The emergence of novel pathogenic strains with increased antibacterial resistance patterns poses a significant threat to the management of infectious diseases. In this study, we aimed at utilizing the subtractive genomic approach to identify novel drug targets against
Recent progress in the field of computational biology and bioinformatics has generated various in silico analysis and drug designing approaches, eliminating the time and cost involved in the trial and error experimentations that go into drug development [
The complete proteome of pathogen,
Schematic diagram of the flow chart for drug target identification.
The whole proteome of
The essential proteins of
The subcellular locations of the essential proteins must be known for determination of suitable drug targets by allowing prediction of protein function and genome annotation. Computational prediction methods are used to establish the location of a particular protein in the cell. PSORTb version 3.0 (
The essential proteins associated with the unique pathway in
BlastP was performed individually for each of the drug targets found above against a database containing nonredundant protein sequences. As obtained from the taxonomy report, if the drug targets were found to be present in greater than 25 bacteria, they were classified as broad-spectrum targets. Different bacterial species were used as references.
In order to understand the structural basis of the protein targets specificity with the drugs, a computational target-ligand docking approach was used to analyze structural complexes of the novel druggable targets with the ligands (drugs). For this purpose, the three-dimensional structures of the novel druggable targets were downloaded from the UniProt database. The chemical structures of the ligands were obtained from DrugBank database [
This article describes a simple subtractive genomics approach for identification of a suitable drug target among the essential proteins within the proteome of
Subtractive proteomic and metabolic pathway analysis result for
Features of |
Number |
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Total number of proteins | 4906 |
Duplicates ( |
154 |
Nonparalogs | 4752 |
Nonhuman homologous proteins ( |
3664 |
Essential protein in DEG ( |
198 |
Pathways unique to |
14 |
Proteins involved in unique pathways | 52 |
The set of 198 proteins deemed to be essential through the DEG analysis was passed through the KEGG-KASS server to analyze their metabolic pathway. It was found that 52 proteins were involved in metabolic pathways unique to the
Essential proteins of
Associated pathway | Gene(s) name | KEGG orthology (KO) |
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D-Alanine metabolism | Alr | K01775 |
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Vancomycin resistance | alr, murG | K01775, K02563 |
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Lipopolysaccharide biosynthesis | lpxA, lpxC, lpxD, lpxH, lpxB, lpxK, kdtA, lpxL, kdsB, kdsA, lpxM, waaQ, waaC, waaF, waaG, waaJ, | K00677, K02535, K02536, K03269, K00748, K00912, K02527, K02517, K00979, K01627, K02560, K02849, K02841, K02843, K02844, K03279, |
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Cationic antimicrobial peptide (CAMP) resistance | lpxA, tolC, phoQ, arnT, pmrK, acrB, mexB, adeJ, smeE, mtrD, cmeB | K00677, K12340, K07637, K07264, K18138 |
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Peptidoglycan biosynthesis | murA, murC, murD, murF, mraY, murG, murJ | K00790, K01924, K01925, K01929, K01000, K02563, K03980, K05515 |
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Cell cycle | murG, dnaA, dnaB, ftsZ, ftsQ, ftsA | K02563, K02313, K02314, K03531, K03589, K03590 |
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Bacterial secretion system | tolC, yscJ, sctJ, hrcJ, yscT, sctT, hrcT, secD, secY, secA, tatC | K12340, K03222, K03228, K03072, K03076, K03070, K03118 |
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Two-component system | tolC, phoQ, envZ, ompR, ompF, rcsB, dnaA,glnL, ntrB,cheB, pagO | K12340, K07637, K07638, K07659, K09476, K07687, K02313, K07708, K03412, K07790 |
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Protein export | secD, secY, secA, tatC | K03072, K03076, K03070, K03118 |
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DNA replication | dnaB | K02314 |
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Bacterial chemotaxis | cheB | K03412 |
A metabolic pathway of particular importance is the lipopolysaccharide biosynthesis in
Peptidoglycan composes the cell wall of bacterial cells and inhibitors of peptidoglycans form a major class of antibiotics. Drug targets that inhibit peptidoglycan biosynthesis can minimize microbe generated pathogenicity [
Two-component system is a signal transduction system responsible for sensing any change in the environment or intracellular state of the bacteria and inducing the appropriate response to adapt to these changes [
Cationic antimicrobial peptides (CAMPS) are key components of the innate immune system and weaken the bacterial cell membrane integrity. On the other hand, various bacteria, including
Vancomycin is a glycopeptide antibiotic which is active against most Gram-positive bacteria. This inhibits the synthesis of peptidoglycan in the bacterial cell walls by interacting with D-Ala-D-Al-pentapeptide at C-terminus and preventing their addition to the peptidoglycan chain [
Since the ultimate goal of the current study was to identify novel drug targets, the next step was to evaluate the druggability of the essential proteins that were involved in unique
Druggable targets, available drugs, and broad-spectrum property analysis of the shortlisted essential proteins from
Target number | KEEG orthology (KO) | Protein name | Broad-spectrum property | Available drug in DrugBank | DrugBank IDs |
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( |
K01775 | Alanine racemase | 25 | Cycloserine | DB00260 |
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( |
K00790 | UDP-N-Acetyl glucosamine 1-carboxyvinyltransferase | 19 | Fosfomycin | DB00828 |
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( |
K05515 | Penicillin-binding protein 2 | 8 | Ceftazidime |
DB00438 |
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( |
K12340 | Outer membrane channel protein tolC | 28 | Colistin | DB00803 |
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( |
K09476 | Outer membrane pore protein F | 26 | Polymyxin B Sulfate | DB00781 |
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( |
K03531 | Cell division protein FtsZ | 29 | Guanosine 5′-diphosphate | DB04315 |
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( |
K01627 | 2-Dehydro-3-deoxyphosphooctonate aldolase (KDO 8-P synthase) | 8 | 2-Phosphoglyceric acid | DB01709 |
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( |
K02563 | UDP-N-Acetylglucosamine--N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase | 29 | Uridine diphosphate-N-acetylgalactosamine | DB02196 |
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( |
K07637 | Two-component system, OmpR family, sensor histidine kinase PhoQ | 197 | Radicicol | DB03758 |
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( |
K07638 | Two-component system, OmpR family, osmolarity sensor histidine kinase EnvZ | 32 | Phosphoaminophosphonic acid-adenylate ester | DB04395 |
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( |
K18138 | Multidrug efflux pump arcB | 279 | Rhodamine 6g | DB03825 |
The proteins in Table
Lowest docking energies and important residues of the binding site observed to be interactive with the ligands.
Protein name or names of molecules | UDP-N-Acetylglucosamine O-acyltransferase | UDP-3-O-[3-Hydroxymyristoyl] N-acetyl glucosamine deacetylase | 3-Deoxy-manno-octulosonate cytidylyltransferase | Phosphate regulon response regulator OmpR | Nitrogen regulation sensor histidine kinase GlnL | Chemotaxis family, response regulator CheB |
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UniProt ID of the database target | O25927 | P47205 | P44490 | O32393 | Q9X180 | Q9A5I5 |
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% identity | 42.688 | 57.237 | 63.855 | 39.516 | 30.00 | 37.273 |
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Possible drug | D-Tartaric acid. | (2R)-N-Hydroxy-3-naphthalen-2-yl-2-[(naphthalen-2-ylsulfonyl)amino]propanamide | Cmp-2-Keto-3-Deoxy-Octulosonic Acid | Heparin disaccharide Iii-S | Ethylmercurithiosalicylic acid | Guanosine-5′-Monophosphate |
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DrugBank ID | DB01694 | DB07861 | DB04482 | DB02353 | DB02731 | DB01972 |
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Being used against |
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Binding affinity of the drug with potential target (kcal/mol) | −4.4 | −9.8 | −10.4 | −9.0 | −4.8 | −7.2 |
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Binding affinity of the drug with database target Kcal/mol | −4.8 | −9.2 | −8.2 | −9.9 | −4.3 | −6.2 |
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Associated amino acid residues present in the database target binding pocket | Gly(48), Gly(65), Glu(76) | Leu(18), Met(62), Glu(77), His(78), Thr (190), Ile(197), Ala(206), Ala(214), His(237), Asp(241), His(264) | Gly(75), Thr(76), Asn(96), Gln(98), His(185), Gly(187), Tyr(189), Leu(213), Glu(214), Gln(215) | Asp(1017), Phe(1021), Thr(1022), Val(1059), Glu(1153) | Asp(464), Glu(467), Tyr(487), Phe(489) | Asp(41), Arg(114), Gly(137), Tyr(143), Glu(144) |
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Associated amino acid residues present in the potential target binding pocket | Ser(0), Gly(17), Ser(19), Glu(37), Pro(34), His(35) | Leu(18), Leu(62), Glu(78), Phe(161), Phe(192), Phe(194), Ile(198), Cys(207), Ala(215), His(265) | Pro(8), Arg(10), Arg(15), Ser(74), Gly(75), Gln(98), Asp(100), Arg(164), His(181), Gly(183), Tyr(185), Met(208), Glu(210), Gln(211) | Arg(119), Arg(121), Asp(160), Val(182), Gly(184), Arg(186) | Phe(483), Asn(485), Pro(752) | Glu(125), Lys(126), Ser(164), Arg(255), Arg(257) |
The current study was further reinforced by performing comparative docking studies of the novel druggable proteins with the ligands. Binding affinities from docking were compared between our target proteins and intended targets from other species against the corresponding drug. The shortlisted potential drug targets showed a pattern of similar binding characteristics, similar residues involved in the active site, and lower free energy (Table
Important residues of the binding site of UDP-N-acetyl glucosamine O-acyltransferase of
Important residues of the binding site of UDP-3-O-[3-hydroxymyristoyl] N-acetyl glucosamine deacetylase of
Important residues of the binding site of 3-deoxy-manno-octulosonate cytidylyltransferase of
Important residues of the binding site of phosphate regulon response regulator OmpR of
Important residues of the binding site of nitrogen regulation sensor histidine kinase GlnL of
Important residues of the binding site of chemotaxis family, response regulator CheB of
The vast array of information regarding the proteomes and genomes of various prokaryotic organisms and knowledge obtained from the human genome project can be manipulated to accelerate drug designing and gain further knowledge of pharmacogenomics in the treatment of bacterial infections. Subtractive genomics can aid in the identification of proteins targeted by existing FDA approved targets. A total of 52 potential targets were found within the
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Tanvir Hossain and Mohammad Kamruzzaman contributed equally to this work as first author.