposon mutagenesis, in Haemophilus
influenzae by high-density transposon mutagenesis, in Vibrio cholerae by a
mariner-based transposon, in yeast by genetic foot printing, and in M.genitalium
and H.influenzae by comparative genomics.
We have constructed a Database of Essential Genes (DEG) that contains all
the essential genes currently available. These genes include the essential genes
identified in the genomes of M.genitalium, H.influenzae, V.cholerae, S.aureus,
Escherichia coli and Saccharomyces cerevisiae. The essential genes in the E.coli
genome were extracted from the web site
http://magpie.genome.wisc.edu/~chris/essential.html, in which the essential genes
are collected from a large number of related references. The essential genes in
yeast genome were extracted from the yeast genome database
(http://www.mips.biochem.mpg.de/proj/yeast), which is maintained by the Munich
Information Center for Protein Sequences
Each entry of essential genes has a unique DEG identification number, gene
reference number, gene function and sequence. All information is stored and
operated by using an open-source database management system, MySQL. Users
can browse and extract all the records of these entries. In addition, users can also
search DEG by gene function or name. Furthermore, we have installed the BLAST
program locally. Therefore, users can BLAST the query sequences against all the
essential gene sequences in DEG.
1
One of the applications is the prediction of essential genes based on
homologous sequence search against DEG. The functions encoded by essential
genes are considered to be generally essential for all cells. It is even believed that
some basic functions and principles are common to all cellular life on this planet.
Therefore, if the query sequences compared using BLAST have homologous genes
in DEG, it is likely that the queried genes are also essential. In addition, by
performing the BLAST search against DEG for all the protein-coding genes in a
genome, it is possible to define the putative essential genes for the proteomes of
newly sequenced genomes. However, caution must be taken in interpreting the
BLAST results, since many essential genes are essential only in given growth
conditions, such as in rich or minimal medium.
Another application is that by analyzing all the essential genes in DEG,
some principles or regulations could be found to answer the question of what are
the basic functions necessary to support cellular life. Those principles could lead to
the development of new algorithms to predict essential genes. Some functions
encoded by essential genes are expected, such as DNA replication, gene
transcription, protein synthesis, energy production and cell division. Some
essential genes, however, are somewhat unexpected, such as Embden–Meyerhof–
Parnas pathway genes and a purine biosynthesis gene. Analysis of DEG, which has
all essential genes among different organisms, could help to classify those
‘unexpected’ essential genes.
Currently some essential gene projects are still ongoing and the
identification of more essential genes is expected. DEG will be updated
periodically to include more entries upon the availability of newly identified
essential genes. We plan to integrate more information about experimental
methods for each entry. In the next version of DEG, we also plan to include the
1
essential genes of vertebrates, such as mouse. We welcome users’ comments,
corrections and new information, which will be used for updating.
DEG is freely available at the web site http://tubic.tju.edu.cn/deg/, and
should be cited with the present publication as reference.
FIGURE 14: SEQUENCE SUBMISSION IN DEG DATABASE
CELLO PREDICTION:
1
Protein sub-cellular localization prediction involves the computational
prediction of where a protein resides in a cell. Prediction of protein subcellular
localization is an important component of bioinformatics-based prediction of
protein function and genome annotation, and it can aid the identification of drug
targets.
Most eukaryotic proteins are encoded in the nuclear genome and synthesized
in the cytosol, but many need to be further sorted before they reach their final
destination. For prokaryotes, proteins are synthesized in the cytoplasm and some
must be targeted to other locations such as to a cell membrane or the extracellular
environment. Proteins must be localized at their appropriate subcellular
compartment to perform their desired function.
Experimentally determining the subcellular localization of a protein is a
laborious and time consuming task. Through the development of new approaches
in computer science, coupled with an increased dataset of proteins of known
localization, computational tools can now provide fast and accurate localization
predictions for many organisms. This has resulted in subcellular localization
prediction becoming one of the challenges being successfully aided by
bioinformatics. Many protein subcellular localization prediction methods now
exceed the accuracy of some high-throughput laboratory methods for the
identification of protein subcellular localization.[1]
Particularly, some predictors developed recently can be used to deal with
proteins that may simultaneously exist, or move between, two or more different
subcellular locations.
APPLICATIONS:
Determining subcellular localization is important for understanding protein
function and is a critical step in genome annotation. Knowledge of the subcellular
1
localization of a protein can significantly improve target identification during the
drug discovery process. For example, secreted proteins and plasma membrane
proteins are easily accessible by drug molecules due to their localization in the
extracellular space or on the cell surface.
Bacterial cell surface and secreted proteins are also of interest for their
potential as vaccine candidates or as diagnostic targets. Aberrant subcellular
localization of proteins has been observed in the cells of several diseases, such as
cancer and Alzheimer’s disease. Secreted proteins from some archaea that can
survive in unusual environments have industrially important applications.
CELLO is a multi-class SVM classification system. CELLO uses 4 types of
sequence coding schemes: the amino acid composition, the di-peptide composition,
the partitioned amino acid composition and the sequence composition based on the
physico-chemical properties of amino acids. We combine votes from these
classifiers and use the jury votes to determine the final assignment. The general
architecture of our predictive system is shown below.
1
FIGURE 15: GENERAL ARCHITECTURE OF OUR PREDICTIVE SYSTEM
FIGURE 16: HOME PAGE OF CELLO
1
FIGURE 17: RESULT PAGE OF CELLO
1
PROTEIN DATA BANK:
FIGURE 18: HOME PAGE OF PDB
The Protein Data Bank (PDB) is a repository for the 3-D structural data of
large biological molecules, such as proteins and nucleic acids. The data is either
obtained by X-ray crystallography or NMR spectroscopy and submitted by
biologists and biochemists from around the world, which are freely accessible on
the Internet via the websites of its member organizations (PDBe, PDBj, and
RCSB). The PDB is overseen by an organization called the Worldwide Protein
Data Bank, wwPDB.
1
The PDB is a key resource in areas of structural biology, such as structural
genomics. Most major scientific journals, and some funding agencies, such as the
NIH in the USA, now require scientists to submit their structure data to the PDB. If
the contents of the PDB are thought of as primary data, then there are hundreds of
derived (i.e., secondary) databases that categorize the data differently. For
example, both SCOP and CATH categorize structures according to type of
structure and assumed evolutionary relations; GO categorize structures based on
genes.
HISTORY:
The PDB originated as a grassroots. In 1971, Walter Hamilton of the
Brookhaven National Laboratory agreed to setup the data bank at Brookhaven.
Upon Hamilton's death in 1973, Dr. Tom Koeztle took over direction of the PDB
for the subsequent 20 years. In January 1994, Dr. Joel Sussman of Israel's
Weizmann Institute of Science was appointed head of the PDB. In October 1998,
the PDB was transferred to the Research Collaboratory for Structural
Bioinformatics (RCSB); the transfer was completed in June 1999. The new
director was Dr. Helen M. Berman of Rutgers University (one of the member
institutions of the RCSB). In 2003, with the formation of the wwPDB, the PDB
became an international organization. The founding members are PDBe (Europe),
RCSB (USA), and PDBj (Japan). The BMRB joined in 2006. Each of the four
members of wwPDB can act as deposition, data processing and distribution centers
for PDB data. The data processing refers to the fact that wwPDB staff review and
annotates each submitted entry. The data are then automatically checked for
plausibility (the source code for this validation software has been made available to
the public at no charge).
1
CONTENTS:
The PDB database is updated weekly (on Tuesday). Likewise, the PDB
Holdings List is also updated weekly. As of 8 March 2011, the breakdown of
current holdings was as follows:
Experimental
MethodProteins Nucleic Acids
Protein/Nucleic Acid
complexesOther Total
X-ray diffraction 58192 1262 2822 17 62293
NMR 7686 941 168 7 8802
Electron microscopy 245 22 86 0 353
Hybrid 28 3 1 1 33
Other 132 4 5 13 154
Total: 66283 2232 3082 38 71635
51,697 structures in the PDB have a structure factor file.
6,101 structures have an NMR restraint file.
20 structures in the PDB have a chemical shifts file.
These data show that most structures are determined by X-ray diffraction,
but about 15% of structures are now determined by protein NMR. When using X-
ray diffraction, approximations of the coordinates of the atoms of the protein are
obtained, whereas estimations of the distances between pairs of atoms of the
protein are found through NMR experiments. Therefore, the final conformation of
the protein is obtained, in the latter case, by solving a distance geometry problem.
A few proteins are determined by cryo-electron microscopy.
1
The significance of the structure factor files, mentioned above, is that, for
PDB structures determined by X-ray diffraction that have a structure file, the
electron density map may be viewed. The data of such structures is stored on the
"Electron Density Server", where the electron maps can be viewed.
In the past the number of structures in the PDB has grown at an
approximately exponential rate. However, since 2007 the rate of accumulation of
new proteins appears to have plateaued, with 7263 proteins added in 2007, 7073 in
2008, 7448 in 2009, and 7971 in 2010.
FILE FORMAT:
The file format initially used by the PDB was called the PDB file format.
This original format was restricted by the width of computer punch cards to 80
characters per line. Around 1996, the "macromolecular Crystallographic
Information file" format, mmCIF, started to be phased in. An XML version of this
format, called PDBML, was described in 2005. The structure files can be
downloaded in any of these three formats. In fact, individual files are easily
downloaded into graphics packages using web addresses:
For PDB format files, use, e.g., http://www.pdb.org/pdb/files/4hhb.pdb.gz or
http://pdbe.org/download/4hhb
For PDBML (XML) files, use, e.g.,
http://www.pdb.org/pdb/files/4hhb.xml.gz or http://pdbe.org/pdbml/4hhb
The "4hhb" is the PDB identifier. Each structure published in PDB receives
a four-character alphanumeric identifier, its PDB ID. (This cannot be used as an
identifier for biomolecules, because often several structures for the same molecule
1
—in different environments or conformations—are contained in PDB with
different PDB IDs.)
VIEWING THE DATA:
The structure files may be viewed using one of several open source
computer programs. Some other free, but not open source programs include VMD,
MDL Chime, Swiss-PDB Viewer, StarBiochem (a Java-based interactive
molecular viewer with integrated search of protein databank), Sirius, and
VisProt3DS (a tool for Protein Visualization in 3D stereoscopic view in anaglyth
and other modes). The RCSB PDB website contains an extensive list of both free
and commercial molecule visualization programs and web browser plug-ins.
PUBCHEM:
FIGURE 19: HOME PAGE OF PUBCHEM
1
PubChem is a database of chemical molecules and their activities against
biological assays. The system is maintained by the National Center for
Biotechnology Information (NCBI), a component of the National Library of
Medicine, which is part of the United States National Institutes of Health (NIH).
PubChem can be accessed for free through a web user interface.
Millions of compound structures and descriptive datasets can be freely
downloaded via FTP. PubChem contains substance descriptions and small
molecules with fewer than 1000 atoms and 1000 bonds. The American Chemical
Society tried to get the U.S. Congress to restrict the operation of PubChem,
because they claim it competes with their Chemical Abstracts Service. More than
80 database vendors contribute to the growing PubChem database.
Compounds, 31 million entries, contain pure and characterized chemical
compounds.
Substances, 75 million entries, contain also mixtures, extracts, complexes
and uncharacterized substances.
Bioassay, bioactivity results from 1644 high-throughput screening programs
with several million values.
SEARCHING:
Searching the databases is possible for a broad range of properties including
chemical structure, name fragments, chemical formula, molecular weight, XLogP,
and hydrogen bond donor and acceptor count.
PubChem contains its own online molecule editor with SMILES/SMARTS
and InChI support that allows the import and export of all common chemical file
formats to search for structures and fragments.
1
Each hit provides information about synonyms, chemical properties,
chemical structure including SMILES and InChI strings, bioactivity, and links to
structurally related compounds and other NCBI databases like PubMed.
In the text search form the database fields can be searched by adding the
field name in square brackets to the search term. A numeric range is represented by
two numbers separated by a colon. The search terms and field names are case-
insensitive. Parentheses and the logical operators AND, OR, and NOT can be used.
AND is assumed if no operator is used.
SOFTWARE:
MARVIN SKETCH:
MarvinSketch is an advanced, Java based chemical editor for drawing
chemical structures, queries and reactions. It has a rich (and growing) list of editing
features, is chemically aware and is able to call ChemAxon's structure based
calculation plugins for structures on the canvas.
RICH EDITING:
wide range of file types supported: MOL, MOL2, SDF, RXN, RDF
(V2000/V3000), SMILES, SMARTS/SMIRKS (recursive), MRV, InChi,
CML, PDB etc
Copy and paste between different editors
Abbreviated groups
Pre-loaded structure templates and "My Templates"
1
3D editing
3D geometry and conformer generation
2D cleaning and conformer generation
Advanced query features (generic atoms and bonds, atom lists/not lists,
query properties, pseudo atoms, multiple groups, Link nodes, etc)
Creating and editing molecule sets (without a database)
Multipage documents and printing support
Drawing and formatting shapes, arrows and text boxes
Structure annotation
User definable customisable styles (colours, structure representations, etc)
CHEMICALLY AWARE :
Structure based calculations can be called directly from MarvinSketch. For a
complete listing of functions please see the Calculator Plugins section
Error checking (valence and reaction error checking)
Structure query design (R-logic, SMARTS properties, etc)
Isotopes, charges radicals, lone pairs and aliases are supported
Manual and automapping for reaction drawing
Advanced stereochemistry functions (E/Z double bonds, R/S chirality,
ABS/OR/AND enhanced stereo, etc)
1
CROSS PLATORM DELIVERY
Marvin is Java based and so can run on all major operating systems,
ChemAxon make Marvin available in the following distributions:
o Java Applets can easily be implemented into Java enabled web pages
without the need for the user to install software or plugins
o Java Beans can be directly installed to give standalone desktop
applications
o Java Web Start enables web delivery of end user applications
.NET support
1
FIGURE 20: MARVIN SKETCH
AUTO DOCK:
AutoDock is a molecular modeling simulation software. Since 2009, it has
been open source and is free for non-commercial usage. It is especially effective
for Protein-ligand docking.
AutoDock is one of the mostly cited docking software in the research
community. It is a base for the FightAIDS@Home project run by World
1
Community Grid. In February 2007, a search of the ISI Citation Index showed
more than 1100 publications have been cited using the primary AutoDock method
papers. As of 2009, this number surpassed 1200.
AutoDock is currently maintained by The Scripps Research Institute and
Olson Laboratory. AutoDock is a suite of automated docking tools. It is designed
to predict how small molecules, such as substrates or drug candidates, bind to a
receptor of known 3D structure.
Current distributions of AutoDock consist of two generations of software:
AutoDock 4 and AutoDock Vina.
AutoDock 4 actually consists of two main programs: autodock performs the
docking of the ligand to a set of grids describing the target protein; autogrid pre-
calculates these grids.
In addition to using them for docking, the atomic affinity grids can be
visualised. This can help, for example, to guide organic synthetic chemists design
better binders.
AutoDock Vina does not require choosing atom types and pre-calculating grid maps for
them. Instead, it calculates the grids internally, for the atom types that are needed, and it does this
virtually instantly.
We have also developed a graphical user interface called AutoDockTools, or ADT
for short, which amongst other things helps to set up which bonds will treated as
rotatable in the ligand and to analyze dockings. AutoDock has applications in:
X-ray crystallography;
structure-based drug design;
lead optimization;
virtual screening (HTS);
1
combinatorial library design;
protein-protein docking;
chemical mechanism studies.
AutoDock 4 is free and is available under the GNU General Public License.
AutoDock Vina is available under the Apache license, allowing commercial and
non-commercial use and redistribution. Click on the "Downloads" tab. And Happy
Docking!
WHAT IS AUTODOCK VINA?
AutoDock Vina is a new generation of docking software from the Molecular
Graphics Lab. It achieves significant improvements in the average accuracy of the
binding mode predictions, while also being up to two orders of magnitude faster
than AutoDock 4.
Because the scoring functions used by AutoDock 4 and AutoDock Vina are
different and inexact, on any given problem, either program may provide a better
result.
PROGRAMS:
AutoDock consists of two main programs:
AutoDock for docking of the ligand to a set of grids describing the target
protein;
AutoGrid for pre-calculating these grids.
1
AutoDock has an improved version, AutoDock Vina with has an improved
local search routine and allows the use of multicore/multi-CPU computer setups.
Usage of AutoDock has contributed to the discovery of several drugs, including
HIV1 integrate inhibitors.
THIRD PARTY IMPROVEMENTS:
As an Open source project, AutoDock has gained several third party
improved versions such as:
GPU improved calculation routines
SSE improved calculation routines
Integration within bigger projects: OFF-TARGET PIPELINE
FIGURE 21: AUTO DOCK
1
LIPINSKI’S RULE:
Lipinski's Rule of Five is a rule of thumb to evaluate drug likeness, or
determine if a chemical compound with a certain pharmacological or biological
activity has properties that would make it a likely orally active drug in humans.
The rule was formulated by Christopher A. Lipinski in 1997, based on the
observation that most medication drugs are relatively small and lipophilic
molecules.
The rule describes molecular properties important for a drug's
pharmacokinetics in the human body, including their absorption, distribution,
metabolism, and excretion ("ADME"). However, the rule does not predict if a
compound is pharmacologically active.
The rule is important for drug development where a pharmacologically
active lead structure is optimized step-wise for increased activity and selectivity, as
well as drug-like properties as described by Lipinski's rule.
Lipinski's rule says that, in general, an orally active drug has no more than
one violation of the following criteria:
Not more than 5 hydrogen bond donors (nitrogen or oxygen atoms with one
or more hydrogen atoms).
Not more than 10 hydrogen bond acceptors (nitrogen or oxygen atoms).
A molecular weight not greater than 500 Daltons.
An octanol-water partition coefficient log P not greater than 5.
CPH MODEL:
1
CPH models-3.0 is a web-server predicting protein 3D-structure by use of
single template homology modeling. The server employs a hybrid of the scoring
functions of CPHmodels-2.0 and a novel remote homology-modeling algorithm. A
query sequence is first attempted modeled using the fast CPHmodels-2.0 profile-
profile scoring function suitable for close homology modeling. The new
computational costly remote homology-modeling algorithm is only engaged
provided that no suitable PDB template is identified in the initial search.
CPHmodels-3.0 was benchmarked in the CASP8 competition and produced
models for 94% of the targets (117 out of 128), 74% were predicted as high
reliability models (87 out of 117). These achieved an average RMSD of 4.6? when
superimposed to the 3D-structure. The remaining 26% low reliably models (30 out
of 117) could superimpose to the true 3D-structure with an average RMSD of 9.3?.
These performance values place the CPHmodels-3.0 method in the group of high
performing 3D-prediction tools. Beside its accuracy, one of the important features
of the method is its speed. For most queries, the response time of the server is less
than 20 minutes. The web server is available at
http://www.cbs.dtu.dk/services/CPHmodels/.
Sequence profiles have a broad application in field of bioinformatics
prediction algorithms dating back to the pioneering work by Rost and Sanders. The
field of protein structure prediction has largely benefited from this work, and most
high performing algorithms for protein homology modeling use sequence profiles
as their main vehicle. Likewise has prediction of local protein structural features
been demonstrated to improve when sequence profile are used to represent the
protein sequences. Here, we develop a scoring scheme for remote homology
modeling building on these findings. Two protein sequences are aligned using
local sequence alignment with an amino acids scoring matrix constructed
1
combining sequence profiles, and local protein structural features like secondary
structure and relative surface accessibility. For the query sequence where the
structure is unknown, predicted local features are used. For the template PDB
structure averages of predicted and DSSP assigned local features are used.
Secondary structure predictions are performed using the artificial neural network
approach described by Petersen et al, and relative surface exposure predicted using
a doubled structure neural network approach as described by Petersen et al..
Each element in the alignment function (profile, secondary structure, and
relative surface exposure) where scored using a log-likelihood approach where the
likelihood was estimated as (sum p_ia p_ja)/O , where the sum is over the different
classes of the given feature (amino acids, secondary structure elements, and
exposure class), pia is the probability of observing that given feature class a in
protein i, and O is the odds value definition a background score for a given feature.
The log-likelihood odds values, relative weights on the three parts of the alignment
function as well as the two affine gap-penalty values were optimized using a set of
structurally superimposable sequence pairs with low mutual sequence similarity.
Relating a sequence alignment score to a likelihood of the two sequences been
structurally similar is not straightforward. The protein length and protein amino
acids composition among other things determine how a protein sequence will score
against other protein sequences. We design a double-sided baseline corrected
scoring scheme to allow for a direct interpretation of the alignment scoring values
in terms of structural similarity likelihood.
Each sequence is aligned against a set of 1500 sequence representatives with
internal low sequence similarity and broad structural diversity. A baseline
correction for the sequence is estimated from a least square fit of the alignment
scores to the logarithm of the template query sequence. Next, a mean score and
1
standard deviation is estimated from the baseline correction score distribution after
removal of outliers. The baseline fit, mean score and standard deviation values for
the two sequences are next used to determine the significance of a given alignment
score. This significance score is calculated as Z=(2 ZQ ZT)/(ZQ+ZT), where ZQ
and ZT are the baseline corrected Z-score values for the alignment score for the
query (Q) and template (T) sequences, respectively. A curated version of the PDB
where the SEQRES sequence was aligned to the PDB sequence with atom
coordinates was used as template database. Sequence profiles were generated using
PSI-Blast with default parameters for three iterations and an e-value cut-off of
0.001. Large scale benchmarking and cross validation demonstrates that the use of
local structure predictions to guide the pairwise sequence alignment significantly
improved the alignment quality beyond that obtained using sequence profiles only.
Further, the use of double-sided baseline correction improved the specificity of the
method for template recognition.
USAGE INSTRUCTIONS:
1.A. SPECIFY THE INPUT SEQUENCE
All the input sequences must be in one-letter amino acid code. The allowed
alphabet (not case sensitive) is as follows:
A C D E F G H I K L M N P Q R S T V W Y
Please note that the sequences containing other symbols e.g. X (unknown)
will be discarded before processing. The sequences can be input in the following
two ways:
1.B. SPECIFY THE INPUT SEQUENCE
All the input sequences must be in one-letter amino acid code. The allowed
alphabet (not case sensitive) is as follows:
1
A C D E F G H I K L M N P Q R S T V W Y and X (unknown)
All the other symbols will be converted to X before processing. The
sequences can be input in the following two ways:
Paste a single sequence (just the amino acids) or a number of sequences in
FASTA format into the upper window of the main server page.
Select a FASTA file on your local disk, either by typing the file name into
the lower window or by browsing the disk.
Both ways can be employed at the same time: all the specified sequences
will be processed. However, there may be not more than 10 sequences in toto in
one submission. The sequences shorter than 15 or longer than 4000 amino acids will
be ignored.
1
OUTPUT FORMAT:
1. DESCRIPTION:
Example of output is found below. The output is divided into the
following sections:
QUERY SEQUENCE: In this section the query sequence that you submitted
are shown in fasta format.
SEARCHING FOR TEMPLATE: The template for building the model is
sought by iteratively building up a profile by aligning the query sequence to a
non redundant database of protein sequences and then searching a database of
proteins with known structure (Pdb) to find a suitable template for making a
model.
RETRIEVING TEMPLATE: In this section the Pdb entry name and the
chain identifier are listed for the template that are used to construct the
model.
MAKING PROFILE-PROFILE ALIGNMENT: In this section the score
from the profile profile alignment (in bits) and the percentage sequence
identity between query and template are shown together with the alignment
in "Blast-like" format.
MODELING: By clicking on the link "model.pdb" you can download the
coordinates in pdb format to your own computer.
PDB3D: If you have an java enabled browser the C-alpha trace of the model
will be shown. You can rotate it by klicking on it with the left mouse button
and holding it down while mooving the mouse. The right mouse button can
1
be used to scale the model.
FIGURE 22: HOME PAGE OF CPH MODELS
2. CUSTOMIZE YOUR RUN:Click on the button labelled "Example button".
3. SUBMIT THE JOB:
Click on the "Submit" button. The status of your job (either 'queued' or
'running') will be displayed and constantly updated until it terminates and the
server output appears in the browser window.
1
COLLECTION OF MOLECULES VIA LITERATURE STUDIES:
Many herbs and oils are natural antibacterial agents and may be used as teas,
skin washes, made into salves. Some of the most effective herbs contain berberine
- goldenseal and Oregon grape root are two. Herbs that contain essential oils are
antibacterial and antiseptic.
There are a number of natural antibacterial products which can be used to
fight bacteria without resorting to harsh chemicals and synthetic products. Many
natural antibacterials can be used in cleaning solutions around the house, and they
can also be added to the laundry, or blended into soaps used to wash the hands and
body. Some people also find that ingesting natural antibacterial products can help
to fight off infection, although it is a good idea to see a doctor for a suspected
bacterial infection to confirm that the bacteria are susceptible to a natural
antibacterial product.
Many essential oils are naturally antibacterial, including peppermint, tea tree
oil, oregano, lemon, thyme, and eucalyptus. Essential oils are not safe to consume
or to apply undiluted to the skin, but they can be added to household cleaning
solutions, soap, and loads of laundry. It is important to obtain high grade essential
oils, with only a few drops being needed in a cleaning solution. Consumers should
also be aware that essential oils do not kill 100% of bacteria, although many are
very effective. Tea tree oil also kills fungus, and can be used on mold and mildew
in places like the bathroom.
Hydrogen peroxide might not leap to mind when one thinks of natural
products, but this chemical actually occurs naturally, and it is very effective at
clearing out bacteria. Hydrogen peroxide is also safe for topical use on the skin,
and some people use it to clean out wounds or to rinse the mouth to eliminate unwanted
1
bacteria. Hydrogen peroxide can also be used for cleaning and laundry, but it does have a bleaching
effect, and consumers should be careful about where they use it.
Some natural antibacterial products which are safe for ingestion include raw
honey and yogurt with active cultures. Honey has historically also been applied
topically to wounds, where it appears to be effective at killing bacteria and
promoting wound healing, although it can be messy. Yogurt can eliminate
unwanted bacteria in the mouth, and the live cultures in the yogurt will also
contribute to the commensal bacteria population in the gut, promoting healthy
digestion. The bacteria in yogurt can also be used to treat yeast infections.
Bacteria are microorganisms that have circular double-stranded DNA and
(except for mycoplasmas) cell walls. Most bacteria live extracellularly. Some
bacteria (eg, Salmonella typhi; Neisseria gonorrhoeae; Legionella, Mycobacterium,
Chlamydia, and Chlamydophila spp) preferentially reside and replicate
intracellularly. Some bacteria such as chlamydiae and rickettsiae are obligate
intracellular pathogens (i.e., able to grow, reproduce, and cause disease only within
the cells of the host); others (eg, Salmonella typhi, Brucella sp, Francisella
tularensis, N. gonorrhoeae, N. meningitidis, Legionella and Listeria spp,
Mycobacterium tuberculosis) are facultative intracellular pathogens.
Many bacteria are present in humans as normal flora, often in large numbers
and in many areas (eg, in the GI tract). Only a few bacterial species are human
pathogens.
1
Table 1: NATURAL PLANT SOURCE FOR MOLECULES
Sl.
no
Name
of the
compou
nd
structure source reference
1 Berberi
ne
goldensea
l and
Oregon
grape root
http://
www.anniesremedy.com/
chart.php?prop_ID=6
2 Thymol Trachysp
ermum
ammi
http://
www.anniesremedy.com/
herb_detail458.php
3 Alpha
Pinene
Trachysp
ermum
ammi
http://
www.anniesremedy.com/
herb_detail458.php
4 Beta-
Pinene
Trachysp
ermum
ammi
http://
www.anniesremedy.com/
herb_detail458.php
5 Camphe
ne
Trachysp
ermum
ammi
http://
www.anniesremedy.com/
herb_detail458.php
1
6 Carvacr
ol
Trachysp
ermum
ammi
http://
www.anniesremedy.com/
herb_detail458.php
7 Limone
ne
Trachysp
ermum
ammi
http://
www.anniesremedy.com/
herb_detail458.php
8 Eugenol Pimenta
officinalis
http://
www.anniesremedy.com/
herb_detail1.php
9 Eugenol
methyl
ether
Pimenta
officinalis
http://
www.anniesremedy.com/
herb_detail1.php
10 Myrcen
e
Pimenta
officinalis
http://
www.anniesremedy.com/
herb_detail1.php
11 Alpha-
Phellan
drene
Pimenta
officinalis
http://
www.anniesremedy.com/
herb_detail1.php
12 Anthraq
uinone
Aloe vera http://
www.anniesremedy.com/
herb_detail2.php
1
13 Choline Pimpinell
a anisum
http://
www.anniesremedy.com/
herb_detail3.php
14 Anethol Pimpinell
a anisum
http://
www.anniesremedy.com/
herb_detail3.php
15 Bixin Bixa
orellana
http://
www.anniesremedy.com/
herb_detail459.php
16 Cineole Populus
spp
http://
www.anniesremedy.com/
herb_detail358.php
17 Bisabol
ene
Populus
spp
http://
www.anniesremedy.com/
herb_detail358.php
18 Bisabol
ol
Populus
spp
http://
www.anniesremedy.com/
herb_detail358.php
19 Humule
ne
Populus
spp
http://
www.anniesremedy.com/
herb_detail358.php
1
20 Populin Populus
spp
http://
www.anniesremedy.com/
herb_detail358.php
21 Salicin Populous
spp
http://
www.anniesremedy.com/
herb_detail358.php
22 Oxyaca
nthine
Berberis
vulgaris
L.
http://
www.anniesremedy.com/
herb_detail253.php
23 Columb
amine
Berberis
vulgaris
L.
http://
www.anniesremedy.com/
herb_detail253.php
24 Myricitr
in
Myrica
cerifera
http://
www.anniesremedy.com/
herb_detail207.php
25 Linalyl
acetate
Citrus
bergamia
http://
www.anniesremedy.com/
herb_detail7.php
1
26 Bergam
otine
Citrus
bergamia
http://
www.anniesremedy.com/
herb_detail7.php
27 d-
Limone
ne
Citrus
bergamia
http://
www.anniesremedy.com/
herb_detail7.php
28 Linalool Citrus
bergamia
http://
www.anniesremedy.com/
herb_detail7.php
29 Sanguin
arine
Sanguinar
ia
canadensi
s L
http://
www.anniesremedy.com/
herb_detail222.php
30 Cauloph
ylline
Caulophy
llum
thalictroi
de
http://
www.anniesremedy.com/
herb_detail88.php
31 Bornyl
acetate
Pinus
sylvestris
http://
www.anniesremedy.com/
herb_detail49.php
1
32 Jaligoni
c-acid
Phytolacc
a
american
a
http://
www.anniesremedy.com/
herb_detail407.php
33 Oleanoli
c-acid
Phytolacc
a
american
a
http://
www.anniesremedy.com/
herb_detail407.php
34 Xylose Phytolacc
a
american
a
http://
www.anniesremedy.com/
herb_detail407.php
35 borneol Salvia
officinalis
http://
www.anniesremedy.com/
herb_detail52.php
36 Carnosi
c acid
Salvia
officinalis
http://
www.anniesremedy.com/
herb_detail52.php
1
37 Parillin Smilax
sarsaparil
la
http://
www.anniesremedy.com/
herb_detail297.php
38 Sarasap
arillosid
e
Smilax
sarsaparil
la
http://
www.anniesremedy.com/
herb_detail297.php
39 Safrol Sassafras
albidum
http://
www.anniesremedy.com/
herb_detail345.php
40 Apiole Sassafras
albidum
http://
www.anniesremedy.com/
herb_detail345.php
41 Baptifol
ine
Caulophy
llum
thalictroi
de
http://
www.anniesremedy.com/
herb_detail88.php
42 Anagyri
ne
Caulophy
llum
thalictroi
de
http://
www.anniesremedy.com/
herb_detail88.php
1
43 Boldine Peumus
boldus
Molina
http://
www.anniesremedy.com/
herb_detail223.php
44 Campho
r
Peumus
boldus
Molina
http://
www.anniesremedy.com/
herb_detail223.php
45 Querceti
n
Eupatoriu
m
perfoliatu
m
http://
www.anniesremedy.com/
herb_detail144.php
46 Kaempf
erol
Eupatoriu
m
perfoliatu
m
http://
www.anniesremedy.com/
herb_detail144.php
47 Rutin Eupatoriu
m
perfoliatu
m
http://
www.anniesremedy.com/
herb_detail144.php
48 Eupatori
n
Eupatoriu
m
perfoliatu
m
http://
www.anniesremedy.com/
herb_detail144.php
1
49 Diosphe
nol
Agathos
ma
betulina
http://
www.anniesremedy.com/
herb_detail203.php
50 Diosmin Agathos
ma
betulina
http://
www.anniesremedy.com/
herb_detail203.php
51 Alpha-
Terpine
ol
Melaleuc
a
leucadend
ron, M.
leucadend
ra
http://
www.anniesremedy.com/
herb_detail10.php#5
52 Azulene Melaleuc
a
leucadend
ron, M.
leucadend
ra
http://
www.anniesremedy.com/
herb_detail10.php#5
53 Nerolid
ol
Melaleuc
a
leucadend
ron, M.
leucadend
ra
http://
www.anniesremedy.com/
herb_detail10.php#5
1
54 Benzald
ehyde
Melaleuc
a
leucadend
ron, M.
leucadend
ra
http://
www.anniesremedy.com/
herb_detail10.php#5
55 Beta-
Asarone
Acorus
calamus
http://
www.anniesremedy.com/
herb_detail225.php
56 Delta-
Cadinen
e
Acorus
calamus
http://
www.anniesremedy.com/
herb_detail225.php
57 Elemici
n
Acorus
calamus
http://
www.anniesremedy.com/
herb_detail225.php
58 Galangi
n
Acorus
calamus
http://
www.anniesremedy.com/
herb_detail225.php
1
59 Yohimb
ine
Erythroxy
lum
catuaba
http://
www.anniesremedy.com/
herb_detail420.php
60 Cinchon
ain
Erythroxy
lum
catuaba
http://
www.anniesremedy.com/
herb_detail420.php
61 Capsaici
n
Capsicum
minimum
http://
www.anniesremedy.com/
herb_detail122.php
62 Ascorbi
c acid
Chrysant
hemum
morifoliu
m, C.
sinense
http://
www.anniesremedy.com/
herb_detail472.php
63 Coumar
in
Cinnamo
mum
zeylanicu
m,
http://
www.anniesremedy.com/
herb_detail15.php
1
64 Caprylic Cocos
nucifera
http://
www.anniesremedy.com/
herb_detail347.php
65 Linoleic Cocos
nucifera
http://
www.anniesremedy.com/
herb_detail347.php
66 Caryoph
yllene
Copaifera
Officinali
s
http://
www.anniesremedy.com/
herb_detail436.php
67 Diterpe
ne
Copaifera
Officinali
s
http://
www.anniesremedy.com/
herb_detail436.php
68 Coptisin
e
Coptis
spp
http://
www.anniesremedy.com/
herb_detail434.php
1
69 Vitamin
a
Agropyro
n repens
http://
www.anniesremedy.com/
herb_detail383.php
70 Arbutin Vacciniu
m
macrocar
pon
http://
www.anniesremedy.com/
herb_detail353.php
71 Alantola
ctone
Inula
helenium
http://
www.anniesremedy.com/
herb_detail146.php
72 Cuminal
dehyde
Eucalyptu
s globulus
http://
www.anniesremedy.com/
herb_detail23.php
73 Aromad
endrene
Eucalyptu
s globulus
http://
www.anniesremedy.com/
herb_detail23.php
74 Methyl-
cinnama
te
Alpinia
officinaru
m
http://
www.anniesremedy.com/
herb_detail481.php
1
75 Allicin Allium
sativum
http://
www.anniesremedy.com/
herb_detail128.php
76 Citral Allium
sativum
http://
www.anniesremedy.com/
herb_detail128.php
77 Geranio
l
Allium
sativum
http://
www.anniesremedy.com/
herb_detail128.php
78 Canadin
e
Hydrastis
canadensi
s
http://
www.anniesremedy.com/
herb_detail155.php
79 Meconi
n
Hydrastis
canadensi
s
http://
www.anniesremedy.com/
herb_detail155.php
80 Allohyd
roxycitri
c-acid
Hibiscus
sabdariffa
http://
www.anniesremedy.com/
herb_detail391.php
81 Malic-
acid
Hibiscus
sabdariffa
http://
www.anniesremedy.com/
herb_detail391.php
82 Hibiscu
s-acid
Hibiscus
sabdariffa
http://
www.anniesremedy.com/
herb_detail391.php
1
83 Beta-
Sitoster
ol
Ocimum
sanctum
http://
www.anniesremedy.com/
herb_detail464.php
84 Palmitic
-Acid
Ocimum
sanctum
http://
www.anniesremedy.com/
herb_detail464.php
85 Sinigrin Armoraci
a
rusticana
http://
www.anniesremedy.com/
herb_detail371.php
86 Thujone Juniperus
communi
s
http://
www.anniesremedy.com/
herb_detail30.php
87 Sabinen
e
Juniperus
communi
s
http://
www.anniesremedy.com/
herb_detail30.php
88 Kawain Juniperus
communi
s
http://
www.anniesremedy.com/
herb_detail30.php
89 Methyst
icin
Piper
methystic
um
http://
www.anniesremedy.com/
herb_detail237.php
1
90 Citronel
lol
Cymopog
on
citratus
http://
www.anniesremedy.com/
herb_detail34.php
91 Dipente
ne
Cymopog
on
citratus
http://
www.anniesremedy.com/
herb_detail34.php
92 Asparag
in
Althaea
officinalis
L.
http://
www.anniesremedy.com/
herb_detail133.php
93 Allyl
Isothioc
yanate
Brassica
nigra
http://
www.anniesremedy.com/
herb_detail369.php
94 Cymene Melaleuc
a
alternifoli
a
http://
www.anniesremedy.com/
herb_detail56.php#7
95 Terpine
ne
Melaleuc
a
alternifoli
a
http://
www.anniesremedy.com/
herb_detail56.php#7
96 Ocimen
e
Origanum
vulgare
http://
www.anniesremedy.com/
herb_detail163.php
1
97 Apiin Petroselin
um
crispum
http://
www.anniesremedy.com/
herb_detail108.php
98 Vincami
ne
Vinca
minor
http://
www.anniesremedy.com/
herb_detail492.php
99 Vanillic
-acid
Vinca
minor
http://
www.anniesremedy.com/
herb_detail492.php
100 Ursolic-
acid
Vinca
minor
http://
www.anniesremedy.com/
herb_detail492.php
1
RESULTS
1
AND
DISCUSSION
RETRIVAL OF STRUCTURES FOR COLLECTED MOLECULES:
PUBCHEM:
1
FIGURE 23: RETRIEVING STRUCTURE FROM PUBCHEM
MARVIN SKETCH:
1
FIGURE 24: OPENING WITH MARVIN SKETCH
1
FIGURE 25: FINDING CONFORMERS WITH MARVIN SKETCH
FIGURE 26: STRUCTURE OF THE CONFORMER
1
AUTODOCK:
FIGURE 27: OPENING THE STRUCTURE OF A PROTEIN WITH
AUTODOCK SOFTWARE
1
FIGURE 28: CHOOSING MACROMOLECULE IN THE STRUCTURE
FIGURE 29: SETTING THE GRID BOX
1
FIGURE 30: OPENING OF A LIGAND IN AUTODOCK SOFTWARE
FIGURE 31: RESULT BEING DISPLAYED
1
TABLE 2: LIPINSKI’S RULE:
1. Sl.no
NA
ME
OF
THE
CO
MP
OU
ND
HD HA M
W
Log p Lipins
ki’s
rule
yes/no
1 BERBERINE 0 4 336.36122 3.6 Yes
2 THYMOL 1 1 150.21756 3.3 Yes
3 ALPHA-PINENE 0 0 136.23404 2.8 Yes
4 BETA-PINENE 0 0 136.23404 3.1 Yes
5 CAMPHENE 0 0 136.23404 3.3 Yes
6 CARVACROL 1 1 150.21756 3.1 Yes
7 LIMONENE 0 0 136.23404 3.4 Yes
8 EUGENOL 1 2 164.20108 2 Yes
9 EUGENOL METHYL
ETHER
0 2 178.22766 2.5 Yes
10 MYRCENE 0 0 136.23404 4.3 Yes
11 ALPHA-
PHELLANDRENE
0 0 136.23404 3.2 Yes
12 ANTHRAQUINONE 0 2 208.21212 3.4 Yes
13 CHOLINE 1 2 139.62376 _ Yes
14 ANETHOL 0 1 148.20168 3.3 Yes
15 BIXIN 1 4 394.5033 7.5 No
1
16 CINEOLE 0 1 154.24932 2.5 Yes
17 BISABOLENE 0 0 204.35106 4.7 Yes
18 BISABOLOL 1 1 222.36634 3.8 Yes
19 HUMULENE 0 0 204.35106 4.5 Yes
20 POPULIN 4 8 390.38388 0.5 Yes
21 SALICIN 5 7 286.27782 -1.2 Yes
22 OXYACANTHINE 1 8 608.7233 6.3 No
23 COLUMBAMINE 1 4 338.3771 3.4 Yes
24 MYRICITRIN 8 12 464.3763 0.5 No
25 LINALYL ACETATE 0 2 196.286 3.3 Yes
26 BERGAMOTINE 0 4 338.39698 5.6 Yes
27 D-LIMONENE 0 0 136.23404 3.4 Yes
28 LINALOOL 1 1 154.24932 2.7 Yes
29 SANGUINARINE 0 4 332.32946 4.4 Yes
30 CAULOPHYLLINE 0 2 204.26824 0.7 Yes
31 BORNYL ACETATE 0 2 196.286 3.3 Yes
32 JALIGONIC ACID 5 7 518.68204 4.2 No
33 OLEANOLIC-ACID 2 3 456.70032 7.5 No
34 XYLOSE 4 5 150.1299 -2.5 Yes
35 BORNEOL 1 1 154.24932 2.7 Yes
36 CARNOSIC ACID 3 4 332.43392 4.9 Yes
37 PARILLIN 12 22 1049.19946 0.1 No
38 SARASAPARILLOSIDE 17 28 1229.35534 -2.5 No
39 SAFROL 0 2 162.1852 3 Yes
40 APIOLE 0 4 222.23716 2.7 Yes
1
41 BAPTIFOLINE 1 3 260.3315 0.6 Yes
42 ANAGYRINE 0 2 244.3321 1.6 Yes
43 BOLDINE 2 5 327.37434 2.7 Yes
44 CAMPHOR 0 1 152.23344 2.2 Yes
45 QUERCETIN 5 7 302.2357 1.5 Yes
46 KAEMPFEROL 4 6 286.2363 1.9 Yes
47 RUTIN 10 16 610.5175 -1.3 No
48 EUPATORIN 2 7 344.31544 2.9 Yes
49 DIOSPHENOL 1 2 168.23284 2 Yes
50 DIOSMIN 8 15 608.54468 -0.8 No
51 ALPHA-TERPINEOL 1 1 154.24932 1.8 Yes
52 AZULENE 0 0 128.17052 3.2 Yes
53 NEROLIDOL 1 1 222.36634 4.6 Yes
54 BENZALDEHYDE 0 1 106.12194 1.5 Yes
55 BETA-ASARONE 0 3 208.25364 3 Yes
56 DELTA-CADINENE 0 0 204.35106 3.8 Yes
57 ELEMICIN 0 3 208.25364 2.5 Yes
58 GALANGIN 3 5 270.2369 2.3 Yes
59 YOHIMBINE 2 4 354.44274 2.9 Yes
60 CINCHONAIN 11 15 740.66238 3.2 No
61 CAPSAICIN 2 3 305.41188 3.6 Yes
62 ASCORBIC ACID 4 6 176.12412 -1.8 Yes
63 COUMARIN 0 2 146.14274 1.4 Yes
64 CAPRYLIC ACID 1 2 144.21144 3 Yes
65 LINOLEIC ACID 1 2 280.44548 6.8 No
66 CARYOPHYLLENE 0 0 204.35106 4.4 Yes
1
67 DITERPENE 2 3 320.46628 3 Yes
68 COPTISINE 0 4 320.31876 3.5 Yes
69 VITAMIN A 1 1 286.4516 5.7 No
70 ARBUTIN 5 7 272.25124 -0.7 Yes
71 ALANTOLACTONE 0 2 232.3181 3.7 Yes
72 CUMINALDEHYDE 0 1 148.20168 2.7 Yes
73 AROMADENDRENE 0 0 204.35106 4.7 Yes
74 METHYL-CINNAMATE 0 2 162.1852 2.6 Yes
75 ALLICIN 0 1 162.273 1.3 Yes
76 CITRAL 0 1 152.23344 3 Yes
77 GERANIOL 1 1 154.24932 2.9 Yes
78 CANADINE 0 5 339.38504 3.1 Yes
79 MECONIN 0 4 194.184 1.3 Yes
80 ALLOHYDROXYCITRIC-
ACID
3 7 190.10764 -1.2 Yes
81 MALIC-ACID 3 5 134.08744 -1.3 Yes
82 HIBISCUS-ACID 5 8 208.12292 -2.6 Yes
83 BETA-SITOSTEROL 1 1 414.7067 9.3 No
84 PALMITIC-ACID 1 2 256.42408 6.4 No
85 SINIGRIN 4 10 358.36534 -1.1 Yes
86 THUJONE 0 1 152.23344 2.3 Yes
87 SABINENE 0 0 136.23404 3.1 Yes
88 KAVAIN 0 3 230.25916 2.5 Yes
89 METHYSTICIN 0 5 274.26866 2.4 Yes
90 CITRONELLOL 1 1 156.2652 3.2 Yes
91 DIPENTENE 0 0 136.23404 3.4 Yes
1
92 ASPARAGIN 3 4 132.11792 -3.4 Yes
93 ALLYL
ISOTHIOCYANATE
0 1 99.1542 2.4 Yes
94 CYMENE 0 0 134.21816 4.1 Yes
95 TERPINENE 0 0 136.23404 2.8 Yes
96 OCIMENE 0 0 136.23404 4.3 Yes
97 APIIN 8 14 564.49212 -0.4 No
98 VINCAMINE 1 4 354.44274 2.9 Yes
99 VANILLIC-ACID 2 4 168.14672 1.4 Yes
100 URSOLIC-ACID 2 3 456.70032 7.3 No
TABLE 3:ACCEPTED PROTEINS
Sl. nameoftheprotein score
score e value identity
2. cell-division protein 457 e-129 44
3. adenylosuccinate synthetase 335
1.00E-
92 41
4. beta-galactosidase 3 342
9.00E-
95 37
5. dihydroxyacid dehydratase 439 e-124 44
6. 4-alpha-glucanotransferase (amylomaltase) 370 e-103 39
7. arginyl-tRNA synthetase(arginine--tRNA ligase)
(ARGRS) 379 e-106 36
1
8. glyceraldehyde 3-phosphate dehydrogenase 268
1.00E-
72 45
9. ABC transporter, ATP-binding protein 116
5.00E-
27 36
10.aminopeptidase C 311
2.00E-
85 41
11.GTP cyclohydrolase 152
4.00E-
38 43
12.pentose-5-phosphate-3-epimerase 119
4.00E-
52 48
13.UDP-galactose 4-epimerase 216
6.00E-
57 36
14.phosphoglycerate kinase 300
3.00E-
82 45
15.class I heat-shock protein (molecular chaperone) 566 e-162 50
16.dnaJ protein, Heat-shock protein (activation of
DnaK) 213
5.00E-
56 36
17.CTP synthase (UTP--ammonia ligase) 472 e-134 46
18.CH60_SMI 60 kDa chaperonin (protein Cpn60) 508 e-144 50
19.exodeoxyribonuclease III 162
6.00E-
41 36
20.anthranilate synthase component I 286
4.00E-
78 35
21.anthranilate synthase component II (glutamine
amido-transferase) 134
7.00E-
33 42
1
22.anthranilate phosphoribosyltransferase 205
1.00E-
53 37
23.tryptophan synthase, beta subunit 478 e-135 60
24.aquaporin Z-water channel protein 115
6.00E-
27 35
25.phosphoglycerate dehydrogenase-related protein,
GTP-binding protein 339
5.00E-
94 41
26.2,3-bisphosphoglycerate-dependent
phosphoglycerate mutase
(phosphoglyceromutase) 253
3.00E-
68 53
27.D-tyrosyl-tRNA(Tyr) deacylase 111
6.00E-
26 42
28.Threonyl-tRNA synthetase, threonine-tRNA
ligase 446 e-126 39
29.UDP-glucose 4-epimerase 368 e-103 51
30.superfamily II DNA and RNA helicases ATP-
dependent RNA helicase, DEAD-box family 283
5.00E-
77 40
31.methionine sulfoxide reductase 109
5.00E-
25 35
32.V-type H+-ATPase, subunit A 558 e-159 52
33.V-type H+-ATPase, subunit B 533 e-152 56
34.ATP-dependent Clp protease, ATP-binding
subunit 513 e-146 42
1
35.FolD bifunctional protein; includes:
methylenetetrahydrofolate dehydrogenase,
methenyltetrahydrofolate cyclohydrolase 217
2.00E-
57 48
36.hypothetical protein 105
9.00E-
24 35
37.hypothetical protein 373 e-104 40
38.Translation elongation factor TU 396 e-111 50
39.pyrroline-5-carboxylate reductase 139
7.00E-
34 36
40.DNA-dependent RNA Polymerase sigma factor
rpoD 206
5.00E-
54 41
41.6-phosphofructokinase I 194
2.00E-
50 38
42.pyruvate kinase I; fructose-stimulated 297
3.00E-
81 37
43.GTP-binding protein LepA 100
6.00E-
22 37
44.uracil-DNA glycosylase 211
1.00E-
55 50
45.peptide chain release factor I 304
2.00E-
83 42
46.enolase 395 e-111 50
47.cell division protein FtsY 154
2.00E-
38 35
48.2-isopropylmalate synthase 306
4.00E-
84 42
1
49.carbamoyl-phosphate synthase, large subunit 801 0 41
50.carbamoyl-phosphate synthase, small subunit 229
7.00E-
61 37
51.signal recognition particle protein Ffh 385 e-107 46
52.phosphoglycerate mutase 380 e-106 43
53.proton-translocating ATPase, F1 sector, alpha-
subunit 535 e-153 57
54.pyridoxal-phosphate dependent aminotransferase 207
2.00E-
54 37
55.glutamine amidotransferase involved in
pyridoxine biosynthesis 139
2.00E-
34 39
56.glycyl-tRNA synthetase alpha subunit 344
1.00E-
95 57
57.S-adenosylmethionine synthetase 399 e-112 54
58.cell division ABC transporter, ATP-binding
protein FtsE 108
6.00E-
25 35
59.P-type ATPase-probable copper transporter 445 e-125 40
60.TypA, predicted membrane GTPase involved in
stress response 100
8.00E-
22 36
61.chorismate binding enzyme para-aminobenzoate
synthetase 169
2.00E-
42 36
62.cell wall surface anchor family protein 260
2.00E-
69 40
63.tRNA nucleotidyltransferase 117
3.00E-
27 38
1
64.hypothetical protein 128
6.00E-
31 39
65.triose phosphate isomerase 169
3.00E-
43 39
66.adenine phosphoribosyltransferase 158
5.00E-
40 46
67.50S ribosomal protein L11 177 ####### 63
68.cysteinyl-tRNA synthetase 201
3.00E-
52 44
69.serine/threonine protein kinase 132
1.00E-
31 36
70.guanylate kinase 135
7.00E-
33 39
71.acetyl-CoA carboxylase biotin carboxylase
subunit 419 e-118 47
72.HSP70 family protein 285
1.00E-
77 36
73.ATP-dependent Clp protease, ATP-binding
subunit 499 e-142 43
74.30S ribosomal protein S9 184
2.00E-
48 70
75.50S ribosomal protein L13 195 ####### 64
76.hypothetical protein 207
2.00E-
55 86
77.phenylalanyl-tRNA synthetase, alpha chain 445 e-126 60
1
78.rRNA methylase 177
5.00E-
46 52
79.pyruvate formate-lyase 610 e-175 43
80.undecaprenyl-diphosphatase 515 e-147 93
81.ABC transporter permease and substrate-binding
protein, amino acid transport 133
4.00E-
32 41
82.ABC transporter ATP-binding protein, amino
acid transport 195
3.00E-
51 46
83.threonine dehydratase 229
4.00E-
61 37
84.ketol-acid reductoisomerase 332
4.00E-
92 49
85.acetolactate synthase, small subunit 129
1.00E-
31 43
86.acetolactate synthase, large subunit 484 e-138 46
87.kinase 340
2.00E-
94 35
88.ABC transporter, permease and ATP-binding
protein, multidrug export 212
2.00E-
55 37
89.nucleoside diphosphate kinase 140
8.00E-
35 48
90.undecaprenyl diphosphate synthase 121
1.00E-
28 35
91.ABC tranporter, ATP-binding protein 129
6.00E-
31 37
1
92.leucyl-tRNA synthetase 915 0 53
93.adenylate kinase 153
2.00E-
38 36
94.50S ribosomal protein L6 152
4.00E-
38 44
95.DNA mismatch repair protein hexB 191
4.00E-
49 36
96.argininosuccinate lyase 352
6.00E-
98 42
97.argininosuccinate synthase 320
4.00E-
88 42
98.glycerol kinase 405 e-114 45
99.ATP-dependent Clp protease, ATP-binding
subunit 280
1.00E-
75 56
100. ABC transporter ATP-binding protein
cobalt transport 134
2.00E-
32 36
101. inosine monophosphate dehydrogenase 317
4.00E-
87 40
TABLE 4:TARGET PROTEINS
NAME OF THE MOLECULE DEG SCORE
1. cell-division protein 457 e-129 44 yes Membrane 3KDS
2. signal recognition particle
protein Ffh 385 e-107 46 yes Cytoplasmic Membrane 2J28
3. 50S ribosomal protein L11 177 ####### 63 yes Cytoplasmic Membrane 2K3F
1
4. 50S ribosomal protein L13 195 ####### 64 yes Cytoplasmic Extracellular 2GYA
5. glycerol kinase 405 e-114 45 yes Cytoplasmic Membrane 3H3N
Scoree-valueidentityaccepted/notcellopredictio
n pdbid
DOCKING SCORE FOR THE MOLECULE
TABLE 5: PDB ID: 3KDS
Sl.no Name of the molecule Docking score
1 alantolactone -7.4
2 allicin -3.8
3 Allohydroxycitric acid -6.2
4 Allyl isothiocyanate -3.6
5 Alpha phellandrene -5.4
6 Alpha_pinene -5.1
7 Alpha_terpineol -5.5
8 Anagyrine -6.4
9 Anethol -5.2
10 antraquinone -7.5
11 Apiole -5.9
12 Arbutin -6.7
13 Aromadendrene -6.1
14 Ascorbic acid -5.8
15 Asparagine -4.7
16 Azulene -5.3
17 Baptifoline -7.0
1
18 Benzaldehyde -4.3
19 Berbarine -8.5
20 Bergamotine -7.3
21 Beta_asarone -5.4
22 Beta_pinene -5.0
23 Bisabolene -6.5
24 Bisabolol -6.4
25 boldine -7.4
26 Borneol -5.4
27 Boronyl acetate -5.6
28 Camphene -4.1
29 Camphor -5.3
30 Canadine -8.3
31 Caprylic acid -4.3
32 Capsaicin -7.1
33 Carnosic acid -8.1
34 Carvacrol -5.6
35 Caryophyllene -6.2
36 Caulophylline -6.6
37 Choline -3.5
38 Cineole -5.0
39 Citral -4.9
40 Citronellol -4.6
41 Columbamine -8.6
42 Coptisine -9.3
1
43 Coumarin -6.0
44 Cuminaldehyde -5.7
45 Cymene -5.0
46 Delta_candinene -6.5
47 Diosphenol -5.5
48 Dipentene -4.8
49 Diterpene -8.1
50 D_limonene -5.2
51 Elemicin -5.1
52 Eugenol -5.7
53 Eugenol methyl ether -5.2
54 Eupatorin -8.1
55 Galangin -8.0
56 Geraniol -4.8
57 Hibiscus acid -6.2
58 Humulene -6.1
59 Kaempferol -7.9
60 Kavain -7.2
61 Limonene -4.8
62 Linalool -5.0
63 Linlyl acetate -5.1
64 Malic acid -5.1
65 Meconin -5.8
66 Methyl cinnamate -5.9
67 Methysticin -7.2
68 Myrcene -4.7
1
69 Nerolidol -5.9
70 Ocimene -4.8
71 Populene -8.0
72 Quercetin -7.9
73 Sabinene -4.9
74 Safrol -5.5
75 Salicin -6.5
76 Sanguinarine -9.9
77 Singirin -6.3
78 Terpinene -5.2
79 Thujone -5.1
80 Thymol -5.2
81 Vanillic acid -6.0
82 Vincamine -7.6
83 Xylose -5.3
84 Yohimbine -8.4
TABLE 6: PDB ID: 3H3N
Sl.no Name of the molecule Docking score
1 alantolactone -7.6
2 allicin -3.8
3 Allohydroxycitric acid -6.1
4 Allyl isothiocyanate -2.9
5 Alpha phellandrene -5.2
6 Alpha_pinene -5.4
1
7 Alpha_terpineol -5.4
8 Anagyrine -6.7
9 Anethol -4.9
10 antraquinone -6.9
11 Apiole -5.5
12 Arbutin -7.1
13 Aromadendrene -6.5
14 Ascorbic acid -5.6
15 Asparagine -4.8
16 Azulene -5.4
17 Baptifoline -7.4
18 Benzaldehyde -4.2
19 Berbarine -7.7
20 Bergamotine -7.1
21 Beta_asarone -5.0
22 Beta_pinene -5.4
23 Bisabolene -6.5
24 Bisabolol -5.5
25 boldine -6.9
26 Borneol -5.6
27 Boronyl acetate -5.8
28 Camphene -5.3
29 Camphor -5.4
30 Canadine -7.3
31 Caprylic acid -4.2
32 Capsaicin -5.7
1
33 Carnosic acid -7.7
34 Carvacrol -5.6
35 Caryophyllene -6.8
36 Caulophylline -6.2
37 Choline -3.5
38 Cineole -5.5
39 Citral -4.7
40 Citronellol -4.7
41 Columbamine -7.0
42 Coptisine -8.8
43 Coumarin -5.5
44 Cuminaldehyde -5.1
45 Cymene -5.3
46 Delta_candinene -6.5
47 Diosphenol -6.0
48 Dipentene -5.2
49 Diterpene -7.9
50 D_limonene -5.3
51 Elemicin -5.1
52 Eugenol -5.3
53 Eugenol methyl ether -4.8
54 Eupatorin -7.4
55 Galangin -7.5
56 Geraniol -4.9
57 Hibiscus acid -5.6
58 Humulene -6.4
1
59 Kaempferol -7.9
60 Kavain -6.5
61 Limonene -5.2
62 Linalool -5.0
63 Linlyl acetate -5.2
64 Malic acid -4.7
65 Meconin -5.6
66 Methyl cinnamate -5.0
67 Methysticin -7.3
68 Myrcene -4.9
69 Nerolidol -5.6
70 Ocimene -4.9
71 Populene -7.8
72 Quercetin -8.0
73 Sabinene -5.1
74 Safrol -5.2
75 Salicin -6.7
76 Sanguinarine -8.1
77 Singirin -7.2
78 Terpinene -5.3
79 Thujone -5.2
80 Thymol -5.4
81 Vanillic acid -5.6
82 Vincamine -7.2
83 Xylose -5.3
84 Yohimbine -7.6
1
TABLE 7&8:MOLECULES RANKED BASED UPON THEIR DOCKING
SCORE
PDB ID: 3KDS
Sl.no Name of molecule Dock score Rank
1 sanguinarine -9.9 1
2 Coptisine -9.3 2
3 Columbamine -8.6 3
4 Berbarine -8.5 4
5 Yohimbine -8.4 5
PDB ID: 3H3N
Sl.no Name of molecule Dock score Rank
1 Coptisine -8.8 1
2 Diterpine -7.9 2
3 Kaempferol -7.9 3
4 Populene -7.8 4
5 Carnosic acid -7.7 5
6 Berbarine -7.7 6
DISCUSSION:
Around 100 small molecules from different categories such as alkaloids,
flavonoids, tannins, glycosides were taken as targeting agents that are responsible
1
for inhibiting the biological process important in causing Endocarditis. The
investigational drug that is Amoxil which is under clinical trial was used as a
reference drug in this study. Since Endocarditis is mainly responsible for
Inflammation of the inner lining of the heart. We took cell division protein and
glycerol kinase as our targets and structure for the same was derived from Protein
Data Bank (PDB). Initial screening of the molecules was based on Lipinski’s rule
of five. The molecules which satisfy the criteria were subjected to receptor-ligand
interaction study using docking tool AutoDock Vina and docking score was
considered for further result interpretation. Molecules which showed better
interactions with cell division protein and glycerol kinase than reference drug were
considered. The least dock score is for the compounds are sanguinarine, Coptisine.
This led to result that 5 compounds sanguinarine, Coptisine, Columbamine,
Berbarine, Yohimbine for cell wall protein and Coptisine, Diterpine, Kaempferol,
Populene, Carnosic acid for glycerol kinase were found to be the best “lead
compounds” for the disease.
SUMMARY:
In our study, attempt was made to find potent anti bacterial agent for cell
wall protein and glycerol kinase using natural agents targeting biological process
important in Endocarditis. Cell wall protein and glycerol kinase are served as
molecular targets for our study. This investigational anti-bacterial agents
1
sanguinarine, Coptisine was considered as a reference drug in this work. Hundreds
of natural molecules were selected from various scientific articles. Conformers
were derived using Marvin sketch tool of the molecules. Around 100 molecules
were screened according to the structure similarity of the commercial drugs. These
molecules were subjected to docking with cell wall protein and glycerol kinase.
After the docking process the molecules were ranked according to their docking
score keeping the sanguinarine and Coptisine are the standard. After docking the
highest docking score was selected. The natural molecules sanguinarine, Coptisine,
Columbamine, Berbarine, Yohimbine for cell wall protein and Coptisine,
Diterpine, Kaempferol, Populene, Carnosic acid for glycerol kinase were found to
be the best “lead compounds” for the disease.
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