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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

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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

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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

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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):

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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,

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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.

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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.

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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.

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FIGURE 15: GENERAL ARCHITECTURE OF OUR PREDICTIVE SYSTEM

FIGURE 16: HOME PAGE OF CELLO

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FIGURE 17: RESULT PAGE OF CELLO

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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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