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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:

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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. 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). SubAcute 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.

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Figure 2: Sub-Acute Endocarditis

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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. 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.

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Figure 3: Bacterial Endocarditis

MECHANISM:

Figure 4: Mechanism of Bacterial Endocarditis 1


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. 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.

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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: 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

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Headache

Weakness

Aches

Back pain

Heart murmur

Weight loss

Shortness of breath on exertion

Swollen feet

Swollen legs

Swollen abdomen

Blood in urine

• Dark lines under nails caused by hemorrhage • Unusual urine color • Painful red nodes on finger pads • Painful red nodes on toe pads •

Petechiae

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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, 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

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.

Figure 5

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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 Figure 6

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. PRE-EXISTING

HEART

CONDITIONS

ENDOCARDITIS: •

Previous cardiac valve surgery

•

Previous infective Endocarditis

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ASSOCIATED

WITH


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: The list of complications that have been mentioned in various sources for Endocarditis includes: •

Heart block

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Heart embolism (see Heart symptoms)

Heart valve damage

Echo Samples

Figure 7: Echo samples of Endocarditis DIAGNOSIS AND TREATMENT FOR ENDOCARDITIS:  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:

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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 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

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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 received antibiotics; we recommend stopping any antibiotics for 2–4 days and reculturing. 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

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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 ‘FollowOn’ therapy after completion of a course of treatment. 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,

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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 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

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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: 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 1


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 sideeffects 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 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 1


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,

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 SHAPE: Coccus ARRANGEMENT: Chains MOTILE: No HABITAT: Host-associated OXYGEN REQUIREMENT: Aerobic TEMPERATURE RANGE: Mesophilic PATHOGENIC - Yes: Human DISEASE: Bacterial Endocarditis

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Lactobacillales,

Streptococcaceae,


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 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

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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 Xalignment 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 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 Îą-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:

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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 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

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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 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:

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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: 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.

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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

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.

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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 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

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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 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.

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•

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 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.

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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 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 Xalignment when compared with S. pneumoniae. 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 1


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 Xalignment when compared with S. pneumoniae. The pneumococcal choline-containing teichoic acids are targeted by cholinebinding 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 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

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(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 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.

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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 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.

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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 Îą2A-adrenergic receptor gene.

Figure 11: Yohimbine

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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 째C. 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.

Figure 12: Kaempferol

MATERIALS AND METHODS

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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 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/),

(http://www.imtech.res.in/raghava/pslpred/),

and

SOSUI

PSLpred 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

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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°WCoordinates: 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 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

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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 polymorphisms, 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): 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,

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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 NCBIâ&#x20AC;&#x2122;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 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).

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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, 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

1


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. 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

1


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. 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

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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 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.

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FIGURE 14: SEQUENCE SUBMISSION IN DEG DATABASE

CELLO PREDICTION: 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.

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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 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.

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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 Alzheimerâ&#x20AC;&#x2122;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.

FIGURE 15: GENERAL ARCHITECTURE OF OUR PREDICTIVE SYSTEM

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FIGURE 16: HOME PAGE OF CELLO

1


FIGURE 17: RESULT PAGE OF CELLO

1


PROTEIN DATA BANK:

FIGURE 18: HOME PAGE OF PDB The Protein Data Bank (PDB) is a repository for the 3-D structural data of large biological molecules, such as proteins and nucleic acids. The data is either obtained by X-ray crystallography or NMR spectroscopy and submitted by biologists and biochemists from around the world, which are freely accessible on the Internet via the websites of its member organizations (PDBe, PDBj, and RCSB). The PDB is overseen by an organization called the Worldwide Protein Data Bank, wwPDB. 1


The PDB is a key resource in areas of structural biology, such as structural genomics. Most major scientific journals, and some funding agencies, such as the NIH in the USA, now require scientists to submit their structure data to the PDB. If the contents of the PDB are thought of as primary data, then there are hundreds of derived (i.e., secondary) databases that categorize the data differently. For example, both SCOP and CATH categorize structures according to type of structure and assumed evolutionary relations; GO categorize structures based on genes. HISTORY: The PDB originated as a grassroots. In 1971, Walter Hamilton of the Brookhaven National Laboratory agreed to setup the data bank at Brookhaven. Upon Hamilton's death in 1973, Dr. Tom Koeztle took over direction of the PDB for the subsequent 20 years. In January 1994, Dr. Joel Sussman of Israel's Weizmann Institute of Science was appointed head of the PDB. In October 1998, the PDB was transferred to the Research Collaboratory for Structural Bioinformatics (RCSB); the transfer was completed in June 1999. The new director was Dr. Helen M. Berman of Rutgers University (one of the member institutions of the RCSB). In 2003, with the formation of the wwPDB, the PDB became an international organization. The founding members are PDBe (Europe), RCSB (USA), and PDBj (Japan). The BMRB joined in 2006. Each of the four members of wwPDB can act as deposition, data processing and distribution centers for PDB data. The data processing refers to the fact that wwPDB staff review and annotates each submitted entry. The data are then automatically checked for plausibility (the source code for this validation software has been made available to the public at no charge).

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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 Method X-ray diffraction NMR Electron microscopy Hybrid Other Total:

Proteins Nucleic Acids 58192 7686 245 28 132 66283

1262 941 22 3 4 2232

Protein/Nucleic Acid complexes 2822 168 86 1 5 3082

Other Total 17 7 0 1 13 38

62293 8802 353 33 154 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 Xray 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. 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

1


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 —in different environments or conformations—are contained in PDB with different PDB IDs.)

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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 PubChem is a database of chemical molecules and their activities against biological assays. The system is maintained by the National Center for 1


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. 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. 1


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 caseinsensitive. 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"

3D editing

3D geometry and conformer generation

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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)

CROSS PLATORM DELIVERY

1


â&#x20AC;˘

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 â&#x20AC;˘

Java Web Start enables web delivery of end user applications

.NET support

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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 precalculates 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 profileprofile 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: ACDEFGHIKLMNPQRSTVWY 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: â&#x20AC;˘

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.

â&#x20AC;˘

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 be used to scale the model.

1


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.

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

1


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 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

1


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.

Table 1: NATURAL PLANT SOURCE FOR MOLECULES Sl.

Name

no

of the

structure

source

reference

compou 1

nd Berberi

goldensea http://www.anniesremedy.c

ne

l

and om/chart.php?prop_ID=6

Oregon grape root 2

Thymol

Trachysp

http://www.anniesremedy.c

ermum

om/herb_detail458.php

ammi

1


3

Alpha

Trachysp

http://www.anniesremedy.c

Pinene

ermum

om/herb_detail458.php

ammi 4

Beta-

Trachysp

http://www.anniesremedy.c

Pinene

ermum

om/herb_detail458.php

ammi 5

Camphe

Trachysp

http://www.anniesremedy.c

ne

ermum

om/herb_detail458.php

ammi 6

Carvacr

Trachysp

http://www.anniesremedy.c

ol

ermum

om/herb_detail458.php

ammi 7

Limone

Trachysp

http://www.anniesremedy.c

ne

ermum

om/herb_detail458.php

ammi 8

Eugenol

Pimenta

http://www.anniesremedy.c

officinalis om/herb_detail1.php

9

Eugenol

Pimenta

methyl

officinalis om/herb_detail1.php

ether

1

http://www.anniesremedy.c


10

11

Myrcen

Pimenta

http://www.anniesremedy.c

e

officinalis om/herb_detail1.php

Alpha-

Pimenta

Phellan

officinalis om/herb_detail1.php

http://www.anniesremedy.c

drene 12

Anthraq

Aloe vera http://www.anniesremedy.c

uinone 13

14

15

16

17

om/herb_detail2.php

Choline

Pimpinell

http://www.anniesremedy.c

a anisum

om/herb_detail3.php

Pimpinell

http://www.anniesremedy.c

a anisum

om/herb_detail3.php

Bixa

http://www.anniesremedy.c

orellana

om/herb_detail459.php

Populus

http://www.anniesremedy.c

spp

om/herb_detail358.php

Bisabol

Populus

http://www.anniesremedy.c

ene

spp

om/herb_detail358.php

Anethol

Bixin

Cineole

1


18

19

20

21

22

Bisabol

Populus

http://www.anniesremedy.c

ol

spp

om/herb_detail358.php

Humule

Populus

http://www.anniesremedy.c

ne

spp

om/herb_detail358.php

Populin

Populus

http://www.anniesremedy.c

spp

om/herb_detail358.php

Populous

http://www.anniesremedy.c

spp

om/herb_detail358.php

Oxyaca

Berberis

http://www.anniesremedy.c

nthine

vulgaris

om/herb_detail253.php

Salicin

L.

23

Columb

Berberis

http://www.anniesremedy.c

amine

vulgaris

om/herb_detail253.php

L. 24

Myricitr

Myrica

http://www.anniesremedy.c

in

cerifera

om/herb_detail207.php

1


25

26

27

Linalyl

Citrus

http://www.anniesremedy.c

acetate

bergamia

om/herb_detail7.php

Bergam

Citrus

http://www.anniesremedy.c

otine

bergamia

om/herb_detail7.php

d-

Citrus

http://www.anniesremedy.c

Limone

bergamia

om/herb_detail7.php

Citrus

http://www.anniesremedy.c

bergamia

om/herb_detail7.php

ne 28

29

Linalool

Sanguin

Sanguinar http://www.anniesremedy.c

arine

ia

om/herb_detail222.php

canadensi sL 30

Cauloph

Caulophy

http://www.anniesremedy.c

ylline

llum

om/herb_detail88.php

thalictroi 31

Bornyl

de Pinus

http://www.anniesremedy.c

acetate

sylvestris

om/herb_detail49.php

1


32

Jaligoni

Phytolacc http://www.anniesremedy.c

c-acid

a

om/herb_detail407.php

american a

33

Oleanoli

Phytolacc http://www.anniesremedy.c

c-acid

a

om/herb_detail407.php

american a 34

Xylose

Phytolacc http://www.anniesremedy.c a

om/herb_detail407.php

american 35

a Salvia

borneol

http://www.anniesremedy.c

officinalis om/herb_detail52.php 36

37

Carnosi

Salvia

c acid

officinalis om/herb_detail52.php

Parillin

Smilax

http://www.anniesremedy.c

sarsaparil

om/herb_detail297.php

la

1

http://www.anniesremedy.c


38

39

40

41

Sarasap

Smilax

http://www.anniesremedy.c

arillosid

sarsaparil

om/herb_detail297.php

e

la

Safrol

Sassafras

http://www.anniesremedy.c

albidum

om/herb_detail345.php

Sassafras

http://www.anniesremedy.c

albidum

om/herb_detail345.php

Baptifol

Caulophy

http://www.anniesremedy.c

ine

llum

om/herb_detail88.php

Apiole

thalictroi 42

Anagyri

de Caulophy

http://www.anniesremedy.c

ne

llum

om/herb_detail88.php

thalictroi 43

Boldine

de Peumus

http://www.anniesremedy.c

boldus

om/herb_detail223.php

Molina 44

Campho

Peumus

http://www.anniesremedy.c

r

boldus

om/herb_detail223.php

Molina

1


45

Querceti

Eupatoriu http://www.anniesremedy.c

n

m

om/herb_detail144.php

perfoliatu m 46

Kaempf

Eupatoriu http://www.anniesremedy.c

erol

m

om/herb_detail144.php

perfoliatu 47

m Eupatoriu http://www.anniesremedy.c

Rutin

m

om/herb_detail144.php

perfoliatu m 48

Eupatori

Eupatoriu http://www.anniesremedy.c

n

m

om/herb_detail144.php

perfoliatu 49

50

Diosphe

m Agathos

http://www.anniesremedy.c

nol

ma

om/herb_detail203.php

Diosmin

betulina Agathos

http://www.anniesremedy.c

ma

om/herb_detail203.php

betulina 51

Alpha-

Melaleuc

http://www.anniesremedy.c

Terpine

a

om/herb_detail10.php#5

ol

leucadend ron,

M.

leucadend

1


52

Azulene

ra Melaleuc

http://www.anniesremedy.c

a

om/herb_detail10.php#5

leucadend ron,

M.

leucadend 53

Nerolid

ra Melaleuc

http://www.anniesremedy.c

ol

a

om/herb_detail10.php#5

leucadend ron,

M.

leucadend 54

Benzald

ra Melaleuc

http://www.anniesremedy.c

ehyde

a

om/herb_detail10.php#5

leucadend ron,

M.

leucadend 55

56

Beta-

ra Acorus

http://www.anniesremedy.c

Asarone

calamus

om/herb_detail225.php

Delta-

Acorus

http://www.anniesremedy.c

Cadinen

calamus

om/herb_detail225.php

e

1


57

58

59

Elemici

Acorus

http://www.anniesremedy.c

n

calamus

om/herb_detail225.php

Galangi

Acorus

http://www.anniesremedy.c

n

calamus

om/herb_detail225.php

Yohimb

Erythroxy http://www.anniesremedy.c

ine

lum

om/herb_detail420.php

catuaba

60

Cinchon

Erythroxy http://www.anniesremedy.c

ain

lum

om/herb_detail420.php

catuaba

61

Capsaici

Capsicum http://www.anniesremedy.c

n

minimum

1

om/herb_detail122.php


62

Ascorbi

Chrysant

http://www.anniesremedy.c

c acid

hemum

om/herb_detail472.php

morifoliu m, 63

C.

Coumar

sinense Cinnamo

http://www.anniesremedy.c

in

mum

om/herb_detail15.php

zeylanicu 64 65

66

Caprylic Linoleic

m, Cocos

http://www.anniesremedy.c

nucifera

om/herb_detail347.php

Cocos

http://www.anniesremedy.c

nucifera

om/herb_detail347.php

Caryoph

Copaifera http://www.anniesremedy.c

yllene

Officinali

om/herb_detail436.php

s 67

Diterpe

Copaifera http://www.anniesremedy.c

ne

Officinali s

1

om/herb_detail436.php


68

69

70

Coptisin

Coptis

http://www.anniesremedy.c

e

spp

om/herb_detail434.php

Vitamin

Agropyro http://www.anniesremedy.c

a

n repens

om/herb_detail383.php

Arbutin

Vacciniu

http://www.anniesremedy.c

m

om/herb_detail353.php

macrocar pon

71

72

73

Alantola

Inula

http://www.anniesremedy.c

ctone

helenium

om/herb_detail146.php

Cuminal

Eucalyptu http://www.anniesremedy.c

dehyde

s globulus om/herb_detail23.php

Aromad

Eucalyptu http://www.anniesremedy.c

endrene

s globulus om/herb_detail23.php

1


74

75 76 77

78

Methyl-

Alpinia

cinnama

officinaru om/herb_detail481.php

te

m

Allicin

Allium

http://www.anniesremedy.c

sativum

om/herb_detail128.php

Allium

http://www.anniesremedy.c

sativum

om/herb_detail128.php

Geranio

Allium

http://www.anniesremedy.c

l

sativum

om/herb_detail128.php

Canadin

Hydrastis

http://www.anniesremedy.c

e

canadensi om/herb_detail155.php

Citral

http://www.anniesremedy.c

s

79

80

Meconi

Hydrastis

http://www.anniesremedy.c

n

canadensi om/herb_detail155.php

Allohyd

s Hibiscus

roxycitri

sabdariffa om/herb_detail391.php

http://www.anniesremedy.c

c-acid 81

82

Malic-

Hibiscus

acid

sabdariffa om/herb_detail391.php

Hibiscu

Hibiscus

s-acid

sabdariffa om/herb_detail391.php

1

http://www.anniesremedy.c

http://www.anniesremedy.c


83

Beta-

Ocimum

http://www.anniesremedy.c

Sitoster

sanctum

om/herb_detail464.php

ol 84

Palmitic

Ocimum

http://www.anniesremedy.c

85

-Acid Sinigrin

sanctum Armoraci

om/herb_detail464.php http://www.anniesremedy.c

a

om/herb_detail371.php

rusticana

86

Thujone

Juniperus

http://www.anniesremedy.c

communi

om/herb_detail30.php

s 87

Sabinen

Juniperus

http://www.anniesremedy.c

e

communi

om/herb_detail30.php

s 88

Kawain

Juniperus

http://www.anniesremedy.c

communi

om/herb_detail30.php

s 89

Methyst

Piper

http://www.anniesremedy.c

icin

methystic

om/herb_detail237.php

um 90

Citronel

Cymopog http://www.anniesremedy.c

lol

on citratus

1

om/herb_detail34.php


91

Dipente

Cymopog http://www.anniesremedy.c

ne

on

om/herb_detail34.php

citratus 92

93

94

Asparag

Althaea

http://www.anniesremedy.c

in

officinalis om/herb_detail133.php

Allyl

L. Brassica

http://www.anniesremedy.c

Isothioc

nigra

om/herb_detail369.php

yanate Cymene

Melaleuc

http://www.anniesremedy.c

a

om/herb_detail56.php#7

alternifoli 95

Terpine

a Melaleuc

http://www.anniesremedy.c

ne

a

om/herb_detail56.php#7

alternifoli 96 97

Ocimen

a Origanum http://www.anniesremedy.c

e

vulgare

Apiin

Petroselin http://www.anniesremedy.c um

om/herb_detail163.php om/herb_detail108.php

crispum 98

Vincami

Vinca

http://www.anniesremedy.c

ne

minor

om/herb_detail492.php

1


99

Vanillic

Vinca

http://www.anniesremedy.c

-acid

minor

om/herb_detail492.php

Vinca

http://www.anniesremedy.c

minor

om/herb_detail492.php

100 Ursolicacid

1


RESULTS AND DISCUSSION

1


RETRIVAL OF STRUCTURES FOR COLLECTED MOLECULES: PUBCHEM:

FIGURE 23: RETRIEVING STRUCTURE FROM PUBCHEM

1


MARVIN SKETCH:

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: Sl.no NAME

OF

THE HD HA MW

Log p

Lipinski’s

1 2 3 4 5 6 7 8 9

COMPOUND BERBERINE THYMOL ALPHA-PINENE BETA-PINENE CAMPHENE CARVACROL LIMONENE EUGENOL EUGENOL METHYL

0 1 0 0 0 1 0 1 0

4 1 0 0 0 1 0 2 2

336.36122 150.21756 136.23404 136.23404 136.23404 150.21756 136.23404 164.20108 178.22766

3.6 3.3 2.8 3.1 3.3 3.1 3.4 2 2.5

rule yes/no Yes Yes Yes Yes Yes Yes Yes Yes Yes

10 11

ETHER MYRCENE ALPHA-

0 0

0 0

136.23404 136.23404

4.3 3.2

Yes Yes

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

PHELLANDRENE ANTHRAQUINONE CHOLINE ANETHOL BIXIN CINEOLE BISABOLENE BISABOLOL HUMULENE POPULIN SALICIN OXYACANTHINE COLUMBAMINE MYRICITRIN LINALYL ACETATE BERGAMOTINE D-LIMONENE LINALOOL SANGUINARINE CAULOPHYLLINE BORNYL ACETATE JALIGONIC ACID

0 1 0 1 0 0 1 0 4 5 1 1 8 0 0 0 1 0 0 0 5

2 2 1 4 1 0 1 0 8 7 8 4 12 2 4 0 1 4 2 2 7

208.21212 139.62376 148.20168 394.5033 154.24932 204.35106 222.36634 204.35106 390.38388 286.27782 608.7233 338.3771 464.3763 196.286 338.39698 136.23404 154.24932 332.32946 204.26824 196.286 518.68204

3.4 _ 3.3 7.5 2.5 4.7 3.8 4.5 0.5 -1.2 6.3 3.4 0.5 3.3 5.6 3.4 2.7 4.4 0.7 3.3 4.2

Yes Yes Yes No Yes Yes Yes Yes Yes Yes No Yes No Yes Yes Yes Yes Yes Yes Yes No

1


33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68

OLEANOLIC-ACID XYLOSE BORNEOL CARNOSIC ACID PARILLIN SARASAPARILLOSIDE SAFROL APIOLE BAPTIFOLINE ANAGYRINE BOLDINE CAMPHOR QUERCETIN KAEMPFEROL RUTIN EUPATORIN DIOSPHENOL DIOSMIN ALPHA-TERPINEOL AZULENE NEROLIDOL BENZALDEHYDE BETA-ASARONE DELTA-CADINENE ELEMICIN GALANGIN YOHIMBINE CINCHONAIN CAPSAICIN ASCORBIC ACID COUMARIN CAPRYLIC ACID LINOLEIC ACID CARYOPHYLLENE DITERPENE COPTISINE

2 4 1 3 12 17 0 0 1 0 2 0 5 4 10 2 1 8 1 0 1 0 0 0 0 3 2 11 2 4 0 1 1 0 2 0 1

3 5 1 4 22

456.70032 150.1299 154.24932 332.43392 1049.1994

7.5 -2.5 2.7 4.9 0.1

No Yes Yes Yes No

28

6 1229.3553

-2.5

No

2 4 3 2 5 1 7 6 16 7 2 15 1 0 1 1 3 0 3 5 4 15 3 6 2 2 2 0 3 4

4 162.1852 222.23716 260.3315 244.3321 327.37434 152.23344 302.2357 286.2363 610.5175 344.31544 168.23284 608.54468 154.24932 128.17052 222.36634 106.12194 208.25364 204.35106 208.25364 270.2369 354.44274 740.66238 305.41188 176.12412 146.14274 144.21144 280.44548 204.35106 320.46628 320.31876

3 2.7 0.6 1.6 2.7 2.2 1.5 1.9 -1.3 2.9 2 -0.8 1.8 3.2 4.6 1.5 3 3.8 2.5 2.3 2.9 3.2 3.6 -1.8 1.4 3 6.8 4.4 3 3.5

Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes Yes


69 70 71 72 73 74 75 76 77 78 79 80

VITAMIN A ARBUTIN ALANTOLACTONE CUMINALDEHYDE AROMADENDRENE METHYL-CINNAMATE ALLICIN CITRAL GERANIOL CANADINE MECONIN ALLOHYDROXYCITRIC-

1 5 0 0 0 0 0 0 1 0 0 3

1 7 2 1 0 2 1 1 1 5 4 7

286.4516 272.25124 232.3181 148.20168 204.35106 162.1852 162.273 152.23344 154.24932 339.38504 194.184 190.10764

5.7 -0.7 3.7 2.7 4.7 2.6 1.3 3 2.9 3.1 1.3 -1.2

No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

81 82 83 84 85 86 87 88 89 90 91 92 93

ACID MALIC-ACID HIBISCUS-ACID BETA-SITOSTEROL PALMITIC-ACID SINIGRIN THUJONE SABINENE KAVAIN METHYSTICIN CITRONELLOL DIPENTENE ASPARAGIN ALLYL

3 5 1 1 4 0 0 0 0 1 0 3 0

5 8 1 2 10 1 0 3 5 1 0 4 1

134.08744 208.12292 414.7067 256.42408 358.36534 152.23344 136.23404 230.25916 274.26866 156.2652 136.23404 132.11792 99.1542

-1.3 -2.6 9.3 6.4 -1.1 2.3 3.1 2.5 2.4 3.2 3.4 -3.4 2.4

Yes Yes No No Yes Yes Yes Yes Yes Yes Yes Yes Yes

94 95 96 97 98 99 100

ISOTHIOCYANATE CYMENE TERPINENE OCIMENE APIIN VINCAMINE VANILLIC-ACID URSOLIC-ACID

0 0 0 8 1 2 2

0 0 0 14 4 4 3

134.21816 136.23404 136.23404 564.49212 354.44274 168.14672 456.70032

4.1 2.8 4.3 -0.4 2.9 1.4 7.3

Yes Yes Yes No Yes Yes No

TABLE 3:ACCEPTED PROTEINS

1


Sl. nameoftheprotein

score

1. cell-division protein

score e value

identity

457 335

e-129 1.00E-

44 41

342

92 9.00E-

37

2. adenylosuccinate synthetase 3. beta-galactosidase 3

95

4. dihydroxyacid dehydratase

439

e-124

44

5. 4-alpha-glucanotransferase (amylomaltase) 370 6. arginyl-tRNA synthetase(arginine--tRNA ligase)

e-103 e-106

39 36

379 268

1.00E-

45

116

72 5.00E-

36

311

27 2.00E-

41

152

85 4.00E-

43

119

38 4.00E-

48

216

52 6.00E-

36

300

57 3.00E-

45

(ARGRS) 7. glyceraldehyde 3-phosphate dehydrogenase 8. ABC transporter, ATP-binding protein 9. aminopeptidase C 10.GTP cyclohydrolase 11.pentose-5-phosphate-3-epimerase 12.UDP-galactose 4-epimerase 13.phosphoglycerate kinase

82

14.class I heat-shock protein (molecular chaperone) 566 15.dnaJ protein, Heat-shock protein (activation of

e-162 5.00E-

DnaK)

50 36

213

56

16.CTP synthase (UTP--ammonia ligase)

472

e-134

46

17.CH60_SMI 60 kDa chaperonin (protein Cpn60)

508

e-144

50

1


6.00E18.exodeoxyribonuclease III

162

41 4.00E-

36

19.anthranilate synthase component I 286 20.anthranilate synthase component II (glutamine

78 7.00E-

35

134

33 1.00E-

42

21.anthranilate phosphoribosyltransferase

205

53

37

22.tryptophan synthase, beta subunit

478

e-135 6.00E-

60

23.aquaporin Z-water channel protein 115 24.phosphoglycerate dehydrogenase-related protein,

27 5.00E-

35

GTP-binding protein 25.2,3-bisphosphoglycerate-dependent

94

41

amido-transferase)

phosphoglycerate

339 mutase

(phosphoglyceromutase)

3.00E253

68 6.00E-

53

26

42

e-126

39

e-103 5.00E-

51

283

77 5.00E-

40

30.methionine sulfoxide reductase

109

25

35

31.V-type H+-ATPase, subunit A

558

e-159

52

32.V-type H+-ATPase, subunit B 33.ATP-dependent Clp protease,

533

e-152

56

e-146 2.00E-

42 48

26.D-tyrosyl-tRNA(Tyr) deacylase 111 27.Threonyl-tRNA synthetase, threonine-tRNA ligase

446

28.UDP-glucose 4-epimerase 368 29.superfamily II DNA and RNA helicases ATPdependent RNA helicase, DEAD-box family

subunit 34.FolD

bifunctional

methylenetetrahydrofolate

ATP-binding

protein;

513 includes: 217

dehydrogenase,

1

57


methenyltetrahydrofolate cyclohydrolase 9.00E35.hypothetical protein

105

24

35

36.hypothetical protein

373

e-104

40

37.Translation elongation factor TU

396

e-111 7.00E-

50

38.pyrroline-5-carboxylate reductase 139 39.DNA-dependent RNA Polymerase sigma factor

34 5.00E-

36

206

54 2.00E-

41

40.6-phosphofructokinase I

194

50 3.00E-

38

41.pyruvate kinase I; fructose-stimulated

297

81 6.00E-

37

42.GTP-binding protein LepA

100

22 1.00E-

37

43.uracil-DNA glycosylase

211

55 2.00E-

50

44.peptide chain release factor I

304

83

42

45.enolase

395

e-111 2.00E-

50

46.cell division protein FtsY

154

38 4.00E-

35

47.2-isopropylmalate synthase

306

84

42

48.carbamoyl-phosphate synthase, large subunit

801

0 7.00E-

41

49.carbamoyl-phosphate synthase, small subunit

229

61

37

50.signal recognition particle protein Ffh

385

e-107

46

51.phosphoglycerate mutase 380 52.proton-translocating ATPase, F1 sector, alpha-

e-106

43

e-153

57

rpoD

subunit

535

1


2.00E53.pyridoxal-phosphate dependent aminotransferase 207 54.glutamine amidotransferase involved in

54 2.00E-

37

139

34 1.00E-

39

344

95

57

e-112 6.00E-

54

25

35

58.P-type ATPase-probable copper transporter 445 59.TypA, predicted membrane GTPase involved in

e-125 8.00E-

40

stress response 100 60.chorismate binding enzyme para-aminobenzoate

22 2.00E-

36

169

42 2.00E-

36

61.cell wall surface anchor family protein

260

69 3.00E-

40

62.tRNA nucleotidyltransferase

117

27 6.00E-

38

63.hypothetical protein

128

31 3.00E-

39

64.triose phosphate isomerase

169

43 5.00E-

39

65.adenine phosphoribosyltransferase

158

40 ######

46

66.50S ribosomal protein L11

177

# 3.00E-

63

67.cysteinyl-tRNA synthetase

201

52 1.00E-

44

68.serine/threonine protein kinase 69.guanylate kinase

132 135

31 7.00E-

36 39

pyridoxine biosynthesis 55.glycyl-tRNA synthetase alpha subunit

56.S-adenosylmethionine synthetase 399 57.cell division ABC transporter, ATP-binding protein FtsE

108

synthetase

1


33 70.acetyl-CoA

carboxylase

biotin

carboxylase

subunit

419

e-118 1.00E-

47

285

77

36

499

e-142 2.00E-

43

73.30S ribosomal protein S9

184

48 ######

70

74.50S ribosomal protein L13

195

# 2.00E-

64

75.hypothetical protein

207

55

86

76.phenylalanyl-tRNA synthetase, alpha chain

445

e-126 5.00E-

60

77.rRNA methylase

177

46

52

78.pyruvate formate-lyase

610

e-175

43

79.undecaprenyl-diphosphatase 515 80.ABC transporter permease and substrate-binding

e-147 4.00E-

93

protein, amino acid transport 133 81.ABC transporter ATP-binding protein, amino

32 3.00E-

41

195

51 4.00E-

46

82.threonine dehydratase

229

61 4.00E-

37

83.ketol-acid reductoisomerase

332

92 1.00E-

49

84.acetolactate synthase, small subunit

129

31

43

85.acetolactate synthase, large subunit

484

e-138 2.00E-

46

86.kinase

340

94

35

71.HSP70 family protein 72.ATP-dependent Clp

protease,

ATP-binding

subunit

acid transport

1


87.ABC transporter, permease and ATP-binding protein, multidrug export

2.00E212

55 8.00E-

37

88.nucleoside diphosphate kinase

140

35 1.00E-

48

89.undecaprenyl diphosphate synthase

121

28 6.00E-

35

90.ABC tranporter, ATP-binding protein

129

31

37

91.leucyl-tRNA synthetase

915

0 2.00E-

53

92.adenylate kinase

153

38 4.00E-

36

93.50S ribosomal protein L6

152

38 4.00E-

44

94.DNA mismatch repair protein hexB

191

49 6.00E-

36

95.argininosuccinate lyase

352

98 4.00E-

42

96.argininosuccinate synthase

320

88

42

97.glycerol kinase 98.ATP-dependent

405

e-114 1.00E-

45

subunit 280 99.ABC transporter ATP-binding protein cobalt

75 2.00E-

56

134

32 4.00E-

36

317

87

40

Clp

protease,

ATP-binding

transport 100.

inosine monophosphate dehydrogenase

TABLE 4:TARGET PROTEINS Name of the molecule

DEG score

1


Scoree45 1. cell-division protein 2. signal recognition

7 particle 38

e-129

44

yes Membrane Cytoplasmi

5 17

e-107 ######

46

yes c Cytoplasmi

Membrane

2J28

3. 50S ribosomal protein L11

7 19

# ######

63

yes c Cytoplasmi

Membrane

2K3F

4. 50S ribosomal protein L13

5 40

#

64

yes c Cytoplasmi

Extracellular 2GYA

yes c pdbid

Membrane

protein Ffh

5. glycerol kinase 5 e-114 45 valueidentityaccepted/notcelloprediction

DOCKING SCORE FOR THE MOLECULE TABLE 5: PDB ID: 3KDS Sl.no 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Name of the molecule alantolactone allicin Allohydroxycitric acid Allyl isothiocyanate Alpha phellandrene Alpha_pinene Alpha_terpineol Anagyrine Anethol antraquinone Apiole Arbutin Aromadendrene Ascorbic acid Asparagine Azulene Baptifoline Benzaldehyde

Docking score -7.4 -3.8 -6.2 -3.6 -5.4 -5.1 -5.5 -6.4 -5.2 -7.5 -5.9 -6.7 -6.1 -5.8 -4.7 -5.3 -7.0 -4.3 1

3KDS

3H3N


19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57

Berbarine Bergamotine Beta_asarone Beta_pinene Bisabolene Bisabolol boldine Borneol Boronyl acetate Camphene Camphor Canadine Caprylic acid Capsaicin Carnosic acid Carvacrol Caryophyllene Caulophylline Choline Cineole Citral Citronellol Columbamine Coptisine Coumarin Cuminaldehyde Cymene Delta_candinene Diosphenol Dipentene Diterpene D_limonene Elemicin Eugenol Eugenol methyl ether Eupatorin Galangin Geraniol Hibiscus acid

-8.5 -7.3 -5.4 -5.0 -6.5 -6.4 -7.4 -5.4 -5.6 -4.1 -5.3 -8.3 -4.3 -7.1 -8.1 -5.6 -6.2 -6.6 -3.5 -5.0 -4.9 -4.6 -8.6 -9.3 -6.0 -5.7 -5.0 -6.5 -5.5 -4.8 -8.1 -5.2 -5.1 -5.7 -5.2 -8.1 -8.0 -4.8 -6.2 1


58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84

Humulene Kaempferol Kavain Limonene Linalool Linlyl acetate Malic acid Meconin Methyl cinnamate Methysticin Myrcene Nerolidol Ocimene Populene Quercetin Sabinene Safrol Salicin Sanguinarine Singirin Terpinene Thujone Thymol Vanillic acid Vincamine Xylose Yohimbine

-6.1 -7.9 -7.2 -4.8 -5.0 -5.1 -5.1 -5.8 -5.9 -7.2 -4.7 -5.9 -4.8 -8.0 -7.9 -4.9 -5.5 -6.5 -9.9 -6.3 -5.2 -5.1 -5.2 -6.0 -7.6 -5.3 -8.4

TABLE 6: PDB ID: 3H3N Sl.no 1 2 3 4 5 6

Name of the molecule alantolactone allicin Allohydroxycitric acid Allyl isothiocyanate Alpha phellandrene Alpha_pinene

Docking score -7.6 -3.8 -6.1 -2.9 -5.2 -5.4

1


7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

Alpha_terpineol Anagyrine Anethol antraquinone Apiole Arbutin Aromadendrene Ascorbic acid Asparagine Azulene Baptifoline Benzaldehyde Berbarine Bergamotine Beta_asarone Beta_pinene Bisabolene Bisabolol boldine Borneol Boronyl acetate Camphene Camphor Canadine Caprylic acid Capsaicin Carnosic acid Carvacrol Caryophyllene Caulophylline Choline Cineole Citral Citronellol Columbamine Coptisine Coumarin Cuminaldehyde Cymene

-5.4 -6.7 -4.9 -6.9 -5.5 -7.1 -6.5 -5.6 -4.8 -5.4 -7.4 -4.2 -7.7 -7.1 -5.0 -5.4 -6.5 -5.5 -6.9 -5.6 -5.8 -5.3 -5.4 -7.3 -4.2 -5.7 -7.7 -5.6 -6.8 -6.2 -3.5 -5.5 -4.7 -4.7 -7.0 -8.8 -5.5 -5.1 -5.3 1


46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84

Delta_candinene Diosphenol Dipentene Diterpene D_limonene Elemicin Eugenol Eugenol methyl ether Eupatorin Galangin Geraniol Hibiscus acid Humulene Kaempferol Kavain Limonene Linalool Linlyl acetate Malic acid Meconin Methyl cinnamate Methysticin Myrcene Nerolidol Ocimene Populene Quercetin Sabinene Safrol Salicin Sanguinarine Singirin Terpinene Thujone Thymol Vanillic acid Vincamine Xylose Yohimbine

-6.5 -6.0 -5.2 -7.9 -5.3 -5.1 -5.3 -4.8 -7.4 -7.5 -4.9 -5.6 -6.4 -7.9 -6.5 -5.2 -5.0 -5.2 -4.7 -5.6 -5.0 -7.3 -4.9 -5.6 -4.9 -7.8 -8.0 -5.1 -5.2 -6.7 -8.1 -7.2 -5.3 -5.2 -5.4 -5.6 -7.2 -5.3 -7.6 1


TABLE 7&8:MOLECULES RANKED BASED UPON THEIR DOCKING SCORE PDB ID: 3KDS Sl.no 1 2 3 4 5

Name of molecule sanguinarine Coptisine Columbamine Berbarine Yohimbine

Dock score -9.9 -9.3 -8.6 -8.5 -8.4

Rank 1 2 3 4 5

Dock score -8.8 -7.9 -7.9 -7.8 -7.7 -7.7

Rank 1 2 3 4 5 6

PDB ID: 3H3N Sl.no 1 2 3 4 5 6

Name of molecule Coptisine Diterpine Kaempferol Populene Carnosic acid Berbarine

DISCUSSION: Around 100 small molecules from different categories such as alkaloids, flavonoids, tannins, glycosides were taken as targeting agents that are responsible 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

1


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 Lipinskiâ&#x20AC;&#x2122;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 â&#x20AC;&#x153;lead compoundsâ&#x20AC;? 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 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

1


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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TO FIND A POTENT ANTI BACTERIAL AGENT FOR CELL WALL PROTEIN USING BIOINFORMATICS