KR20150005239A - Pharmaceutical Compositions for Preventing or Treating a Microorganism Infection Disease Comprising a Chemical Compound with an Inhibitory Activity Against Phosphotransacetylase - Google Patents

Abstract

The present invention relates to a method for identifying ligand for phosphotransacetylase of Staphylococcus aureus using a virtual screening method and to a pharmaceutical composition for preventing or treating microorganism infectious diseases comprising the ligand as an active ingredient. The in silico virtual screening method of the present invention analyzes tertiary structure of target protein, and analyzes interactions between the target protein and reference ligand using a molecular dynamics simulation method, thereby predicting activity position of the target protein using homology modeling. Based on the above facts, the present invention identifies drug-like molecules having increased bond energy for the target protein and pharmacokinetics properties through docking analysis between the target protein and lead ligand library. The present invention identifies a compound represented by chemical formula I as a Staphylococcus aureus-derived phosphotransacetylase inhibitor using the above method. Therefore, the method of the present invention can rapidly and efficiently predict large amount of physiological active candidate substances based on proteomics, bio-informatics, and combinational chemistry, thereby being able to drastically reduce new drug development costs by being applied to a new drug development field.

Description

FIELD OF THE INVENTION The present invention relates to a pharmaceutical composition for preventing or treating a bacterial infection disease comprising a compound having a phosphotransacetylase inhibitory activity,

The present invention relates to a method for identifying a ligand for phosphotransacetylase of Staphylococcus aureus using a virtual screening method and a method for identifying a ligand for phosporotransacetylase in a pharmaceutical composition for the prevention or treatment of a bacterial infectious disease comprising the ligand will be.

Staphylococcus aureus causes a variety of purulent infections and toxinoses in humans. Staphylococcus aureus is a skin surface lesion such as boils, styes and furuncules; More severe infections such as pneumonia, mastitis, phlebitis, meningitis and urinary tract infections; And deep-seated infections such as osteomyelitis and endocarditis [1-5]. Staphylococcus aureus is a major causative agent of infections associated with hospital-acquired (nosocomial) infections and indwelling medical devices for surgical wounds [1, 3, 4]. Staphylococcus aureus releases enterotoxin into food causing food poisoning and discharging super-antigens into the bloodstream, resulting in toxic shock syndrome (TSS) [6, 7 ]. Staphylococcus is a surface protein that promotes host colonization of host tissue; Invasins (hyaluronidase, kinase and leucocidin); Surface factors (capsules and protein A); Immune gastrointestinal (protein A and coagulase); Membrane-damaging toxins (hyalolcin, leukotoxin and leucocidin); Exotoxin (SEA-G, TSST and ET); (Carotenoids and catalase production) with biochemical properties that enhance survival, including innate and acquired resistance to antimicrobial agents [8-13]. Designing antibiotics targeting the aforementioned pathogenic factors is not an easy task. The incidence of Staphylococcus aureus, an antibiotic resistance strain, is so high that 90% of strains are penicillin resistant and 50% of strains have multidrug resistance, so the war between them is ongoing [3, 14] . Targeting specific molecules is not sufficient to develop effective antimicrobial agents. Pseudomonas Ke Rouge labor (Pseudomonas aeruginosa) [15, 16 ], mycoplasma to nital Solarium (Mycoplasma genitalium) [17], Aspergillus peogil Russ (Aspergillus) [18, 19, 53] and Helicobacter pylori (Helicobacter pylori) [15, 20] have been used to investigate novel drug targets in other pathogenic organisms. As is most commonly known, antimicrobial agents are essentially inhibitors of certain bacterial enzymes: Bacteria-specific enzymes can be considered as potential drug targets [5, 18, 19, 21].

In a previous study of the present inventors, we conducted a comparative analysis of metabolic pathways to discover some potential targets for Staphylococcus aureus [5]. Additional drugabiltiys were observed for the enzymes that were observed to be specific compared to the host proteome. As a result, in the present study, the present inventors investigated phosphotransacetylase (PTA) as a potential target. PTA (EC: 2.3.1.8) is a 328-amino-enriched enzyme encoded by the etuD gene, also known as phosphate acetyltransferase, phosphotransacetylase and phosphoacylase [22, 23]. The enzyme is involved in three different pathways (i. E., The taurine and hypotaurine metabolism pathway, the pyruvate metabolism pathway and the propanoate metabolic pathway) (KEGG database). Acetyl-CoA or acetate, an intermediate compound, plays a very important role in completing the energy required process [5, 24]. PTA can cleave short-chain CoA esters such as propionyl-CoA and butyryl-CoA other than acetyl-CoA, but can not hydrolyze long-chain fatty acid acyl CoA such as succinyl-CoA or palmityl-CoA [ 25]. Many anaerobic / fermentative / aerobic / methanogenic microorganisms that utilize these compounds through specific pathways can convert acetyl-CoA to acetate by the action of phosphotransacetylase, and acetate kinase is able to convert most energy-providing ATP It is used for production. Acetate is the final product of virtually all anaerobic / fermentative / aerobic / methanogenic microorganisms, and subsequently functions as a growth substrate, even though the main role of acetate is involved in the acetate metabolism [26,27]. Therefore, inhibition of PTA can effectively inhibit growth and proliferation of Staphylococcus aureus. To date, none of the structural or drug-targeting information for Staphylococcus aureus using PTA is available.

Numerous papers and patent documents are referenced and cited throughout this specification. The disclosures of the cited papers and patent documents are incorporated herein by reference in their entirety to better understand the state of the art to which the present invention pertains and the content of the present invention.

The present inventors have sought to develop a high-throughput virtual screening system for identifying drug targets for the treatment of bacterial infectious diseases and for developing inhibitors thereof. As a result, the present inventors virtually identified a reference ligand binding position to the target protein by structural analysis of a target protein (for example, phosphotransacetylase) and homology modeling using a reference ligand, The present invention was accomplished by identifying drug-like molecules having increased binding energy and pharmacokinetic properties to the target protein through docking analysis between the protein and the lead ligand library.

It is therefore an object of the present invention is a drug-to provide a virtual in silico screening of a ligand (ligand) having a similar activity (drugability) (In silico virtual screening ) methods.

It is another object of the present invention to provide a pharmaceutical composition for preventing or treating bacterial infectious diseases.

Other objects and advantages of the present invention will become more apparent from the following detailed description of the invention, claims and drawings.

According to one aspect of the invention there is provided a drug comprising the steps of - providing an in silico virtual screening (In silico virtual screening) methods of the ligand (ligand) having a similar activity (drugability): (a) the target Analyzing the interaction between a protein and a reference ligand to produce a homology modeling, wherein the three-dimensional structure of the interaction between the target protein and the reference ligand is determined by computer program And obtaining the physicochemical properties of the reference ligand binding to the target protein based on the model; (b) preparing a candidate ligand library, wherein said ligand library conforms to the physicochemical properties of the reference ligand obtained in said step (a) and is characterized by a rule for predicting drug-likeness Made to conform; (c) docking the target protein of step (a) and the ligand prepared in step (b), wherein the docking process is performed using a computer program using a MolDock algorithm, the docking score ), The ligands within the top 30 docking scores are selected and their pharmacological properties are analyzed to determine drug compliance; And (d) analyzing the activity change of the target protein by the ligand identified in the step (c). When the identified ligand inhibits the activity of the target protein, it is judged to be a target protein inhibitor In silico virtual screening method.

The present inventors have sought to develop a high-speed virtual screening system for identifying drug targets for the treatment of bacterial infectious diseases and for developing inhibitors thereof. As a result, the present inventors have virtually identified a reference ligand binding position to the target protein by structural analysis of a target protein (for example, phosphotransacetylase) and homology modeling using a reference ligand, Drug-like molecules with increased binding energy and pharmacokinetic properties to the target protein were identified through docking analysis between the material ligand libraries.

According to the present invention, the present invention provides a database (or computer program) -based primary, secondary and tertiary structure analysis of a target protein (e.g., a phosphotransacetylase of the invention) To predict the active site of the target protein as a drug target (sequence alignment and homology modeling). Then, the interaction (binding) between the target protein and the reference ligand is analyzed by a molecular dynamics simulation method. Based on the above results, a candidate substance ligand having an increased binding energy and pharmacokinetic characteristics to the target protein in the interaction between the target protein and the candidate ligand library is identified.

The in silico virtual screening method of the present invention will be described in detail as follows.

(a) analyzing the interaction between a target protein and a reference ligand to produce a homology modeling;

First, a target protein essential for survival of an organism (specifically, an infectious bacterium) is selected. The target protein is a protein essential for the survival of an organism and is a protein capable of acquiring a nucleotide sequence or an amino acid sequence from a publicly available database (for example, NCBI server, GenBank, etc.) and inhibiting the growth of the organism through its inhibition Any protein can be used.

According to some embodiments of the present invention, the target protein of the present invention is an essential protein essential for microbial-derived survival, and is a protein having a very low sequence homology with a human protein. More specifically, the microbial- Is a protein essential for survival derived from the genus Staphylococcus (Genus Staphylococcus ), and is a protein having an amino acid sequence of 50% or less And most specifically Staphylococcus aureus-derived phosphotransacetylase, which is a protein which does not show very low amino acid sequence homology with human protein.

According to the present invention, the secondary and tertiary structures of the target protein are analyzed using the above-described amino acid sequence of the target protein. The nucleotide sequence and amino acid sequence of the phosphotransacetylase, the target protein of the present invention, are disclosed in SEQ ID NO: 1 and SEQ ID NO: 2. The secondary structure of the target protein can be obtained from a well known database (e.g., BLASTp, PROSITE, SOSUI, SOPMA, STRIDE, CYS_REC ( http://suni.softberry.com/berry.phtml?topic ), ClustalW, LALIGN, 2ZIP, etc.). In the presence of proteomic analysis results, it is possible to analyze the intracellular location using a PA-SUB server (proteome-specific intracellular location server v2.5).

The target protein of the present invention, Staphylococcus aureus-derived phosphotransacetylase, has high sequence homology with phosphotransacetylase of other microorganisms (bacteria), while it has amino acid sequence homology with human protein (Note: Table 1 and Fig. 2).

The tertiary structure is modeled through homology modeling based on the amino acid sequence analysis (primary and secondary structure analysis) of the target protein of the present invention. Modeling of the tertiary structure is performed using methods well known in the art, for example, using database-based modeling, X-ray crystallography, NMR methods, and the like.

According to some embodiments of the present invention, the three-dimensional structural analysis of the interaction between the target protein and the reference ligand in step (a) of the present invention is based on the sequence-based secondary structure analysis of the target protein, It is modeled through virtual structure prediction using a computer program.

According to some embodiments of the present invention, the computer program (or database) available in step (a) of the present invention includes, but is not limited to PROCHECK, WHAT IF, SuperPose, YASARA and ESyPred3D.

Based on the predicted 3D modeling structure, the interaction between the target protein and the reference ligand (e.g., PTA and acetyl phosphate) is analyzed to obtain the physicochemical properties of the reference ligand.

As used herein, the term "reference ligand" refers to a substance (e.g., a compound) known in the art that interacts with (or binds to) a target protein, And used as a control group.

According to some embodiments of the invention, the physical and chemical properties measured in step (a) of the present invention, molecular weight, isoelectric point (isoelectric point, I p), the calculated value LopP, H- bond donor, H- bond acceptors, CC The number of heavy atoms, the number of rotatable couplings (number of distortions), the polar surface area (Å ² ), the polar or apolar desolvation energy (Kcal / mol), the extinction coefficient but are not limited to, coefficient, instability index, aliphatic index (AI), and GRAVY (grand average hydropathy).

According to some embodiments of the present invention, the structural information on S. pneumoniae using PTA is presented for the first time in the present invention as information on the structure of PTA not available to date.

(b) preparing a candidate ligand library :

The ligand library of the present invention is prepared from a small compound library having characteristics similar to the physicochemical properties of the above-mentioned reference ligand. The ligand library is also prepared to conform to the rules for predicting oral bioavailability and drug-likeness.

As used herein, the term "drug-likeness" is a quantitative concept that has been used in the design of drug-like materials and can be predicted through the following factors, No: (a) partition coefficient (LopP) to predict solubility in water and fat; (b) an IC ₅₀ value (half maximal inhibitory concentration) indicative of the binding activity of the candidate substance to the target; (c) various scoring methods that can be used to express physicochemical properties such as ligand efficiency and lipophilic efficiency; (d) H-bond donors and H-bond acceptors; (e) molecular weight (e.g., 450 Da or less); And (f) a structure that exhibits known compound properties or pharmacological properties.

Prior art techniques for assessing drug compliance use Lipinski's Rule of Five, which includes an assessment of the number of hydrophilic groups, molecular weight, and hydrophobicity. Theoretically, the properties of the drug-like molecules according to the Lipinsky rules are LogP in the range of-0.4 to 5.6, molecular weight of 160 to 480 g / mol and molar refractivity of 40 to 130.

According to some embodiments of the present invention, the rules for predicting drug compliance in step (b) of the present invention may be based on Lipinski's rule, CMC-like rule, MDDRF-like rule, But is not limited to, WDI-like rules. More specifically, the rule for predicting drug compliance in step (b) of the present invention is Lipinski's rule.

The Ripening rule, also referred to as Pfizer's rule of five or RO5, may be used to assess drug compliance or to determine whether a compound having a particular pharmacological or biological activity is suitable as a bioactive agent capable of oral administration to humans It is a rule. The Ripin's rule is roughly as follows: (a) no more than 5 H-bond donors; (b) up to 10 H-binding receptors; (c) a molecular weight of less than 500 Da; And (d) a partition coefficient (LogP) of 5 or less. The Ripening rule contains molecular characteristics important for pharmacokinetics in humans including ADME (absorption, distribution, metabolism and excretion), but it is not possible to predict whether a compound is pharmacologically active and in exceptional cases , The Baldwin rule), the rules are being used in various ways.

According to some embodiments of the present invention, the Ripening rule used in the present invention uses 1.5? XlogP? 5, 2? H-bond donor? 5, 5? H-bond acceptor? 10 and 140.03? Molecular weight? 500 However, it can be modified depending on the interaction of the target protein with the reference ligand (or the physicochemical properties of the reference ligand).

The CMC-like rule is similar to the Ripin 's rule, but has a drug-like property to the CMC database in which several classes of compounds have been removed, such as diagnostic imaging agents, solvents and pharmacological aids . The MDDRF-like rule is applied to the ACDF and MDDRF subsets to produce similar results. The ACD database is a non-drug database and the MDDRF is a drug database. ACDF and MDDRF are databases in which reactive functional groups, including acyl-halides, sulfonyl-halides, Michael acceptors, and the like, are removed. The WDI-like rule is calculated on the basis of a compound with more than 90% of the molecular characteristics found in the World Drug Index (WDI), using preADME as a rule based on the compound. PreADME consists of calculation of physical properties of molecules, estimation of experimental values related to absorption, and prediction of drug compatibility. It is possible to predict each information related to absorption, distribution and metabolism on the pharmacokinetic behavior of various compounds on a computer. It provides experimental data for several published compounds. For example, it is possible to predict the permeability of Caco2-cells, MDCK cells, and blood-brain barrier permeability, which is an experimental model to examine the solubility of compounds and their absorption in the body.

The candidate substance ligand library which is an active ingredient of the present invention can be prepared using a compound database available in the art based on the initial values of the reference ligand and can be prepared using, for example, a ZINC database or a ChEMBL database But is not limited thereto.

The ZINC database provides a curated collection of commercially useful compounds prepared for virtual screening and is used by researchers (usually experts such as biologists or chemists) working in pharmaceutical companies, biotechnology companies and universities Lt; / RTI > The ZINC database is provided by the Shoichet laboratory of the Department of Drug Chemistry, UCSF (University of California, San Francisco). The ZINC database also provides a reference ligand that binds to the target protein (see: the DUD database).

In addition, the ChEMBL database (ChEMBLdb, formerly known as StARlite) was developed by Inpharmatica Ltd., a biotechnology company, as a compound database of bioactive molecules with drug-like activity established by hand . The ChEMBL database contains over 2 million bioassay results for 622,824 compounds containing 24,000 natural products.

According to some embodiments of the invention, the binding sites in the phosphotransacetylase (SEQ ID No. 2) predicted for homology modeling of the present invention are Arg88, Arg89, Arg134, Arg148, Cys167, Arg285, Arg308 and Cys310 ≪ / RTI > of the amino acid sequence of SEQ ID NO.

(c) docking the target protein of step (a) and the ligand prepared in step (b)

In the docking step of the present invention, a candidate substance ligand is selected by calculating a docking score indicating a factor indicating the possibility of binding between the target protein and the candidate substance ligand, and its drug compatibility is determined.

In the present invention, virtual docking between a target protein and a candidate substance ligand can be performed using software available in the art. Specifically, MolDock, a molecular docking algorithm using Molegro Virtual Docker version 1.1.1 software, (Note: Docking analysis section in the experimental method). The docking process can be performed by using a MolDock scoring function to analyze (i) the binding site, (ii) the surface change due to binding between the ligand and protein, and (iii) the scoring function or energy calculation software Conduct. As used herein, the term "scoring functions" refers to the fastest mathematical function used to predict the strength (i.e., binding affinity) of non-covalent interactions between two molecules after docking between ligand- Methods, and are generally used for the evaluation of small compounds such as drugs. Essential parameters for the application of the scoring function are: (a) the tertiary structure of the target protein; (b) a 3D form (active form) of the ligand bound to the target protein; And (c) a binding-mode indicating the orientation of each partner in the ligand-protein complex.

In addition, the docking process using the software determines the docking position (i.e., binding position in the protein) repeatedly (specifically, 10 or more) in order to obtain statistically significant results. We then compare the interaction energy between the ligand and the protein with the highest ranking MolDock score.

Through the docking analysis described above, a pharmacophore model is constructed. At this time, the molDock score, hydrogen bonding energy, charge interaction, or hydrophobic region for each ligand is calculated and the Ripin's rule is applied to remove molecules that do not exhibit drug-like properties, The ligands that exhibit binding affinity are finally selected.

According to certain embodiments of the present invention, the pharmacological properties of the candidate material ligand selected at step (c) of the present invention are determined by ligand scout software, START software, ADME (absorption, distribution, metabolism and excretion) It is predicted through Toxtree software and is used to manufacture pharmacophore models.

According to some embodiments of the present invention, the candidate material ligand identified in step (c) of the present invention comprises a ligand within the top thirty top docking score, and more specifically within the top five positions.

(d) analyzing the activity change of the target protein by the ligand identified in the step (c)

Finally, when the identified ligand inhibits the activity of the target protein, it is determined to be a target protein inhibitor. The activity of the target protein can be assayed using methods known in the art. For example, the activity measurement of the phosphotransacetylase of the present invention can be easily carried out by measuring the reaction product, acetyl-CoA. For example, a continuous spectroscopic measurement of the acetyl-CoA at 233 nm (Continuous Spectrophotometric Rate Determination), HPLC analysis methods, and the like.

According to some embodiments of the present invention, the candidate substance ligand identified through the method of the present invention comprises a compound represented by Formula 1-5.

According to another aspect of the present invention, there is provided a pharmaceutical composition comprising (a) a therapeutically effective amount of a compound represented by formula (I); And (b) a pharmaceutically acceptable carrier. The pharmaceutical composition for preventing or treating a bacterial infectious disease comprises:

Formula I

또는

로 상기 n은 1-5의 정수이고, 상기 R₅는 5각 또는 6각형의 아릴 또는 헤테로아릴이며, 상기 헤테로아릴에서 하나의 헤테로원자인 황을 포함할 수 있는 것을 특징으로 하는 약제학적 조성물.In the formula (I), R ₁ and R ₂ are each independently of the other H or C ₁ -C ₁₀ straight or branched chain alkyl or alkenyl; R ₃ is C ₁ -C ₁₀ straight or branched chain alkyl or C ₆ -C ₁₀ aryl; Or R < _{4 &}

or

Wherein n is an integer from 1 to ₅ , and R < ₅ > is a pentane or hexaaryl aryl or heteroaryl, wherein the heteroaryl may contain sulfur, which is a single heteroatom.

The term "alkyl" used in the above formula (I) means a straight chain or branched, unsubstituted or substituted saturated hydrocarbon group, for example methyl, ethyl, propyl, isobutyl, Pentyl, hexyl, heptyl, octyl, nonyl, decyl, undecyl, tridecyl, pentadecyl and heptadecyl and the like. C _1-10 alkyl means an alkyl group having an alkyl unit of 1 to 10 carbon atoms, and when C _1-10 alkyl is substituted, the carbon number of the substituent is not included. The C _1-10 alkyl at the R ₁ or R ₂ position in formula (1) is specifically C _1-9 alkyl, more particularly C _1-7 alkyl, and more particularly C _1-3 alkyl .

The term "alkenyl" used in the above formula (I) represents a straight-chain or branched unsubstituted or substituted unsaturated hydrocarbon group having a designated number of carbon atoms such as ethenyl, vinyl, propenyl, allyl, isopropenyl, N - pentenyl, n - pentenyl, isobutenyl, t - butenyl, n - pentenyl and n - hexenyl. C _2-10 alkenyl means an alkenyl group having an alkenyl unit having 2 to 10 carbon atoms, and when C _2-10 alkenyl is substituted, the number of carbon atoms of the substituent is not included. The C _2-10 alkenyl at the R ₁ or R ₂ position in formula (I) is specifically C _2-9 alkenyl, more specifically C _2-7 alkenyl, and still more specifically, most particularly, _{Lt; /} RTI > alkenyl.

According to certain embodiments of the present invention, R ₁ and R ₂ in formula I of the present invention are each independently of the other H, C ₁ -C ₁₀ straight or branched chain alkyl or C ₂ -C ₁₀ straight or branched chain al And more specifically, each independently of one another, H, C ₁ -C ₅ straight or branched chain alkyl or C ₂ -C ₅ straight or branched chain alkenyl.

The term "aryl" used in the above formula (I) means a substituted or unsubstituted monocyclic or polycyclic carbon ring which is totally or partially unsaturated. In the formula (I), C _6-10 aryl in the R ₃ position means an aryl group having 6 to 10 carbon ring atoms, and when C _6-10 aryl is substituted, the number of carbon atoms in the substituent is not included. Specifically, the aryl of the present invention is monoaryl or biaryl, more specifically, monoaryl has 5 to 6 carbon atoms, and the biaryl has 9 to 10 carbon atoms, and examples thereof include a phenyl group, a substituted phenyl group, Butyl, substituted naphthyl, and the like. For example, when phenyl is substituted, substitution can be made by various substituents at various positions. For example, halo, hydroxy, nitro, cyano, C ₁ -C ₅ substituted or unsubstituted straight or branched Alkyl or C ₁ -C ₅ straight chain or branched chain alkoxy. The term "C ₁ -C ₅ alkoxy" as used herein includes, but is not limited to, methoxy, ethoxy, propoxy, and the like. The aryl group substituent may specifically include a small number of alkyl or halogen. More specifically, the aryl of the present invention is substituted or unsubstituted phenyl.

According to some embodiments of the present invention, R ₃ in Formula I of the present invention includes C ₁ -C ₁₀ straight or branched chain alkyl or C ₆ -C ₁₀ aryl, more specifically C ₁ -C ₅ straight chain Or branched alkyl or C ₆ aryl.

또는

로 상기 n은 1-5의 정수이다.
According to certain embodiments of the present invention, R < ₄ > in formula (I)

or

And n is an integer of 1-5.

According to certain embodiments of the present invention, R < ₅ > in R < ₄ > in formula I of the present invention is a pentane or hexaaryl aryl or heteroaryl and includes sulfur, which is a heteroatom in said heteroaryl.

The term "heteroaryl containing sulfur (S) as heteroatom" used in formula (I) above is a heterocyclic aromatic group containing S as a heteroatom. C _3-10 heteroaryl means a heteroaryl group having 3 to 10 carbon-carbon atoms, and when C _3-10 heteroaryl is substituted, the number of carbon atoms of the substituent is not included. Specifically, the number of heteroatoms is 1 to 4, more specifically 1 to 2, and more specifically 1. In heteroaryl, aryl is specifically monoaryl. Heteroaryl may be substituted by various substituents at various positions and includes, for example, halo, hydroxy, nitro, cyano, C ₁ -C ₅ substituted or unsubstituted straight or branched chain alkyl, C ₁ -C ₅ straight chain Or by alkoxy of the branched chain.

According to certain embodiments of the present invention, the compounds represented by formula (I) of the present invention include compounds represented by the following formulas (1-5)

[Chemical Formula 1]

(2)

(3)

[Chemical Formula 4]

[Chemical Formula 5]

According to certain embodiments of the present invention, the compounds of formula I of the present invention inhibit the activity of phosphotransacetylase (PTA), and more particularly, the phosphotransacetylase of SEQ ID NO: And the amino acid sequence selected from the group consisting of Arg88, Arg89, Arg134, Arg148, Cys167, Arg285, Arg308, and Cys310 of the lysine to inhibit the activity of the phosphotransacetylase.

According to some embodiments of the present invention, the compound represented by formula (I) of the present invention has an inhibitory activity against Staphylococcus aureus -derived phosphotransacetylase.

In accordance with the present invention, the Staphylococcus aureus -derived phosphotransacetylase of the present invention can be used in combination with the reported bacteria and archaea (e.g., Bacillus subtilis , It has high sequence homology with Methanosarcina thermophila , Escherichia coli , Candida albicans or Streptococcus mutans ] PTA domains and has low homology with human PTA (Note: " Homology modeling and docking " part of FIG. 2 and experimental results).

(1-5) contained in the compound represented by formula (I) of the present invention showed an excellent inhibitory effect on phosphotransacetylase through in vitro evaluation (reference: experimental method and experimental result, FIG. 7 to FIG. 11). Specifically, the IC ₅₀ values of the compounds represented by Formulas 1 to 5 of the present invention against PTA were 0.0774 mg / ml, 0.0756 mg / ml, 0.0781 mg / ml, 0.0729 mg / ml and 0.1184 mg / ml, .

The term "IC ₅₀ values (half maximal inhibitory concentration)" as used herein, biologically or as an indicator to evaluate the efficacy of compounds that inhibit the biochemical activity, certain drugs, or other materials (e. G., Inhibitor) the specific biological processes or Provides a quantitative concentration measurement that is required to suppress up to half the components (e.g., enzymes, cell receptors, etc.) involved in the process. It is typically used to evaluate the potency of a drug as an antagonist in pharmacological studies.

In accordance with the present invention, the calculation of IC ₅₀ values for the compounds of the present invention was carried out using linear interpolation between concentrations of 50% or more and concentrations of 50% or less in the dose response curve. The basic IC ₅₀ value calculation was performed according to the method presented by Wyska et al. (Methods of estimation IC _{50 and} SC ₅₀ parameters for single dose data. J Pharm Sci. 2003 Jul; 92 (7): 1438-54 But the present invention is not limited thereto.

According to certain embodiments of the present invention, the compounds represented by formula (I) of the present invention may be used for the prevention or treatment of bacterial infectious diseases.

According to some embodiments of the present invention, the above-mentioned bacterial infectious disease is selected from the group consisting of dermatitis such as bloody fever, dermatitis, dermatitis and rubella, laryngitis, pneumonia, mastitis, phlebitis, meningitis, enteritis, urinary tract infection, osteomyelitis, keratitis, meningitis, , Purulent diseases, war wounds, food poisoning, sepsis, bacteremia, tuberculosis and toxic shock syndrome.

According to some embodiments of the present invention, the bacterium is an anaerobic, fermentable, breathable or methanogenic microorganism, more specifically a Gram-postive bacterium, and more particularly Staphylococcus , , Enterococcus (Enterococcus), Bacillus (Bacillus), Streptococcus (Streptococcus) and meta industrial scenarios (methanosarcina) in (genera) microorganisms, more still more specifically Staphylococcus aureus, Staphylococcus epidermidis Staphylococcus epidermidis , Bacillus subtilis, Streptococcus pneumoniae , Streptococcus pyogenes , Streptococcus agalactiae , and Methanosinha thermophyll, most specifically, Staphylococcus epidermidis , Staphylococcus epidermidis , Bacillus subtilis, Streptococcus pneumoniae , Streptococcus pyogenes , Streptococcus agalactiae , Staphylococcus aureus, Bacillus subtilis, Streptococcus mutans, and Meta But are not limited to, labor and thermophiles.

The term "pharmaceutically effective amount" as used herein means an amount sufficient to achieve the efficacy or activity (i. E., PTA inhibitory activity) of the compound described above.

The pharmaceutical composition of the present invention includes a pharmaceutically acceptable carrier. The pharmaceutically acceptable carriers to be contained in the pharmaceutical composition of the present invention are those conventionally used in the present invention and include lactose, dextrose, sucrose, sorbitol, mannitol, starch, acacia rubber, calcium phosphate, alginate, gelatin, But are not limited to, calcium silicate, microcrystalline cellulose, polyvinylpyrrolidone, cellulose, water, syrups, methylcellulose, methylhydroxybenzoate, propylhydroxybenzoate, talc, magnesium stearate and mineral oil. It is not. The pharmaceutical composition of the present invention may further contain a lubricant, a wetting agent, a sweetening agent, a flavoring agent, an emulsifying agent, a suspending agent, a preservative, etc. in addition to the above components. Suitable pharmaceutically acceptable carriers and formulations are described in detail in Remington ' s Pharmaceutical Sciences (19th ed., 1995).

The appropriate dosage of the pharmaceutical composition of the present invention may vary depending on factors such as the formulation method, administration method, age, body weight, sex, pathological condition, food, administration time, administration route, excretion rate, . The pharmaceutical composition of the present invention can be administered orally or parenterally, and when administered parenterally, it can be administered by intravenous injection, subcutaneous injection, muscle injection, intraperitoneal injection, transdermal administration, or the like. For example, the oral dosage amount of the pharmaceutical composition of the present invention may be 0.001-100 mg / kg body weight per day.

In addition, the pharmaceutical composition of the present invention preferably determines the route of administration according to the kind of disease to be applied and the decision of prescription. For example, the concentration of the compound of Formula I, which is an active ingredient contained in the composition of the present invention, can be determined in consideration of the purpose of treatment, the condition of the patient, the period of time required, and the like.

The pharmaceutical composition of the present invention may be formulated into a unit dose form by formulating it using a pharmaceutically acceptable carrier and / or excipient according to a method which can be easily carried out by a person having ordinary skill in the art to which the present invention belongs. Or by intrusion into a multi-dose container. Here, the formulations may be in the form of solutions, suspensions or emulsions in oils or aqueous media, or in the form of excipients, powders, granules, tablets or capsules, and may additionally contain dispersing or stabilizing agents.

According to another aspect of the present invention, the present invention includes a nucleotide sequence encoding the amino acid sequence of Arg88, Arg89, Arg134, Arg148, Cys167, Arg285, Arg308 or Cys310 of the phosphotransacetylase of the second sequence of the sequence listing A pharmaceutical composition for preventing or treating bacterial infectious diseases comprising, as an active ingredient, an RNAi (RNA interference) molecule that specifically binds to a nucleotide sequence of 10-100 bases or a binding agent that specifically binds to the amino acid sequence of the above position to provide.

According to some embodiments of the invention, the RNAi molecule used in the present invention includes antisense oligonucleotides, siRNA, shRNA and miRNA.

The term "antisense oligonucleotide " as used herein refers to DNA or RNA or a derivative thereof containing a nucleic acid sequence complementary to the sequence of a specific mRNA, and is capable of binding to a complementary sequence in mRNA and translating mRNA into a protein . ≪ / RTI > As used herein, the term "complementary" means that the antisense oligonucleotide under conditions of sufficient hybridization or annealing conditions, preferably under physiological conditions, is sufficient to selectively hybridize to the target (e.g., the PTA gene of the sequence listing sequence 1) May have one or more mismatch nucleotide sequences that are complementary and have a meaning that encompasses both 'substantially complementary' and 'perfectly complementary' , And more specifically, completely complementary. The length of the antisense oligonucleotide is 6 to 100 bases, more specifically 8 to 60 bases, most specifically 10 to 40 bases.

As used herein, the term "siRNA" means a nucleic acid molecule capable of mediating RNA interference or gene silencing (see WO 00/44895, WO 01/36646, WO 99/32619, WO 01/29058, WO 99/07409 and WO 00/44914). Since siRNA can inhibit the expression of a target gene, it is provided as an efficient gene knockdown method or as a gene therapy method. siRNAs were first discovered in plants, insects, fruit flies and parasites, but recently they have been applied to mammalian cell research to develop / use siRNAs (Degot S, et al. , 2002; Degot S, et al. , 2004; Ballut L , et al. , 2005).

SiRNA molecules that can be used in the present invention include those that have a sense strand (for example, a sequence corresponding to a PTA gene mRNA sequence) and an antisense strand (for example, a sequence complementary to a PTA gene mRNA sequence) And may have a double-stranded structure. In addition, siRNA molecules that may be used in the present invention may have a single stranded structure with self-complementary sense and antisense strands.

The siRNA is not limited to a complete pair of double-stranded RNA portions that are paired with each other, but is paired by a mismatch (the corresponding base is not complementary), a bulge (no base corresponding to one chain) May be included. Specifically, the total length is 10 to 100 bases, more specifically 15 to 80 bases, and even more specifically 20 to 70 bases.

As used herein, the term "shRNA (small hairpin RNA or short hairpin RNA)" refers to an RNA sequence that produces a robust hairpin turn, which can be used to silence gene expression through RNA interference. shRNA uses a vector for introducing cells and mainly uses a U6 promoter capable of expressing shRNA. These vectors are always transferred to daughter cells, allowing genetic silencing to be inherited. The shRNA hairpin structure is degraded into an intracellular machinery siRNA and bound to an RNA-induced silencing complex. The above-described complex binds to and degrades mRNA matched to the siRNA bound thereto. shRNAs are transcribed by RNA polymerase III, and production of shRNAs in mammalian cells may cause interferon responses as cells recognize shRNA as a viral attack and find defensive means. In addition, shRNAs can also be used in plants and other systems, and the U6 promoter is not necessary.

As used herein, the term "microRNA (miRNA)" refers to a material that binds to the 3'-UTR of mRNA (messenger RNA) as a single strand RNA molecule of 21-25 nucleotides and controls gene expression of eukaryotes (Bartel DP, et al. , Cell, 23: 116 (2): 281-297 (2004)). The production of miRNA is made into a stem-loop structure precursor miRNA (pre-miRNA) by Drosha (RNase III type enzyme), and it is transferred to the cytoplasm and cleaved by Dicer to make mature miRNA [Kim VN, et al. , Nat Rev Mol Cell Biol., 6 (5): 376-385 (2005)]. MiRNAs prepared as described above are involved in development, cell proliferation and death, lipid metabolism, tumor formation, etc. by controlling the expression of target proteins [Wienholds E, et al. , ≪ / RTI > Science, 309 (5732): 310-311 (2005); Nelson P, et al. , Trends Biochem Sci., 28: 534-540 (2003); Lee RC, et al. , Cell, 75: 843-854 (1993); And Esquela-Kerscher A, et al. , Nat Rev Cancer, 6: 259-269 (2006)].

According to some embodiments of the present invention, the binding agents that may be used in the present invention include, but are not limited to, peptides, aptamers, and antibodies.

As used herein, the term "binding agent" refers to a substance that binds to an active site of a target protein to inhibit the activity of the target protein. Specifically, the binding agent of the present invention is a binding site of a phosphotransacetylase For example, motifs containing the amino acid sequence of Arg88, Arg89, Arg134, Arg148, Cys167, Arg285, Arg308 and Cys310 of the phosphotransacetylase of the second sequence of the sequence listing) with high affinity to the phosphotransacetylase Activity can be suppressed.

As used herein, the term "peptide" refers to a linear molecule formed by peptide bonds and amino acid residues joined together. The peptides described above can be synthesized by chemical synthesis methods known in the art such as solid-phase synthesis techniques (Merrifield, J. Amer. Chem. Soc. 85: 2149-54 (1963); Stewart, et al. , Solid Phase Peptide Synthesis , 2nd ed., Pierce Chem. Co .: Rockford, 111 (1984)) or liquid phase synthesis technology (US Patent No. 5,516,891).

As used herein, the term "aptamer" is an oligonucleotide or peptide molecule capable of specifically binding to a target protein, and the amino acid sequence of the target protein to be referred to for its production can be found in GenBank. For example, the nucleotide sequence and amino acid sequence of the PTA protein of the present invention are disclosed in GenBank Accession No. BA000018.3 (SEQ ID No. 1) and NP-373799 (SEQ ID No. 2), respectively, You can design a tamer. The general contents of the Aptamer are described in Bock LC et al., Nature 355 (6360): 5646 (1992); Hoppe-Seyler F, Butz K "Peptide aptamers: powerful new tools for molecular medicine". J Mol Med. 78 (8): 42630 (2000); Cohen BA, Colas P, Brent R. "An artificial cell-cycle inhibitor isolated from a combinatorial library". Proc Natl Acad Sci USA. 95 (24): 142727 (1998).

Antibodies that can be used as the binding agent of the present invention are polyclonal or monoclonal antibodies, specifically monoclonal antibodies. Antibodies can be produced using methods commonly practiced in the art, such as the fusion method (Kohler and Milstein, European Journal of Immunology , 6: 511-519 (1976)), the recombinant DNA method (US Patent No. 4,816,56) Or phage antibody library methods (Clackson et al., Nature , 352: 624-628 (1991) and Marks et al . , J. Mol. Biol. , 222: 58, 1-597 (1991)). General procedures for antibody preparation are described in Harlow, E. and Lane, D., Using Antibodies: A Laboratory Manual , Cold Spring Harbor Press, New York, 1999; Zola, H., Monoclonal Antibodies: A Manual of Techniques , CRC Press, Inc., Boca Raton, Florida, 1984; And Coligan, CURRENT PROTOCOLS IN IMMUNOLOGY , Wiley / Greene, NY, 1991, the disclosures of which are incorporated herein by reference. For example, the preparation of hybridoma cells producing monoclonal antibodies is accomplished by fusing an immortalized cell line with an antibody-producing lymphocyte, and the techniques necessary for this process are well known and readily practicable by those skilled in the art. Polyclonal antibodies can be readily obtained by injecting a protein antigen into a suitable animal, collecting the antiserum from the animal, and isolating the antibody from the antiserum using known affinity techniques.

The features and advantages of the present invention are summarized as follows:

(a) The present invention relates to a method for identifying a ligand for phosphotransacetylase of Staphylococcus aureus using a virtual screening method and a pharmaceutical composition for preventing or treating a bacterial infectious disease containing the ligand as an active ingredient ≪ / RTI >

(b) The in silico virtual screening method of the present invention analyzes the tertiary structure of the target protein and analyzes the interaction between the target protein and the reference ligand by a molecular dynamics simulating method to predict the active site of the target protein by homology modeling do.

(c) Based on this, drug-like molecules having increased binding energy and pharmacokinetic properties to the target protein are identified through docking analysis between the target protein and the candidate material ligand library.

(d) The present invention identifies the compound represented by the formula (I) as a Staphylococcus aureus-derived phosphotransacetylase inhibitor through the above-described method.

(e) Therefore, the method of the present invention can be applied to the field of drug development research using biotechnology by predicting a large amount of physiologically active candidate substances more quickly and efficiently based on protein body biology, bioinformatics and combinatorial chemistry This can drastically reduce the development cost of new drugs.

Figure 1 is a diagram showing the 3D structure of the predicted phosphotransacetylase. a, present secondary structure; b, surface representation; And c, overlaps the predicted structure and template (PDB access code, ITD9).
Figure 2 shows the sequence alignment between Staphylococcus aureus N315 strain and chain A of Bacillus subtilis (PDB accession code, itd9).
Figure 3 shows the Ramachandran plot of the phosphotransacetylase of Staphylococcus aureus obtained by PROCHECK. 92.3% of the residues are found in the most favorable region, 7.6% of the residues are found in the additionally allowed region, and 0.3% of the residues are found in the generally accepted regions.
Figure 4 is a view showing the position of the ligand in cavity 1 of the PTA: a, ZINC08442078; b, ZINC08442087; c, ZINC08442182; d, ZINC08442184; And e, ZINC08442200.
5 is a view showing residues (a) included in the cavity 1 formation and residues (b) (i.e., Leu299, Gly298, and Lys143) contained in the hydrogen bond.
Figure 6 shows the results of the drug generating group mapping of the ligands of the present invention, ZINC08442078, ZINC08442087, ZINC08442182, ZINC08442184 and ZINC08442200: a, alignment of selected ligands; b, the shared property of the drug generating module; And c, a merged property of the drug-generating-end.

Hereinafter, the present invention will be described in more detail with reference to Examples. It is to be understood by those skilled in the art that these embodiments are only for describing the present invention in more detail and that the scope of the present invention is not limited by these embodiments in accordance with the gist of the present invention .

Example

Materials and Experiments

Sequence search, physicochemical properties and secondary structure prediction

The PTA sequence of Staphylococcus aureus was retrieved from a protein database available on the NCBI server. Theoretical isoelectric point, p I , molecular weight, total number of positive and negative residues, extinction coefficient [28], instability index [29], AI (aliphatic index) Physico-chemical properties of PTA such as GRAVY (grand average hydropathy) [31] were evaluated using Expasy's ProtParam server [32]. SOPMA [33] was used to calculate the secondary structural properties of the phosphotransacetylase sequence used in this study.

Evaluation of intracellular location and functionality

Subcellular localization analysis of the PTA protein was performed by a proteomic analysis-specific intracellular location server v2.5 (PA-SUB) [34]. For prediction of 'SS' binding from primary structure (protein sequence data), CYS_REC (http://sunI.softberry.com/berry.phtml?topic) was used. The domains and families of proteins have been confirmed by the prosheet database [35].

Homology Modeling

The 328-amino acid-containing phosphotransacetylase sequence of Staphylococcus aureus N315 strain was obtained from the NCBI database and was obtained from gi | 15926266 | (BA000018.3; SEQ ID NO: 1) and protein accession number NP_373799 2 sequence). The FASTA file for the sequence was submitted to EsyPresd3D, a server for protein 3D structure prediction based on homology modeling [36]. An alignment-based prediction model was obtained using the server. A useful model and secondary structure was identified by STRIDE [37]. Energy minimization models and root mean square deviation (RMSD) were performed using YASARA and SuperPose servers, respectively [38, 39]. The RMSD analysis of the developed model was evaluated by the mean deviation for the template using SuperPose. The SuperPose web server quickly and reliably computes both pairwise and multi-protein structure superposition using the modified quaternion eigenvalue approaches [39]. The modeled structure is submitted to UCLA and PROCHEK servers to ensure the quality and stereochemistry of the fabric [19, 40]. Since the information on the PTA activation site used in this study is not found in the CSA database [41], the PTA activation site was predicted and presented for the first time in this study.

Preparation of Ligand Libraries

Acetyl phosphate (DB 02897), known as an inhibitor to PTA, was obtained from the drug bank database and initial values of the molecules were used for library preparation [5]. The ligand library was constructed from a ZINC database using five parameters according to Ripinsky's rule (1.5 ≤ log ≤ 5, 2 ≤ H donors ≤ 5, 5 ≤ H acceptors ≤ 10, 140.03 ≤ molecular weight ≤ 500) [42 , 51, 52].

Docking Analysis

Virtual docking studies between the prepared library and our predicted 3D model of phosphotransacetylase were performed using the Molegro Virtual Docker version 1.1.1 software (Molegro ApS, Aarhus, Denmark, http: MolDock [5, 19, 43], which is a molecular docking algorithm using a molecular docking technique (www.molegro.com). The docking process consisted of three interrelated components: (a) identification of the binding site; (b) a docking algorithm; And (c) a scoring function or energy calculation software [42, 54]. For docking, a MolDock scoring function based on a piecewise linear potential and applied to the highest ranked position of the re-ranking process was used to increase the docking accuracy. The ligands modeled for Arguslab and protein structural complexes were inserted into charge and flexible torsions using docking program-assigned bonds, hybridisation and positive hydrogen molecules if omitted, and Molegro Virtual Docker software. The molecular structure of the inserted ligands was manually checked before docking and correcting in the event of failure. Our structural analysis only includes proteins, which do not include all of the elements or ligands. Water molecules in protein structures have also been excluded from docking experiments [54-56]. The default settings of the software variables were used in this study, and the ligands were provided from libraries of the invention made in mol2 format. The binding sites were restricted to 15 x 15 x 15 Å ³ cubes located at the center of the observed binding of the ligand in the protein complex. Due to the stochastic nature of the ligand-protein docking search algorithm, 10 runs were performed to select 10 docking positions (pose) per ligand. The interaction energy between position and protein with the highest ranking MolDock score was compared [54-56].

Pharmacophore production and Tox studies

Alignment and virtual pharmacokinetic model generation was performed using Ligandcout 2.03 [44]. The ligand scout program provides definitions for a wide range of chemical properties including hydrogen bonding vectors, charge groups, directional side-by-side and directional positive ionic interactions. Advanced sorting algorithms allow overlapping of pharmacogenetic agents and molecules, allowing common modes of attachment to be detected and shared chemical properties to be inserted, and toxic studies are commercially available for computer-based assessment methods in chemical toxicity assays It is implemented using a possible software called toxtree (developed by Ideaconsult Ltd; Sofia, Bulgaria) [45].

Experimental results and further discussion

The selection of phosphotransacetylase (EC: 2.3.1.8) for this study is the result of previous studies by the present inventors. In the previous study, the present inventors investigated the key enzymes to determine the metabolic pathways of Staphylococcus aureus And analyzed extensively. The enzyme was found to be essential. The enzyme did not have similarity with human proteins in the BLASTp-based assay. The 328-amino acid-containing enzyme is the product of the eutD gene. Phosphotransacetylase, one of the enzymes necessary for the survival of microorganisms, may be a potential drug target for the design and evaluation of a novel class of antimicrobial agents [5, 18, 19].

Physiochemical and functional properties of phosphotransacetylase

The variables computed using Expasy's ProtParam means are shown in Table 1.

Physiochemical and functional characteristics of phosphotransacetylase. analysis Variables and properties Physicochemical properties MW
34,951.7 p I
4.72 -R
45 + R
32 EC
10,555 II
34.61 AI
98.17 GRAV
Y 0.086 The calculated secondary structure (SOPMA) Alpha Helix, 46.95% 3 ₁₀ Helix, 0.00% Pi Helix, 0.00% Beta bridge, 0.00% Extended strand, 14.02% Betaturn, 7.93% Bend region, 0.00% Arbitrary coil, 31.10% Ambiguous status, 0.00% CYS_REC
PA-SUB and SOSUI Analysis CYS_REC: C167 and C310
PA-SUB: cytoplasm
SOSUI: soluble protein

The calculated p I (isoelectric point) value of phosphotransacetylase was 4.72 (ie, p I <7), which means that the protein is acidic in nature. The extinction coefficient of the protein was needed to establish a quantitative study of the protein-protein, and the protein-ligand interaction in the solution was found to be 10,555 M ^-1 cm ^-1 . The instability index of the enzyme was calculated to be 34.61, which means that the protein was stable because it was below 40 [29]. The aliphatic index (AI), defined as the relative capacity of proteins occupied by the aliphatic side chains (A, V, I and L), was considered as a positive factor for increasing the thermal stability of spherical proteins and the calculated value was 98.17. The GRAVY value of a peptide or protein is calculated as the sum of the hydropathies of all amino acids divided by the number of residues in the sequence. The GRAVY index of phosphotransacetylase was 0.086 (Table 1). The low range values indicate the possibility of better interaction with water. The enzyme was identified as a soluble cytoplasmic protein by analysis by SOSUI. It is well known that disulphide bridges play a key role in the thermal stability of the protein: Table 1 shows the location of the apparent disulfide bond deduced by CYS_REC. There are no other posi- tive positions beyond those found through the analysis. The inventors have not found any other patterns, motifs, signatures or fingerprints when submitting the sequence to the Prosite server. The secondary structure of the protein was predicted by alignment and self-optimized prediction method (SOPMA), which correctly predicts 69.5% of the amino acids for the formal description of the secondary structure prediction [33]. The characteristics of the secondary structure, as predicted by SOPMA, are shown in Table 1.

Homology modeling and docking

The 3D structure of the phosphotransacetylase enzyme of Staphylococcus aureus was modeled by ESyPred3D, an automated homology modeling server (Fig. 1). Alignment was achieved by combining, weighting, and screening the results of several multiple alignment programs, and the final structure was established using MODELLER, a modeling package [36]. The template used in the investigation of the present invention was a 1TD9 chain 'A' derived from bacillus subtilis which was used in the 3D structural model: the resolution analyzed by X-ray diffraction was 2.75 A [25] . The server results show that the BLASTp results through the PDB database show 99% query coverage while the template shares 64.4% identity with our search sequence (Figure 2) using the ALIGN program. PTA has high sequence similarity with reported bacterial and high bacterial PTA domains (BLAST search results; results not shown).

Structural features of the phosphotransacetylase obtained from Methanosarcina thermophila are that cys159 is necessary for stability and possible catalysis, cys312 which functions as an active site but is not essential for catalysis, and Residues of arg87, arg133 and arg310 were proposed to interact with CoA [27, 46]. The phosphotransacetylase of Staphylococcus aureus has residues cys167 and cys310, and arg88, arg89, arg134, arg148, arg285 and arg308 residues. Of these, arg88, arg134 and arg308, and residues cys167 and cys310 are expected to be included in the active site. In order to validate the hypotheses for the predicted active location in the PTA, a CSA server was used but unfortunately no records were found for EC: 2.3.1.8 [41]. Alignment studies show that residues cys167 and cys310 and residues arg88, arg 89, arg134, arg148, arg285 and arg308 can be included in the active location information. To analyze the pattern, the secondary structure of PTA was fabricated by STRIDE [37]. As a result of applying the modeled structure to force field energy minimization, the final value was -176,747.6 kJ / mol and the score was -0.09 as compared with the initial values of -56, 133.5 kJ / mol and -2.97. The Cα RMSD for the model crystal structure and the backbone RMSD deviation values for the template crystal structure were 0.64 Å and 0.65 Å, respectively. A study on the modeling structure of PTA as a femula plate confirmed whether the predicted structure is suitable for docking [47]. Therefore, other variables for evaluating the predicted structure quality have been studied. The Ramachandran plot for the predicted model showed that 92.3% of the residues were found in the most favorable region and 7.6% were found in the allowed region (FIG. 3). The overall G-factor was calculated based on the main chain parameter [48], where the modeled structure was interpreted as suitable for virtual screening.

Docking of the ligands of the prepared library with the modeled structure of the enzyme was performed using the MolDock algorithm [43]. In addition, recent advances in molecular characterization and bioinformatics have made it possible to 'dock' small molecules (ie, ligands) to proteins and 'score' potential binding. Virtual screening uses computer methodologies to identify biologically active molecules for a specific protein target [49]. In this study, the inventors used a methodology that included methods of similarity searching and molecular docking for verified ligands using crystallographic data of targets. Nevertheless, only very limited information on PTA and individual inhibitors of Staphylococcus aureus is useful. The novelty of this study is based on the use of virtual screening and selection of molecules according to pharmacological properties. The virtual screening method is based on a modeled structure that defines the necessary conditions for a model suitable for docking. Therefore, methods such as docking and virtual screening are widely used for drug development. Thus, the choice of an effective docking protocol based on the similarity of all re-docked positions to the crystallographically-determined bound orientations was a major concern during the course of the experiment. A symmetry-matched RMSD was calculated for all positions. The virtual screening method is one of the fastest methods implemented in the drug discovery process and is used to filter undesired molecules that can not show affinity for the active site of the protein. In this study, we applied the Ripin 's rules, one of the categories for filtering molecules that can not exhibit drug - like properties. Docking was performed using a library of 1,001 molecules prepared under the above conditions. Based on the docking score, five ligands were selected using two search algorithms through conjugation with two scoring functions per returned poses. Cavity ¹ , having a volume of 48.128 Å ³ and a surface area of 148.48 Å ² , was found to be involved in binding with most ligands. To evaluate the interaction energy between ligand and protein, a docking experiment was conducted under the above conditions. The MolDock score and hydrogen bond energy of each ligand were key criteria for selecting molecules. Properties of selected compounds such as H-bond donor, H-bond acceptor, molecular weight, molecular formula, χlog p , compound ID, zinc ID, ligand name,

Physicochemical characteristics of five selected ligands from docking studies. characteristic Ligand molecule library
Ligand name
rescue

ZINC8442078

ZINC8442200

ZINC8442087

ZINC8442184

ZINC8442182

Compound ID 9542913 9542940 9542914 3660145 4612017 Molecular Weight 498.64408 448.5854 498.64408 478.58978 464.5632 Molecular formula C ₂₂ H ₂₂ N ₆ O ₂ S ₃ C ₁₈ H ₂₀ N ₆ O ₂ S ₃ C ₂₂ H ₂₂ N ₆ O ₂ S ₃ C ₂₃ H ₂₂ N ₆ O ₂ S ₂ C ₂₂ H ₂₀ N ₆ O ₂ S ₂ LogP3-AA 3.9 3 3.8 3.4 3 H-bond donor 2 2 2 2 2 H-coupled receptor 5 5 5 5 5 IUPAC name Amino] -2-oxoethyl] sulfanyl-1,2, &lt; / RTI &gt; Triazol-3-yl] ethyl] thiophene-2-cappyrimide Amino-2-oxoethyl] sulfanyl-4-prop-2-enyl-1,2 , 4-triazol-3-yl] ethyl] thiophene-2-capphanamide Amino] ethyl] sulfanyl-1,2,4-triazole, which is a salt of N- [2- (4-ethyl- 3-yl] ethyl] thiophene-2-capphanamide 2-yl) amino] -2-oxoethyl] sulfanyl-1,2,4-triazol- Triazol-3-yl] -N-phenylacetamide Amino] ethyl] sulfanyl-1,2,4-triazol-3-yl] - (2-methyl- N-phenylacetamide Docking score -188.8 -181.242 -180.403 -179.924 -175.551 Middle class 33 29 33 33 32 H-bonding energy -11.9416 -9.89572 -10.9408 -10.2336 -9.55797 Rotatable
The
Number of distortions 9 10 10 8 8 Re-ranking score -130.24 -112.207 -98.3151 -128.719 -124.596

We chose the ligands based on the docking score. The top five selected ligand molecules and their conjugation sites for the cavity are shown in FIG. The residues involved in the formation of the vacancies are also shown in Figure 5a and the residues near the cavities interacting with the ligand molecules by hydrogen bonding are also shown in Figure 5b.

ADME / Tox tree prediction and pharmacodynamic model generation

After docking simulation based on the MolDock score, the top 25 compounds from the initial set of 1,001 molecules were selected. The above-mentioned molecules have been submitted for filtering pharmacological properties, in which a full form of a variety of user-friendly open applications, toxostri, has been used to evaluate the detrimental properties of toxicity. User-restricted molecular structures are also provided to be introduced using SMILES or an embedded 2D structural schematic editor [19]. Other characteristics such as ADME (absorption, distribution, metabolism and excretion) were also predicted. Five molecules were finally selected based on the properties described above (Table 2).

Due to advances in technology in the field of bioinformatics and combinatorial chemistry, the size of available compound libraries as well as the number of known targets are exploding. The five molecules of a non-in silico (In silico) to a target from the library, virtual screening method using the carcinogenic (non-carcinogenic) and dropped to determine that the drug action occurred just before - that the superior method for screening compounds Proved [19, 50]. The pharmacogenetics is defined as the ensemble of universal chemical features that characterize the specific mode of behavior of the ligand to the active site of macromolecules in 3D space. For example, after obtaining the top five ligands (drug-like molecules) from the virtual screening of the ligand library, the chemical characteristics of the molecules analyzed may include hydrogen bonds, charge interactions, hydrophobic areas, . We used Zinc08442078 as the reference molecule in Ligand Scout 2.03 software (a software tool that can generate fast and clear 3D pharmacokinetic development from structural data of macromolecule / ligand complexes in a fully automated and easy manner) Structural alignment of one molecule was performed to generate the theoretical pharmacokinetic model for phosphotransacetylase. The shared features of the pharmacogenetic model represent six features of the pharmacogenetical end: (a) a yellow sphere characterized by a hydrophobic character; (b) one hydrogen bond donor indicated by a green vector; (c) one directional ring; And (d) three hydrogen bond acceptors. On the other hand, the merged feature of the pharmacogenetic model represents eight hydrophobic features, four hydrogen bond donors, nine aromatic rings and eleven hydrogen bond acceptors. The alignment of the five ligands through a shared pharmacogenetic model, as well as the incorporated pharmacogenetics model, is shown in FIG. Taken together, the present inventors have selected PTA as a potential target, and a review of the enzyme in Staphylococcus aureus has been done through several computer analysis methods using standard algorithms. According to this study, the present inventors attempted to identify the potential interactions between the enzyme and the ligand. This study may be helpful in the design and selection of antimicrobial agents and their adjuvants for proper activity.

There are several successful examples of developing inhibitors using virtual screening approaches. The first successful docking experiment test results were performed using the DOCK program to identify the various subset of molecules capable of binding to HIV-1 protease [54-57]. One of the ligands identified from VS was bromoperidol, which was an analog of haloperidol and was identified as an HIV protease inhibitor. Subsequently, thioketal derivatives with 15 μM inhibition were produced by analog synthesis. Further, Merck's HIV protease inhibitor, Indinavir sulfate, was developed in part using docking [58].

Despite numerous warnings, virtual screening methods are a very important tool for exploring biologically relevant chemical fields. Thus, larger virtual screening studies are concentrated on libraries of small molecules (e.g., libraries of millions of compounds). In a previous study of the present inventors, we have analyzed metabolic pathways for the purpose of discovering key proteins that can be targeted for drug design. A comparative study of the metabolites of Staphylococcus aureus has shown that the core enzymes can be targeted for drug design [5] and eight essential proteins have been identified from Staphylococcus aureus. In addition to the potential targets described above, PTA has been selected for this study because it can be found as a non-homologous protein in comparison to human proteins and thus a safer target. Molecules reported as inhibitors to these enzymes can be further explored for drug design. In this work, the present inventors have described a comprehensive and effective molecular modeling and docking methodology capable of investigating the binding of active sites and inhibitors of enzymes. It is still necessary to investigate more in vivo test results for complete evaluation as a drug. Using this selectable approach to designing drugs, the investigator can minimize the try and hit methodology. Thus, the method of the present invention can protect the time, cost and life of test animals. Further, studies of the present invention show that some molecules with potential drug-like properties can be investigated as antimicrobial agents against Staphylococcus aureus.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is obvious that the same is by way of illustration and example only and is not to be construed as limiting the scope of the invention. Accordingly, the actual scope of the present invention will be defined by the appended claims and their equivalents.

references

1. Carleton, H. A., Diep, B. A., Charlebois, E. D., Sensabaugh, G. F., & Perdreau-Remington, F. (2004). Community-adapted methicillin-resistant Staphylococcus aureus (MRSA): population dynamics of an expanding community reservoir of MRSA. Journal of Infectious Diseases, 190, 1730-1738.

2. Diekema, D. J., Pfaller, M. A., & Schmitz, F. J. (2001). Survey of infections due to Staphylococcus species: frequency of occurrence and antimicrobial susceptibility of isolates collected in the SENTRY Antimicrobial Surveillance Program. Clinical Infectious Diseases, 32, S114-S132.

3. Freeman-cook, L., & Freeman-cook, K. (2006). Staphylococcus aureus infections (deadly diseases and epidemics). Philadelphia, Chelsea House Publications.

4. King, M. D., Humphrey, B. J., Wang, Y. F., Kourbatova, E. V., Ray, S. M., & Blumberg, H. M. (2006). Emergence of community-acquired methicillin-resistant Staphylococcus aureus USA 300 clone as the predominant cause of skin and soft-tissue infections. Annals of Internal Medicine, 144, 309-317.

5. Morya, V. K., Dewaker, V., Mecarty, S. D., & Singh, R. (2010). In silico analysis of metabolic pathways for identification of putative drug targets for Staphylococcus aureus. Journal of Computer Science & Systems Biology, 3 (3), 062-069.

6. Ross, R. A., & Onderdonk, A. B. (2000). Production of toxic shock syndrome toxin 1 by Staphylococcus aureus requires both oxygen and carbon dioxide. Infection and Immunity, 68 (9), 5205-5209.

7. Yarwood, J. M., & Schlievert, P. M. (2003). Quorum sensing in Staphylococcus infections. The Journal of Clinical Investigation, 112 (11), 1620-1625.

8. Bien, J., Sokolova, O., & Bozko, P. (2011). Characterization of virulence factors of Staphylococcus aureus: a novel function of known virulence factors that are implicated in activation of airway epithelial proinflammatory response. Journal of Pathogens, 2011 (601905), 13. doi: 10.4061 / 2011/601905.

9. Deurenberg, R. H., & Stobberingh, E. E. (2008). The evolution of Staphylococcus aureus. Infection, Genetics and Evolution, 8 (6), 747-763.

10. Ellis, M. W., Griffith, M. E., Jorgensen, J. H., Hospenthal, D. R., Mende, K., & Patterson, J. E. (2009). Presence and molecular epidemiology of virulence factors in methicillin-resistant Staphylococcus aureus strains colonizing and infecting soldiers. Journal of Clinical Microbiology, 47 (4), 940-945.

11. Ferry, T., Perpoint, T., Vandenesch, F., & Etienne, J. (2005). Virulence determinants in Staphylococcus aureus and their involvement in clinical syndromes. Current Infectious Disease Reports, 7, 420-428.

12. Hogevik, H., Sderquist, B., Tung, H. S., Olaison, L., Westberg, A., Rydn, C., Tarkowski, A., and Anderson, R. (1998). Virulence factors of Staphylococcus aureus strains causing infective endocarditis include strains of skin infections. APMIS, 106 (9), 901-908.

13. Spanu, V., Spanu, C., Virdis, S., Cossu, F., Scarano, C., & De Santis, E. P. (2012). Virulence factors and genetic variability of Staphylococcus aureus strains isolated from raw sheeps milk cheese. International Journal of Food Microbiology, 153 (12), 53-57.

14. Lowy, F. D. (1998). Staphylococcus aureusinfections. The New England Journal of Medicine, 339, 520-532.

15. Sarkar, M., Maganti, L., Ghoshal, N., & Dutta, C. (2012). In silico quest for putative drug targets in Helicobacter pylori HPAG1: molecular modeling of candidate enzymes from lipopolysaccharide biosynthesis pathway. Journal of Molecular Modeling, 18 (5), 1855-1866.

16. Perumal, D., Lim, C. S., Sakharkar, K. R., & Sakharkar, M. K. (2007). Differential genome analyzes of metabolic enzymes in Pseudomonas aeruginosa for drug target identification. Silico Biology, 7 (45), 453-465.

17. Butt, A. M., Tahir, S., Nasrullah, I., Idrees, M., Lu, J., & Tong, Y. (2012). Mycoplasma genitalium: a comparative genomics study of metabolic pathways for the identification of drug and vaccine targets. Infection, Genetics and Evolution, 12 (1), 53-62.

18. Morya, V. K., Kumari, S., & Kim, E. (2011). Imperative pathway analysis to identify potential drug targets for Aspergillus infection. International Journal of Recent Trends in Computing, 2 (1), 178-182.

19. Morya, V. K., Kumari, S., & Kim, E. K. (2012). Virtual screening and evaluation of ketol-acid reductoisomerase (KARI) as a putative drug target for aspergillosis. Clinical Proteomics, 9 (1), 1.

20. Dutta, A., Singh, S. K., Ghosh, P., Mukherjee, R., Mitter, S., & Bandyopadhyay, D. (2006). In silico identification of potential therapeutic targets in the human pathogen Helicobacter pylori. Silico Biology, 6 (1-2), 43-47.

21. Singh, S., Joshi, P., & Chopade, B. A. (2011). Pathway analysis of Acinetobacter baylyi: a combined bioinformatic and genomics approach. Chemical Biology & Drug Design, 78 (5), 893905. doi: 10.1111 / j.1747-0285.2011.01191.x.

22. Bologna, F. P., Campos-Bermudez, V. A., Saavedra, D. D., Andreo, C. S., & Drincovich, M. F. (2010). Characterization of Escherichia coli EutD: a phosphotransacetylase of the ethanolamine operon. Journal of Microbiology, 48 (5), 629-636.

23. Campos-Bermudez, V. A., Bologna, F. P., Andreo, C. S., & Drincovich, M. F. (2010). Functional dissection of Escherichia coli phosphotransacetylase structural domains and analysis of key compounds involved in activity regulation. FEBS Journal, 277 (8), 1957-1966.

24. Dimou, M., Venieraki, A., Liakopoulos, G., & Katinakis, P. (2011). Cloning, characterization and transcriptional analysis of two phosphate acetyltransferase isoforms from Azotobacter vinelandii. Molecular Biology Reports, 38 (6), 3653-3663.

25. Kim, S., & Kim, S. (2005). A study on the effect of the chemical composition of water on the microbial growth of rice. Crystal structures of a phosphotransacetylase from Bacillus subtilis and its complexes with acetyl phosphate. Journal of Structural and Functional Genomics, 6 (4), 269-279.

26. Iyer, P. P., Lawrence, S. H., Luther, K. B., Rajashankar, K. R., Yennawar, H. P., Ferry, J. G., & Schindelin, H. (2004). Crystal structure of phosphotransacetylase from the methanogenic archaeon Methanosarcina thermophile. Structure, 12 (4), 559-567.

27. Rasche, M. E., Smith, K. S., & Ferry, G. J. (1997). Identification of cysteine and arginine residues essential for the phosphotransacetylase from Methanosarcina thermophile. Journal of Bacteriology, 179 (24), 77127717.

28. Gill, S. C., & Von Hippel, P. H. (1989). Extinction coefficient. Analytical Biochemistry, 182, 319-328.

29. Guruprasad, K., Reddy, B. V. P., & Pandit, M.W. (1990). Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting the stability of a protein from its primary sequence. Protection Engineering, 4, 155-164.

30. Ikai, A. J. (1980). Thermo stability and aliphatic index of globular proteins. Journal of Biochemistry, 88, 1895-1898.

31. Kyte, J., & Doolottle, R. F. (1982). A simple method for displaying the hydropathic character of a protein. Journal of Molecular Biology, 157,105-132.

32. Gasteiger, E. (2005). Protein identification and analysis tools on the ExPASy Server. In: J. M. Walker (Ed.), The proteomics protocols handbook (pp. 571-607). Totowa: Humana.

33. Geourjon, C., & Delage, G. (1995). SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Computer Applications in the Biosciences, 11, 681-684.

34. Lu, Z., Szafron, D., Greiner, R., Lu, P., Wishart, D. S., Poulin, B., Anvik, J., Macdonell, C., & Eisner, R. (2004). Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics, 20 (4), 547-556.

35. Falquet, L., Pagni, M., Bucher, P., Hulo, N., Sigrist, C. J., Hofmann, K., & Bairoch, A. (2002). The PROSITE database, its status in 2002. Nucleic Acids Research, 30 (1), 235-238.

36. Lambert, C., Lonard, N., De Bolle, X., & Depiereux, E. (2002). ESyPred3D: prediction of proteins 3D structures. Bioinformatics, 18 (9), 1250-1256.

37. Heinig, M., & Frishman, D. (2004). STRIDE: a web server for secondary structure assignment from known atomic coordinates of protein. Nucleic Acids Research, 32, W500-W502.

J., Tyka, M., Baker, D., & Karplus, K. (2009), &quot; Krieger, E., Joo, K., Lee, J., Lee, J., Raman, S., Thompson, . Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Proteins, 77 (Suppl 9), 114-122.

39. Maiti, R., Gary, H., Domselaar, V., Zhang, H., & Wishart, D. S. (2004). SuperPose: a simple server for sophisticated structural superposition. Nucleic Acids Research, 32, W590-W594.

40. Laskoswki, R. A., MacArthur, M. W., Moss, D. S., & Thorton, J. M. (1993). PROCHECK: a program to check the stereochemical quality of protein structures. Journal of Applied Crystallography, 26, 283-291.

41. Porter, C. T., Bartlett, G. J., & Thornton, J. M. (2004). The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Research, 32, D129-D133.

42. Irwin, J. J., & Shoichet, B. K. (2005). ZINCa is a free database of commercially available compounds for virtual screening. Journal of Chemical Information and Modeling, 45 (1), 177-182.

43. Thomsen, R., & Christensen, M. H. (2006). MolDock: a new technique for high-accuracy molecular docking. Journal of Medicinal Chemistry, 49 (11), 3315-3321.

44. Wolber, G., & Langer, T. (2005). LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. Journal of Chemical Information and Modeling, 45 (1), 160-169.

45. Wolber, G., Seidel, T., Bendix, F., & Langer, T. (2008). Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discovery Today, 13 (12), 23-29.

46. Lawrence, S. H., Luther, K. B., Schindelin, H., & Ferry, J. G. (2006). Structural and functional studies suggest a catalytic mechanism for the phosphotransacetylase from Methanosarcina thermophila. Journal of Bacteriology, 188 (3), 1143-1154.

47. Lee, M. R., Tsai, J., Baker, D., & Kollman, P. A. (2001). Molecular dynamics in the endgame of protein structure prediction. Journal of Molecular Biology, 313, 417-430.

48. Engh, R. A., & Huber, R. (1991). Accurate bond and angle parameters for X-ray protein structure refinement. Acta Crystallographica, A47, 392-400.

49. Vianna, C. P., & de Azevedo, W. F., Jr. (2012). Identification of new potential Mycobacterium tuberculosis shikimate kinase inhibitors through molecular docking simulations. Journal of Molecular Modeling, 18 (2), 755-764.

50. Wermuth, C., & Langer, T. (1998). In H. Kubinyi (Ed.), Pharmacophore Identification (pp. 117136). Leiden: Escom.

51. Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23, 3-25.

52. Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 46 (13), 3-26.

53. Morya, V. K., Yadav, S., Kim, E. K., & Yadav, D. (2012). In silico characterization of alkaline proteases from different species of Aspergillus. Applied Biochemistry and Biotechnology, 166 (1), 243-257.

54. Moraes, F. P., & de Azevedo, W. F., Jr. (2012). Targeted imidazoline site on monoamine oxidase B through molecular docking simulations. Journal of Molecular Modeling, 18, 38773886. doi: 10.1007 / s00894-012-1390-7.

55. Heberl, G., & De Azevedo, W. F., Jr. (2011). Bio-inspired algorithms applied to molecular docking simulations. Current Medicinal Chemistry, 18 (9), 1339-1352.

56. Dias, R., & de Azevedo, W. F., Jr. (2008). Molecular docking algorithms. Current Drug Targets, 9 (12), 1040-1047.

57. Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., & Ferrin, T. E. (1982). A geometric approach to macromoleculeligand interactions. Journal of Molecular Biology, 161 (2), 269-288.

58. DesJarlais, RL, Seibel, GL, Kuntz, ID, Furth, PS, Alvarez, JC, Ortiz de Montellano, PR, De Camp, DL, Bab, LM, & Craik, CS (1990). Structure-based design of nonpeptide inhibitors specific for the human immunodeficiency virus 1 protease. Proceedings of the National Academy of Sciences of the United States of America, 87 (17), 6644-6648.

<110> INHA Industry Partnership Institute <120> Pharmaceutical Compositions for Preventing or Treating a          Microorganism Infection Disease Comprising a Chemical Compound          with an Inhibitory Activity Against Phosphotransacetylase <130> PN130292 <160> 2 <170> Kopatentin 2.0 <210> 1 <211> 984 <212> DNA <213> Staphylococcus aureus <400> 1 atggctgatt tattaaatgt attaaaagac aaactttctg gtaaaaacgt taaaatcgta 60 ttacctgaag gagaggacga acgtgttcta acagctgcaa cacaattaca agcaacagat 120 tatgttacac caatcgtgtt aggtgatgag actaaggttc aatctttagc gcaaaaactt 180 gatcttgata tttctaatat tgaattaatt aatcctgcga caagtgaatt gaaagctgaa 240 ttagttcaat catttgttga acgacgtaaa ggtaaagcga ctgaagaaca agcacaagaa 300 ttattaaaca atgtgaacta cttcggtaca atgcttgttt atgctggtaa agcagatggt 360 ttagttagtg gtgcagcaca ttcaacagga gacactgtgc gtccagcttt acaaatcatc 420 aaaacgaaac caggtgtatc aagaacatca ggtatcttct ttatgattaa aggtgatgaa 480 caatacatct ttggtgattg tgcaatcaat ccagaacttg attcacaagg acttgcagaa 540 attgcagtag aaagtgcaaa atcagcatta agctttggca tggatccaaa agttgcaatg 600 ttaagctttt caacaaaagg gtctgctaaa tcagacgacg tgacaaaagt tcaagaagct 660 gtcaaattag cacaacaaaa agctgaagaa gaaaaattag aagcaatcat tgatggcgaa 720 ttccaatttg atgctgcgat tgtaccaggt gttgctgaga aaaaagcgcc aggtgctaaa 780 ttacaaggtg atgcaaatgt ctttgtattc ccaagtttag aagctggtaa tattggttac 840 aaaattgcac aacgtttagg tggatatgat gcagttggtc cagtattaca aggtttaaat 900 tctccagtaa atgacttatc acgtggctgc tcaattgaag atgtatacaa tcttttcaatt 960 attacagcag cgcaagcctt acaa 984 <210> 2 <211> 328 <212> PRT <213> Staphylococcus aureus <400> 2 Met Ala Asp Leu Leu Asn Val Leu Lys Asp Lys Leu Ser Gly Lys Asn   1 5 10 15 Val Lys Ile Val Leu Pro Glu Gly Glu Asp Glu Arg Val Leu Thr Ala              20 25 30 Ala Thr Gln Leu Gln Ala Thr Asp Tyr Val Thr Pro Ile Val Leu Gly          35 40 45 Asp Glu Thr Lys Val Gln Ser Leu Ala Gln Lys Leu Asp Leu Asp Ile      50 55 60 Ser Asn Ile Glu Leu Ile Asn Pro Ala Thr Ser Glu Leu Lys Ala Glu  65 70 75 80 Leu Val Gln Ser Phe Val Glu Arg Arg Lys Gly Lys Ala Thr Glu Glu                  85 90 95 Gln Ala Gln Glu Leu Leu Asn Asn Val Asn Tyr Phe Gly Thr Met Leu             100 105 110 Val Tyr Ala Gly Lys Ala Asp Gly Leu Val Ser Gly Ala Ala His Ser         115 120 125 Thr Gly Asp Thr Val Arg Pro Ala Leu Gln Ile Ile Lys Thr Lys Pro     130 135 140 Gly Val Ser Arg Thr Ser Gly Ile Phe Phe Met Ile Lys Gly Asp Glu 145 150 155 160 Gln Tyr Ile Phe Gly Asp Cys Ala Ile Asn Pro Glu Leu Asp Ser Gln                 165 170 175 Gly Leu Ala Glu Ile Ala Val Glu Ser Ala Lys Ser Ala Leu Ser Phe             180 185 190 Gly Met Asp Pro Lys Val Ala Met Leu Ser Phe Ser Thr Lys Gly Ser         195 200 205 Ala Lys Ser Asp Asp Val Thr Lys Val Gln Glu Ala Val Lys Leu Ala     210 215 220 Gln Gln Lys Ala Glu Glu Glu Lys Leu Glu Ala Ile Ile Asp Gly Glu 225 230 235 240 Phe Gln Phe Asp Ala Ile Val Pro Gly Val Ala Glu Lys Lys Ala                 245 250 255 Pro Gly Ala Lys Leu Gln Gly Asp Ala Asn Val Phe Val Phe Pro Ser             260 265 270 Leu Glu Ala Gly Asn Ile Gly Tyr Lys Ile Ala Gln Arg Leu Gly Gly         275 280 285 Tyr Asp Ala Val Gly Pro Val Leu Gln Gly Leu Asn Ser Pro Val Asn     290 295 300 Asp Leu Ser Arg Gly Cys Ser Ile Glu Asp Val Tyr Asn Leu Ser Ile 305 310 315 320 Ile Thr Ala Ala Gln Ala Leu Gln                 325

Claims (22)

Drug, comprising the steps of - in silico virtual screening of a ligand (ligand) having a similar activity (drugability) (In silico virtual screening ) by:
(a) analyzing the interaction between a target protein and a reference ligand to prepare a homology modeling, wherein the three-dimensional structure of the interaction between the target protein and the reference ligand ) Is modeled using a computer program and based on it, the physicochemical properties of the reference ligand binding to the target protein are obtained;
(b) preparing a candidate ligand library, wherein said ligand library conforms to the physicochemical properties of the reference ligand obtained in said step (a) and is characterized by a rule for predicting drug-likeness Made to conform;
(c) docking the target protein of step (a) and the ligand prepared in step (b), wherein the docking process is performed using a computer program using a MolDock algorithm, the docking score ), The ligands within the top 30 docking scores are selected and their pharmacological properties are analyzed to determine drug compliance; And
(d) analyzing the activity change of the target protein by the ligand identified in the step (c), and judging that the identified ligand inhibits the activity of the target protein , Silico virtual screening method.
The method of claim 1, wherein the target protein is a microbial-in silico virtual screening method which is characterized by having an amino acid sequence homology to the human protein and 50% or less in the resulting protein.
The method according to claim 1, wherein the physicochemical properties in step (a) are selected from the group consisting of molecular weight, isoelectric point, p I , calculated LopP value, H-bond donor, H-bond acceptor, heavy atom number , Polarizable surface area (Å ² ), polar or apolar desolvation energy (Kcal / mol), extinction coefficient, instability index in silico virtual screening method comprising the instability index), an aliphatic index (aliphatic index, AI) or GRAVY (grand hydropathy average) results.
2. The method of claim 1, wherein in step (a), the three-dimensional structure analysis of the interaction between the target protein and the reference ligand is based on a sequence-based secondary structure analysis of the target protein, Lt; RTI ID = 0.0 &gt; structural &lt; / RTI &gt; prediction.
The method of claim 4, wherein the in silico virtual screening method, characterized in that the available computer program PROCHECK, WHAT IF, SuperPose, YASARA or ESyPred3D.
The method according to claim 1, wherein the rule for predicting the drug compliance in the step (b) is a Lipinski's rule, a CMC-like rule, an MDDRF-like rule or a WDI rule, like rule. &lt; RTI ID = 0.0 &gt; 18. &lt; / RTI &gt;
The method of claim 1, wherein the docking score of the step (c) is in silico virtual screening method characterized in that the factor representing the combination possibility between the target protein and the ligand.
8. The method of claim 7, wherein the docking score of step (c) is calculated using a MolDock scoring function to determine (i) a binding position, (ii) a surface change due to binding between the ligand and the protein, and (iii) in silico virtual screening method characterized in that obtained by analyzing the energy calculation software).
The method of claim 1, wherein the pharmacological properties of the ligand in step (c) are predicted through ligand scout software, START software, absorption, distribution, metabolism and excretion, and Toxtree software, in silico virtual screening method, characterized in that for use in the production stage action occurs (pharmacophore) model.
(a) a therapeutically effective amount of a compound represented by formula (I); And (b) a pharmaceutically acceptable carrier. A pharmaceutical composition for preventing or treating a bacterial infectious disease comprising:
Formula I

In the formula (I), R ₁ and R ₂ are each independently of the other H, a C ₁ -C ₁₀ straight chain or branched chain alkyl or a C ₂ -C ₅ straight or branched chain alkenyl ; R ₃ is C ₁ -C ₁₀ straight or branched chain alkyl or C ₆ -C ₁₀ aryl; Or R &lt; _{4 &}

or

Wherein n is an integer from 1 to ₅ , and R &lt; ₅ &gt; is a pentane or hexaaryl aryl or heteroaryl, wherein the heteroaryl may contain sulfur, which is a single heteroatom.
11. A pharmaceutical composition according to claim 10, wherein R ₁ and R ₂ are each independently of the other H or C ₁ -C ₅ straight or branched chain alkyl or alkenyl.
11. The method of claim 10, wherein R ₃ is a pharmaceutical composition, characterized in that C ₁ -C ₅ straight chain or branched chain alkyl or a C ₆ aryl group.
[Claim 11] The pharmaceutical composition according to claim 10, wherein the compound represented by the formula (I) is any one of compounds represented by the following formulas (1-5)
[Chemical Formula 1]

(2)

(3)

[Chemical Formula 4]

[Chemical Formula 5]

[Claim 11] The pharmaceutical composition according to claim 10, wherein the compound represented by formula (I) inhibits the activity of phosphotransacetylase.
[Claim 11] The compound according to claim 10, wherein the compound represented by formula (I) is selected from the group consisting of Arg88, Arg89, Arg134, Arg148, Cys167, Arg285, Arg308 and Cys310 of the phosphotransacetylase of Sequence Listing RTI ID = 0.0 &gt; 1, &lt; / RTI &gt; amino acid sequence.
11. The pharmaceutical composition according to claim 10, wherein the bacterium is an anaerobic, fermentable, breathable or methanogenic microorganism.
17. The method of claim 16, wherein the anaerobic, fermentable, aerobic, or methanogenic microorganism is selected from the group consisting of Staphylococcus aureus , Bacillus subtilis , Methanosarcina thermophila , RTI ID = 0.0 &gt; Streptococcus &lt; / RTI &gt; mutans .
11. The method according to claim 10, wherein the bacterial infectious disease is selected from the group consisting of dermatitis, dermatitis, irritable bowel syndrome, meningitis, meningitis, prostatitis, endocarditis, purulent diseases such as dermatitis, pneumonia, mastitis, phlebitis, meningitis, enteritis, urinary tract infection, Characterized in that it is a wound wound, food poisoning, sepsis, bacteremia, tuberculosis or toxic shock syndrome.
A nucleotide sequence of 10 to 100 bases comprising the nucleotide sequence encoding the amino acid sequence of Arg88, Arg89, Arg134, Arg148, Cys167, Arg285, Arg308 or Cys310 of the phosphotransacetylase of the second sequence of the sequence listing A pharmaceutical composition for preventing or treating a bacterial infectious disease comprising an RNAi (RNA interference) molecule which binds or a binding agent which specifically binds to an amino acid sequence at said position as an active ingredient.
20. The pharmaceutical composition according to claim 19, wherein the RNAi molecule is an antisense oligonucleotide, siRNA, shRNA or miRNA.
20. The pharmaceutical composition according to claim 19, wherein the binding agent inhibits the activity of phosphotransacetylase.
20. The pharmaceutical composition according to claim 19, wherein the bacterial infection disease is a disease caused by Staphylococcus aureus.

Images