51Drug TargeT InsIghTs 2014:8 Open Access: Full open access to this and thousands of other papers at http://www.la-press.com. Drug Target Insights Genome-wide Analysis of Mycoplasma hominis for the Identification of Putative Therapeutic Targets Md. Masud Parvege, Monzilur rahman and Mohammad shahnoor hossain Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka, Bangladesh. A BSTR ACT: Ever increasing propensity of antibiotic resistance among pathogenic bacteria raises the demand for the development of novel therapeutic agents to control this grave problem. Advances in the field of bioinformatics, genomics, and proteomics have greatly facilitated the discovery of alterna- tive drugs by swift identification of new drug targets. In the present study, we employed comparative genomics and metabolic pathway analysis with an aim of identifying therapeutic targets in Mycoplasma hominis. Our study has revealed 40 annotated metabolic pathways, including five unique pathways of M. hominis. Our study also identified 179 essential proteins, including 59 proteins having no similarity with human proteins. Further filtering by molecular weight, subcellular localization, functional analysis, and protein network interaction, we identified 57 putative candidates for which new drugs can be devel- oped. Druggability analysis for each of the identified targets has prioritized 16 proteins as suitable for potential drug development. K E Y WOR DS: Mycoplasma hominis, drug targets, bioinformatics, genomics, proteomics CITATION: Parvege et al. genome-wide analysis of Mycoplasma hominis for the Identification of Putative Therapeutic Targets. Drug Target Insights 2014:8 51–62 doi:10.4137/DTI.s19728. RECEIVED: august 31, 2014. RESUBMITTED: november 6, 2014. ACCEPTED FOR PUBLICATION: november 10, 2014. ACADEMIC EDITOR: anuj Chauhan, editor in Chief TYPE: Original Research FUNDING: This work was supported by the research grant provided to Dr. Mohammad Shahnoor Hossain from Biotechnology Research Centre, University of Dhaka. The authors confirm that the funder had no influence over the study design, content of the article, or selection of this journal. COMPETING INTERESTS: Authors disclose no potential conflicts of interest. COPYRIGHT: © the authors, publisher and licensee Libertas Academica Limited. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License. CORRESPONDENCE: mshahnoor@du.ac.bd Paper subject to independent expert blind peer review by minimum of two reviewers. All editorial decisions made by independent academic editor. Upon submission manuscript was subject to anti-plagiarism scanning. Prior to publication all authors have given signed confirmation of agreement to article publication and compliance with all applicable ethical and legal requirements, including the accuracy of author and contributor information, disclosure of competing interests and funding sources, compliance with ethical requirements relating to human and animal study participants, and compliance with any copyright requirements of third parties. This journal is a member of the Committee on Publication Ethics (COPE). Introduction Mycoplasmas are cell wall-deficient, smallest, free-living organ- isms, which are resistant to many commonly used antimicrobial agents.1 Although most of the Mycoplasma are naturally found as commensals in the genitourinary tract, three species are well- known pathogens: Mycoplasma pneumoniae, M. hominis, and M. genitalium.2 To lead a parasitic lifestyle, M. hominis resides in the urogenital tract of women and sexually active men where it gains necessary nutrients.3 Sometimes, the bacterium is found in a symbiotic relationship with protozoa, Trichomonas vaginalis, an event that allows for more successful transfer of M. hominis from one person to another, and increases its resistance to antibiot- ics.3,4 This bacterium is the causative agent of numerous health problems, such as pelvic inflammatory disease, bacterial vagino- sis, post-partum fever, and infertility in females.2,5–7 In addition, M. hominis is capable of causing diseases of the central nervous system in newborn babies8 and is associated with prostate cancer.9 Currently, antibiotics are the only available treatment option against M. hominis infection. However, this species is inherently resistant to beta-lactam group antibiotics and mac- rolides because of their lack of cell wall and a mutation in 23S rRNA, respectively.10 Moreover, M. hominis has been found to carry resistance trait against ciprofloxacin and ofloxacin.11 The increasing trend of antibiotic resistance demands the dis- covery of alternative therapeutic agents for the treatment of infection caused by this bacterium. These issues require the need for exploring new drug targets in this bacterium, which will create the avenue for new drug discovery or will augment the sensitivity of the currently existing antimicrobial agents. Increasing availability of related genomics, proteomics, metabolomics, and many other omics data of the infectious agents and information about molecules that can alter the capability of survival of the infectious organism have facili- tated the search for new drugs or drug targets. As the genome Journal name: Drug Target Insights Journal type: Original Research Year: 2014 Volume: 8 Running head verso: Parvege et al Running head recto: Identification of therapeutic targets in Mycoplasma hominis http://www.la-press.com/drug-target-insights-journal-j23 http://www.la-press.com http://www.la-press.com http://www.la-press.com/drug-target-insights-journal-j23 http://dx.doi.org/10.4137/DTI.S19728 http://creativecommons.org/licenses/by-nc/3.0/ http://creativecommons.org/licenses/by-nc/3.0/ mailto:mshahnoor@du.ac.bd Parvege et al 52 Drug TargeT InsIghTs 2014:8 of M. hominis, the second smallest genome among the free- living organisms, has already been sequenced, similar kind of study can also be possible for this bacterium.12 Numerous in silico methods have already been developed for the predic- tion of potential drug targets in many bacteria and fungi.13–16 These approaches predict drug targets by comparing the pro- teins present in an infectious agent to those present or absent in the host. Proteins that are unique to the infectious agent and essential for its survival can be potential therapeutic tar- gets. In principle, any essential protein present in the organ- ism can be targeted to control the growth of that organism.17 In the present study, we performed genomics, proteomics, and metabolic pathway analysis of M. hominis with an aim of identifying new drug targets. This study identified several drug targets and available inhibitors for those targets. We expect that present findings will not only extend our under- standing of the molecular pathogenesis of M. hominis, but also open a new window to develop novel therapeutic agents against this deadly pathogen. Methods Identification of pathogen and host metabolic pathways. Consideration of safety is the prime issue for the development of any therapeutic agent. If the therapeutic agent is not safe for the host, it will never get regulatory approval. Although the overall similarity between eukaryotic and prokaryotic cell is very limited, the similarity in the coding region of a particular gene or functional domain of any protein may result in cross- reactivity of a therapeutic agent against the host. In addition, if a drug inhibits any essential protein of the host, it might pro- duce severe side effects to the host species. Therefore, the start- ing point of this study was to compare the metabolic pathways of the pathogen and the host species. Information of metabolic pathways was extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database.18,19 Metabolic pathways and respective identification numbers of M. hominis and H. sapiens were retrieved from the NCBI database. Sub- sequently, a manual comparison was made and pathways that appeared only in M. hominis but not in humans were selected as unique, and the remaining pathways of the pathogen were cate- gorized as common pathways. Proteins were identified from the unique and common pathways and the corresponding amino acid sequences were downloaded from the NCBI database. Screening of essential genes. Genes that are indispensable to supporting cellular life are called essential genes. Database of Essential Genes (DEG) includes all of the essential genes that are currently available. To identify essential genes of M. hominis, proteins of unique and common pathways were subjected to BLASTp against DEG with an E-value cut-off of 10-10 and with a minimum bit score of 100.20 Identification of non-host essential proteins. After the identification of essential proteins, they were compared with the human non-redundant protein sequence database (nr) to identify non-host proteins of M. hominis. A BLASTp analysis was performed and proteins without hits below the threshold E-value of 0.005 and 35% identity were picked out as non- host essential proteins.21 Molecular weight determination and protein 3-D struc- ture identification. Molecular weight (MW ) of each of the potential targets was determined using online tools followed by confirmation with the available literature. Previous litera- ture suggested that smaller proteins are suitable targets for drug development because they are more soluble and easier to purify in comparison with large proteins.22 Proteins having MW larger than 110  kDa were excluded, and the resultant list of proteins was enlisted as sigma (∑) and was used for qualitative analyses. The 3-D structure was scanned by searching the Pro- tein Data Bank (PDB)23,24 and ModBase25,26 databases. PDB is the worldwide repository where experimentally determined structures of proteins, nucleic acids, and complex biomolecular assemblies are deposited. These structures are curated and anno- tated following the standards set by PDB. On the other hand, ModBase is a database of protein structures that have been developed by computational approaches and validated by sta- tistically significant sequence alignment and model assessment. Qualitative characterization of the ∑ list. Different structural and molecular criteria that facilitate prioritizing therapeutic targets in pathogens were assessed for the ∑ list.27 This involved the prediction of subcellular location, functional analysis, protein network analysis, broad spectrum analysis, and druggability analysis of the ∑ list. Subcellular localization analysis. Subcellular localization analysis of a protein reveals whether that protein is suitable as a drug or a vaccine target. Cytoplasmic proteins can work better as drug targets while surface membrane proteins can be used as tar- gets for vaccine development.28 PSORTb v3.0 server was used to predict the subcellular localization of the proteins,29 and the results were further crosschecked with predictions obtained from CELLO v.2.5,30,31 TOPCONS,32 and TMHMM.33 PSORTb uses 11692 proteins of known localization from bacteria and archaea as a training set to sort out the location of a given pro- tein. CELLO contains 9033 protein sequences as benchmark datasets for the prediction of protein localization using a support vector machine (SVM) method. TMHMM is based on hidden Markov model, and experimental evidence shows that it cor- rectly predicts 97–98% of the transmembrane helices. Functional analysis. The functions of the hypothetical proteins in the ∑ list were predicted using InterPro online tool.34 InterPro classifies hypothetical proteins into families and predicts domains and important sites by integrating vari- ous protein signature recognition methods and databases. Broad spectrum analysis. A BLASTp search against a wide range of pathogenic bacteria with an expected threshold value of 0.005 was used to analyze proteins in the ∑ list for the identification of broad spectrum targets. A total of 240 disease- causing bacteria from different genus were used in the broad spectrum analysis.35 From the homology analysis against each of the pathogen, it is speculated that close homologs present in http://www.la-press.com http://www.la-press.com/drug-target-insights-journal-j23 Identification of therapeutic targets in Mycoplasma hominis 53Drug TargeT InsIghTs 2014:8 maximum number of pathogens are better likely to be promis- ing broad spectrum targets. Interactome analysis. A network of protein–protein inter- action was constructed for each of the ∑ listed proteins using STRING 9.1.36,37 STRING constructs protein–protein interac- tion networks based on experimental data, gene-based analysis (neighborhood, gene fusion, co-occurrence, and co-expression), curated pathways database, and various protein interactions databases. High confidence interactors with score greater than or equal to 0.700 alone were included in the protein network. To minimize false positives and false negatives, all interactors with low as well as medium confidence scores were removed from the network. Druggability analysis. To be druggable, a target must have the potency to interact with drug or drug-like molecules with high affinity. In this study, the druggability potential of each protein of the ∑ list was assessed by DrugBank.38 Cur- rently, DrugBank is a huge and comprehensive collection of drugs with the target information. This database comprises 6816 experimental and FDA-approved drugs, 4326 drug tar- gets, and 169 drug enzymes/carriers. In DrugBank, each of the ∑ list targets was explored for similar therapeutic targets with the same biological function. Degree of homology was evalu- ated using the BLASTp program with an expected value of 10-05. Presence of targets from the ∑ list in DrugBank with the same biological function acts as evidence for their druggable property.39 In contrast, their absence suggests the novelty of the targets, and therefore they are classified as “novel targets.”40 Ranking the druggable therapeutic targets based on quantitative characteristics. The effectiveness of a putative target may depend on its degree of essentiality for the survival of the pathogenic organism under diverse environmental con- ditions. Some targets may prove essential for a limited number of physiological conditions, whereas others may prove essen- tial irrespective of environmental conditions. The number of homologs found in DEG and the number of interactors of the particular target are the two cardinal factors that determine the degree of essentiality. Moreover, the number of available drugs present in DrugBank also determines the druggability of a par- ticular therapeutic target. Therefore, we ranked the therapeutic targets by calculating the product of the number of homologs found in DEG, the number of interactors of the target protein, and the number of drugs available in DrugBank. The drug tar- get score was calculated by dividing the product by 100, and the putative therapeutic targets were ranked according to the scores. Results In the present study, we identified potential therapeutic targets in M. hominis employing comparative and subtractive genomic analyses of metabolic pathways. We used a systematic hierarchical approach that involved various computational tools utilization, databases search, and drug target prioritization analysis (Fig. 1). Figure 1. Schematic representation of workflow for the identification of therapeutic targets. http://www.la-press.com http://www.la-press.com/drug-target-insights-journal-j23 Parvege et al 54 Drug TargeT InsIghTs 2014:8 Table 1. Unique and common metabolic pathways of M. hominis with reference to H. sapiens. NO UNIQUE PATHWAYS PATHWAY ID TOTAL PROTEINS 1 Polycyclic aromatic hydrocarbon degradation 00624 1 2 Methane metabolism 00680 5 3 Biosynthesis of secondary metabolites 01110 22 4 Microbial metabolism in diverse environments 01120 20 5 Bacterial secretion system 03070 8 NO COMMON PATHWAYS PATHWAY ID TOTAL PROTEINS 1 Glycolysis/Gluconeogenesis 00010 10 2 Pentose phosphate pathway 00030 9 3 Fructose and mannose metabolism 00051 4 4 Oxidative phosphorylation 00190 11 5 Purine metabolism 00230 19 6 Pyrimidine metabolism 00240 20 7 Glycine, serine and threonine metabolism 00260 2 8 Cysteine and methionine metabolism 00270 3 9 Arginine and proline metabolism 00330 4 10 Selenocompound metabolism 00450 3 11 Cyanoamino acid metabolism 00460 2 12 glutathione metabolism 00480 2 13 Starch and sucrose metabolism 00500 3 14 Glycerolipid metabolism 00561 3 15 Glycerophospholipid metabolism 00564 6 16 Pyruvate metabolism 00620 3 17 Propanoate metabolism 00640 2 18 One carbon pool by folate 00670 3 19 Thiamine metabolism 00730 2 20 Riboflavin metabolism 00740 1 21 Nicotinate and nicotinamide metabolism 00760 4 22 Aminoacyl-tRNA biosynthesis 00970 57 23 Carbon metabolism 01200 16 24 Biosynthesis of amino acids 01230 16 25 ABC transporters 02010 16 26 ribosome 03010 58 27 rna degradation 03018 5 28 RNA polymerase 03020 3 29 DNA replication 03030 12 30 Protein export 03060 9 31 Base excision repair 03410 5 32 Nucleotide excision repair 03420 6 33 Mismatch repair 03430 9 34 Homologous recombination 03440 14 35 sulfur relay system 04122 2 Comparison bet ween M. hominis and H. sapiens metabolic pathways identified five unique pathways and 35 common pathways. Primary information about the met- abolic pathways of M. hominis and humans was retrieved from the KEGG database. Currently, the KEGG database contains information about 40 metabolic pathways for M. hominis (Table 1). Comparison with human pathways revealed five pathways containing 36 proteins as unique to M. hominis while the remaining 35 pathways containing 197 proteins as common to M. hominis and humans. Thirty-five http://www.la-press.com http://www.la-press.com/drug-target-insights-journal-j23 Identification of therapeutic targets in Mycoplasma hominis 55Drug TargeT InsIghTs 2014:8 out of 36 proteins of unique pathways are also present in common pathways. After removing redundant proteins, a total of 198 protein sequences were obtained from the NCBI database. Homology search revealed 179 putative essential proteins for M. hominis. Proteins that are required by pathogenic microor- ganisms to survive are called essential proteins. Essential proteins are prime targets for drugs and vaccine development. To iden- tify the essential proteins, 198 proteins of common and unique pathways were compared against the DEG database. Out of 198 input proteins, 179 proteins were found to be essential for the pathogen. The distribution of the 179 identified essential pro- teins in each of the 22 bacteria of DEG is presented in Figure 2. Comparative analysis of the essential proteins revealed 59 of them as non-host. The aim of the non-homology analysis was to identify pathogen-specific proteins that are nonhomol- ogous to the host. This step is important to avoid undesir- able cross-reactivity of the drug arising from its binding to the active sites of the homologous proteins in the host. Out of 179 proteins, BLAST search of essential proteins against non- redundant database of H. sapiens identified only 59 proteins as non-host but essential proteins. Exclusion of high MW proteins generated a list of 57 putative therapeutic targets. Proteins of low MW are preferable as drug targets because of their solubility and ease of purification. By MW analysis through online tools and literature study, 57 out of 59 proteins having MW below 110 kDa were short listed (∑ list) for the qualitative charac- terization. Although no experimentally solved 3-D structure was found for the ∑ listed proteins, computationally annotated 3-D models were available for 16 proteins of the ∑ list in the ModBase database. Cellular localization analysis mapped the proteins of ∑ list to different cellular locations. Target proteins found in the cytoplasm can be used as potential drug targets, whereas extracellular and membrane-bound proteins can work as probable vaccine targets.28 Various online tools were used for the prediction of subcellular localization and mem- brane topology. Based on the localization score, PSORTb predicted the location of 32 targets in the cytoplasm and 13 in the membrane. However, 12 proteins could not be mapped to any cellular location with this software. CELLO predicted 43 targets in the cytoplasm and 14 in the mem- brane (Table 2). This tool was able to predict the location of 12 targets previously uncharacterized by PSORTb, and the result of CELLO was in agreement with TOPCONS predic- tion as well. PSORTb prediction varied with CELLO and TOPCONS in the case of only one protein, MHO_3910. 0 20 40 60 80 100 120 140 160 180 A ci ne to ba ct er b ay ly i A D P 1 B ac ill us s ub til is 1 68 B ac te ro id es fr ag ili s 63 8R B ac te ro id es th et ai ot ao m ic ro n V P I- 54 82 B ur kh ol de ria p se ud om al le i K 96 24 3 B ur kh ol de ria th ai la nd en si s E 26 4 C am py lo ba ct er je ju ni s ub sp . j ej un i… C au lo ba ct er c re sc en tu s E sc he ric hi a co li M G 16 55 I E sc he ric hi a co li M G 16 55 II F ra nc is el la n ov ic id a U 11 2 H ae m op hi lu s in flu en za e R d K W 20 H el ic ob ac te r py lo ri 26 69 5 M yc ob ac te riu m tu be rc ul os is H 37 R v M yc ob ac te riu m tu be rc ul os is H 37 R v II M yc ob ac te riu m tu be rc ul os is H 37 R v III M yc op la sm a ge ni ta liu m G 37 M yc op la sm a pu lm on is U A B C T IP P or ph yr om on as g in gi va lis A T C C 3 32 77 P se ud om on as a er ug in os a P A O 1 P se ud om on as a er ug in os a U C B P P -P A 14 S al m on el la e nt er ic a se ro va r T yp hi S al m on el la e nt er ic a se ro va r T yp hi T y2 S al m on el la e nt er ic a S L1 34 4 S al m on el la e nt er ic a su bs p. e nt er ic a st r. … S al m on el la ty ph im ur iu m L T 2 S he w an el la o ne id en si s M R -1 S ph in go m on as w itt ic hi i R W 1 S ta ph yl oc oc cu s au re us N 31 5 S ta ph yl oc oc cu s au re us N C T C 8 32 5 S tr ep to co cc us p ne um on ia e S tr ep to co cc us s an gu in is V ib rio c ho le ra e N 16 96 1 N um be r of h its Figure 2. Comparison of M. hominis genome against DEG. The height of the bars indicates the number of hits on other genomes. http://www.la-press.com http://www.la-press.com/drug-target-insights-journal-j23 Parvege et al 56 Drug TargeT InsIghTs 2014:8 Table 2. Qualitative characterization of ∑ list targets. The subcellular localizations are based on the consensus results from prediction by PSORTb, CELLO, and TOPCONS. TMH is transmembrane helix predicted by TMHMM. NO KEGG ID GENE PRODUCT DEFINITION SUBCELLULAR LOCALIZATION TMH BROAD SPECTRUM PROPERTY INTERACTORS 3D MODEL DRUGGABILITY 1 MhO_0670 Fructose-bisphosphate aldolase Cytoplasm 0 No (91) 13 Yes Druggable 2 MhO_0100 Putative nucleoside phosphorylase Cytoplasm 0 No (71) 4 Yes Druggable 3 MhO_0980 Thymidylate kinase Cytoplasm 0 Yes (102) 10 no Druggable 4 MhO_0990 DNA polymerase III subunit Cytoplasm 0 No (84) 10 no novel 5 MhO_1260 Putative nicotinate-nucleotide adenylyl transferase Cytoplasm 0 No (98) 13 Yes Druggable 6 MhO_1670 Cytidylate kinase Cytoplasm 0 Yes (102) 8 Yes Druggable 7 MhO_2380 Riboflavin biosynthesis protein Membrane 5 No (44) 12 no novel 8 MhO_2730 DNA-directed RNA poly- merase subunit alpha Cytoplasm 0 No (91) 2 Yes Druggable 9 MhO_0200 aTP synthase subunit a Cytoplasm 0 No (89) 50 no novel 10 MhO_0210 aTP synthase subunit C Membrane 2 No (42) 13 no Druggable 11 MhO_3350 Purine-nucleoside phosphorylase Cytoplasm 0 No (74) 9 Yes Druggable 12 MhO_0220 ATP synthase subunit B Membrane 1 No (34) 11 Yes novel 13 MhO_3650 Aspartate—ammonia ligase Cytoplasm 0 No (30) 2 no novel 14 MhO_3810 Putative 2,3-bisphosphoglyc- erate-independent phospho- glycerate mutase Cytoplasm 0 No (59) 9 Yes Druggable 15 MhO_3840 Acetate kinase Cytoplasm 0 No (96) 7 Yes Druggable 16 MhO_3880 Ribose-5-phosphate isomerase Cytoplasm 0 No (76) 9 Yes Druggable 17 MhO_4030 Thiamine biosynthesis protein Cytoplasm 0 No (80) 2 no novel 18 MhO_4130 DNA polymerase I Cytoplasm 0 Yes (127) 13 no Druggable 19 MhO_4370 Putative glycerol-3-phos- phate acyltransferase Membrane 6 No (76) 3 no novel 20 MhO_4600 uridylate kinase Cytoplasm 0 Yes (111) 6 Yes novel 21 MhO_4680 Fatty acid/phospholipid synthesis protein Cytoplasm 0 No (82) 9 Yes novel 22 MhO_2040 Putative DNA polymerase III, delta subunit Cytoplasm 0 No (41) 6 no novel 23 MhO_0850 UvrABC system protein C Cytoplasm 0 Yes (102) 7 no novel 24 MhO_0030 50S ribosomal protein L34 Cytoplasm 0 No (64) 50 no novel 25 MhO_0880 50S ribosomal protein L1 Cytoplasm 0 No (97) 50 no novel 26 MhO_1010 30S ribosomal protein S20 Cytoplasm 0 No (72) 0 no novel 27 MhO_1120 30S ribosomal protein S6 Cytoplasm 0 No (59) 32 no novel 28 MhO_1280 50S ribosomal protein L35 Cytoplasm 0 No (72) 31 no novel 29 MhO_1180 Phenylalanyl-trna synthe- tase subunit beta Cytoplasm 0 Yes (116) 32 no Druggable 30 MhO_0050 DNA polymerase III subunit beta Cytoplasm 0 Yes (101) 24 Yes Druggable 31 MhO_1520 Oligopeptide transport system permease protein Membrane 6 Yes (119) 5 no novel 32 MhO_1990 Cobalt ABC transporter permease protein Membrane 5 No (57) 3 no novel 33 MhO_0970 Recombination protein RecR Cytoplasm 0 Yes (104) 10 no novel 34 MhO_1840 Holliday junction DNA helicase RuvA Cytoplasm 0 No (93) 7 no novel 35 MhO_1130 Single-strand binding protein Cytoplasm 0 No (87) 4 no novel http://www.la-press.com http://www.la-press.com/drug-target-insights-journal-j23 Identification of therapeutic targets in Mycoplasma hominis 57Drug TargeT InsIghTs 2014:8 Table 2. (Continued) NO KEGG ID GENE PRODUCT DEFINITION SUBCELLULAR LOCALIZATION TMH BROAD SPECTRUM PROPERTY INTERACTORS 3D MODEL DRUGGABILITY 36 MhO_2160 DNA primase Cytoplasm 0 Yes (129) 8 no novel 37 MhO_3910 Replicative DNA helicase Cytoplasm 0 Yes (115) 14 Yes novel 38 MhO_3690 Putative Ribonuclease J Cytoplasm 0 No (75) 1 no novel 39 MhO_4170 Phosphopentomutase Cytoplasm 0 No (49) 6 Yes novel 40 MhO_0010 Putative membrane insertase OXA1/ALB3/YidC Membrane 6 No (96) 4 no novel 41 MhO_1860 Protein-export membrane protein Membrane 12 No (07) 6 no novel 42 MhO_2790 Preprotein translocase secY subunit Membrane 10 Yes (104) 49 no novel 43 MhO_4440 Not predicted Membrane 1 No (06) 6 no novel 44 MhO_4460 Spermidine/putrescine ABC transporter permease Membrane 6 No (97) 6 no novel 45 MhO_4470 Spermidine/putrescine ABC transporter permease Membrane 6 No (68) 7 no novel 46 MhO_4480 Not predicted Membrane 1 No (0) 4 no novel 47 MhO_2610 50S ribosomal protein L19 Cytoplasm 0 No (77) 50 no novel 48 MhO_2660 50S ribosomal protein L21 Cytoplasm 0 No (72) 50 no novel 49 MhO_2700 50S ribosomal protein L10 Cytoplasm 0 No (70) 43 no Druggable 50 MhO_2710 50S ribosomal protein L32 Cytoplasm 0 No (44) 34 no Druggable 51 MhO_2820 50S ribosomal protein L18 Membrane 0 No (85) 50 no novel 52 MhO_2830 50S ribosomal protein L6 Cytoplasm 0 No (90) 50 no novel 53 MhO_2870 50S ribosomal protein L24 Cytoplasm 0 No (76) 50 no novel 54 MhO_2900 50S ribosomal protein L29 Cytoplasm 0 No (79) 50 no novel 55 MhO_3020 50S ribosomal protein L31 Cytoplasm 0 No (78) 50 no novel 56 MhO_3920 50S ribosomal protein L9 Cytoplasm 0 No (71) 50 no novel 57 MhO_0630 Carbamate kinase Cytoplasm 0 No (48) 3 Yes novel TMHMM predicted the number of transmembrane helixes (TMH) in membrane proteins. Biological functions were assigned to seven hypo- thetical proteins. Nine proteins of the ∑ list with KEGG ID MHO_0100, MHO_1260, MHO_3810, MHO_4370, MHO_2040, MHO_3690, MHO_0010, MHO_4440, and MHO_4480 were hypothetical. Seven of them could be anno- tated using the InterPro online server (Table 2). MHO_0100 was predicted to have a nucleoside phosphorylase domain with a catalytic role in the nucleoside metabolic process. MHO_1260 is a probable member of the nicotinate-nucleotide adenylyl- transferase protein family having a role in the NAD biosynthetic pathway. MHO_3810 probably plays a role as phosphoglycerate mutase 2,3-bisphosphoglycerate-independent enzyme in glucose metabolism. MHO_4370 and MHO_2040 were recognized as glycerol-3-phosphate acyltransferase (PlsY ) and DNA poly- merase III (delta subunit) type proteins, respectively. MHO_3690 protein was predicted to be beta-lactamase-like protein having RNA- and metal ion-binding potential. The MHO_0010 was recognized as membrane insertase YidC/Oxa1 (C-terminal) having a pivotal role in protein insertion into the membrane. InterPro could not predict any function for MHO_4440 and MHO_4480 as no homolog was found in the database. Comparison of proteomes of 240 pathogens identified 12 broad spectrum targets. BLASTp homology search for proteins in the ∑ list against the whole proteome of each of the 240 bacterial pathogens identified ideal broad spectrum targets. This comparative sequence analysis revealed 12 pro- teins having homologs in more than 100 pathogens, whereas homologs were found in more than 50 pathogens for 33 pro- teins present in the ∑ list. ∑ listed proteins exhibit maximum homology to the proteome of Mycoplasma pathogens like M. capricolum, M. gallisepticum, M. genitallium, M. penetrans, and M. pneumoniae. Proteins having homologs in more than 100 pathogens were considered as broad spectrum target candidates (Table 2). Interactome analysis identified 23 targets having maximum interactors. Protein–protein interactions are important determinants of protein function. To evaluate the functional importance of ∑ listed targets in the metabolic net- work, analysis on protein interaction network was performed using the STRING database. Target protein interacting with http://www.la-press.com http://www.la-press.com/drug-target-insights-journal-j23 Parvege et al 58 Drug TargeT InsIghTs 2014:8 Table 3. Non-host essential proteins similar to FDA-approved/experimental drug targets and the list of FDA-approved drugs for the targets. NO KEGG ID ASSOCIATED PATHWAYS DRUGBANK ID DRUG NAME DRUG GROUP 1 MhO_0670 Glycolysis/Gluconeogenesis, Pentose phosphate pathway, Fructose and mannose metabolism DB03026 Phosphoglycolohydroxamic Acid Experimental 2 MhO_0100 Cysteine and methionine metabolism, Biosynthesis of amino acids DB07463, DB00173 adenine Approved 3 MhO_0980 Pyrimidine metabolism DB03280 P1-(5′-Adenosyl)P5-(5′-Thymidyl) Pentaphosphate Experimental 4 MhO_1260 Nicotinate and nicotinamide metabolism DB04099, DB04272 Deamido-nad+, Citric Acid Experimental, Nutraceutical 5 MhO_1670 Pyrimidine metabolism DB02456, DB02883, DB03403 DB04555 Cytosine arabinose-5′-Phosphate, 2′,3′-Dideoxycytidine-5′-Monophosphate, Cytidine-5′-Monophosphate, Cytidine-5′-Diphosphate Experimental 6 MhO_2730 Purine metabolism, Pyrimidine metabolism, RNA polymerase DB00615, DB08226, DB08266 rifabutin, Myxopyronin B, Methyl carbamate Approved, Experimental 7 MhO_0210 Oxidative phosphorylation DB04464, DB03143 n-Formylmethionine, nonan-1-Ol Experimental 8 MhO_3350 Purine metabolism, Pyrimidine metabolism, Nicotinate and nicotinamide metabolism, Biosynthesis of secondary metabolites DB02857, DB04627 guanosine, Cyclouridine Experimental 9 MhO_3810 Glycolysis/Gluconeogenesis, Glycine, serine and threonine metabolism, Methane metabolism DB01709, DB04510 2-Phosphoglyceric Acid, 3-Phosphoglyceric Acid Experimental 10 MhO_3840 Pyruvate metabolism, Propanoate metabolism, Methane metabolism DB01942 Formic Acid Experimental 11 MhO_3880 Pentose phosphate pathway, Fructose and mannose metabolism, Biosynthesis of secondary metabolites DB03661, DB03108 Cysteinesulfonic Acid, 4-Phospho-D-Erythronate Experimental 12 MhO_4130 Purine metabolism, Pyrimidine metabolism, Nucleotide excision repair, Homologous recombination DB00548, DB03152 Azelaic Acid, B-2-Octylglucoside Approved, Experimental 13 MhO_1180 Aminoacyl-tRNA biosynthesis DB07817 1-{3-[(4-pyridin-2-ylpiperazin-1-14yl) sulfonyl]phenyl}-3-(1,3-thiazol-2-yl)urea Experimental 14 MhO_0050 Purine metabolism, Pyrimidine metabo- lism, DNA replication, Mismatch repair, Homologous recombination DB06998 [(5R)-5-(2,3-dibromo-5-ethoxy-4- hydroxybenzyl)-4-oxo-2-thioxo-1,3- thiazolidin-3-yl]acetic acid Experimental 15 MhO_2700 ribosome DB00778, DB01190, DB01211, DB01369, DB01627 Roxithromycin, Clindamycin, Clarithromycin, Quinupristin, Lincomycin Approved 16 MhO_2710 ribosome DB01361 Troleandomycin Approved more proteins is considered as metabolically important active protein, which can act as an appropriate drug target.41,42 Out of 57 input proteins, 12 were found to have less than five inter- actors and 23 to have more than 10 interactors (Table 2). DrugBank database search identified 16 druggable tar- gets. The probability of being druggable of the potential targets can be evaluated by sequence similarity search against the tar- gets from DrugBank.38,39 BLASTp search against DrugBank targets with FDA-approved drugs, nutraceuticals, and experi- mental drugs revealed that 16 targets in the ∑ list are homolo- gous to DrugBank targets (Table 3). Five targets were found to have homologies to FDA-approved drug targets. Among them, MHO_2700  is homologous to a known target (50S ribosomal protein L10) and has five approved drugs against it, named roxithromycin (DB00778), clindamycin (DB01190), clarithromycin (DB01190), quinupristin (DB01211), and lin- comycin (DB01369), which are used to treat Shigella infection. Other four FDA-approved drug targets have one approved drug against each. Proteins in the ∑ list that did not hit with DrugBank database are novel therapeutic targets for which new drug and vaccines can be developed. Ranking of the drug targets suggests that 50S ribosomal protein L10 has the highest potential to be an effective drug target. It is useful to rank the putative therapeutic targets based http://www.la-press.com http://www.la-press.com/drug-target-insights-journal-j23 Identification of therapeutic targets in Mycoplasma hominis 59Drug TargeT InsIghTs 2014:8 Table 4. Rankings of the druggable targets based on drug target score. KEGG ID GENE PRODUCT DEFINITION HOMOLOGS IN DEG INTERACTORS DRUGBANK HITS DRUG TARGET SCORE RANK MhO_2700 50S ribosomal protein L10 7 43 5 15.05 1 MhO_1180 Phenylalanyl-trna synthe- tase subunit beta 26 32 1 8.32 2 MhO_1670 Cytidylate kinase 24 8 4 7.68 3 MhO_2710 50S ribosomal protein L32 15 34 1 5.1 4 MhO_1260 Putative nicotinate-nucleotide adenylyl transferase 19 13 2 4.94 5 MhO_0050 DNA polymerase III subunit beta 19 24 1 4.56 6 MhO_4130 DNA polymerase I 14 13 2 3.64 7 MhO_0980 Thymidylate kinase 21 10 1 2.1 8 MhO_2730 DNA-directed RNA poly- merase subunit alpha 27 2 3 1.62 9 MhO_0670 Fructose-bisphosphate aldolase 12 13 1 1.56 10 MhO_3810 Putative 2, 3-bisphosphoglyc- erate-independent phospho- glycerate mutase 8 9 2 1.44 11 MhO_3880 Ribose-5-phosphate isomerase 5 9 2 0.9 12 MhO_0210 aTP synthase subunit C 2 13 2 0.52 13 MhO_3840 Acetate kinase 6 7 1 0.42 14 MhO_3350 Purine-nucleoside phosphorylase 2 9 2 0.36 15 MhO_0100 Putative Nucleoside Phosphorylase 2 4 2 0.16 16 Note: Drug target score = homologs in Deg × interactors × DrugBank hits/100. on their quantitative values in order to decide which target has a higher probability of being effective in laboratory experiments. We ranked the putative therapeutic targets based on the number of homologs found in DEG, the number of interactors of the target protein, and the number of drugs available. The ranking of the putative drug targets is presented in Table 4 and showed that 50S ribosomal protein L10 has the highest potential to be an effective therapeutic target. Cytidylate kinase, 50S ribosomal protein L32, and putative nicotinate-nucleotide adenylyl trans- ferase also fall in the group of top five putative therapeutic targets. Discussion The increasing trend of bacterial resistance to antibiotics is posing an imminent public health concern. Researchers around the world are giving attention to find new drug targets so that bacterial infections can be prevented. A vast array of omics data and the availability of various computational tools have speeded up the therapeutic target identification process. The potential of a protein to be a therapeutic target depends on two factors, essentiality and absence in the host. Essen- tial proteins are required for bacterial survival and blocking them inhibits bacterial growth. Non-homologous proteins are preferred because they reduce the probability of side effects. In this study, we extracted metabolic data from KEGG data- base and used different bioinformatics and computational databases and tools for the identification of appropriate therapeutic targets of M. hominis. Systematic computational analyses revealed 57 possible drug targets in M. hominis; among them 16 are druggable targets, which have homologs in DrugBank. We further utilized different drug prioritiza- tion parameters to make a short list of potential drug and vac- cine targets. Predicted drug targets belong to a diverse range of cellular activity and fall mostly into ribosome synthesis, pyrimidine metabolism, homologous recombination, DNA replication, and ABC transporter pathways (Fig. 3). The assembly of bacterial ribosomes has been considered prominently as a potential target for antibacterial drugs.43 We identified 16 potential drug targets from the ribosome synthe- sis and assembly pathway. Among them, two are druggable targets that include 50S ribosomal protein L10 and L32. 50S ribosomal protein L10 interacting with acidic L7/L12 pro- teins constitutes the ribosomal stalk. This stalk is involved in the binding of elongation factors EF-Tu and EF-G and plays a crucial role in activating the GTPase center. This target is highly similar to an already available drug target with five FDA-approved drugs used to control Shigella infection. Our http://www.la-press.com http://www.la-press.com/drug-target-insights-journal-j23 Parvege et al 60 Drug TargeT InsIghTs 2014:8 0 5 10 15 20 25 30 Others (24 pathways) Pentose phosphate pathway Protein export Microbial metabolism in diverse… Biosynthesis of amino acids Biosynthesis of secondary metabolites ABC transporters Homologous recombination Ribosome Number of proteins Figure 3. Distribution of putative therapeutic targets in their associated pathways. The percentage distribution of other pathways ranges from 0 to 4.5. study revealed that 50S ribosomal protein L10 holds the high- est promise to be an effective drug target (Table 4). Hence, the potential of it as a drug target remains open for experimental validation. 50S ribosomal protein L32 is a structural constitu- ent of ribosomes. This target was found to have a homologous target in DrugBank for an FDA-approved drug called trole- andomycin. However, M. hominis is intrinsically resistant to this antibiotic because of mutations in 23S rRNA, which has stimulated the search for other drug targets.10 De novo nucleotide synthesis is crucial for the successful growth of bacteria in human blood.44 Nucleotides synthesized in purine and pyrimidine metabolic pathways are important substrates not only for DNA synthesis but also for DNA repair. We have found five and eight drug targets for purine and pyrimidine metabolism, respectively. DNA polymerase I (Pol I) and DNA-directed RNA polymerase subunit alpha (RpoA), which is involved in nucleotide metabolism as well as other important metabolic pathways, have homologs in DrugBank. Azelaic acid, an approved FDA drug, can be used to treat M. hominis infection by inhibiting the activity of Pol I. This drug is currently being used as an inhibitor of Pol I in Escherichia coli. RpoA activity can be blocked by an approved drug, rifabutin, which is highly specific to bacterial RNA polymerase. In the last few years, aminoacyl-tRNA synthetases (AaRS) have drawn much attention as therapeutic targets because of their crucial roles in protein synthesis and conservation across different pathogens.45 Here, we highlighted phenylalanyl-tRNA synthetase subunit beta (PheT) as a therapeutic target, which showed considerable homology with a target in DrugBank database. Phenyl-thiazolylurea-sulfonamide, a successful inhibi- tor of phenylalanyl-tRNA synthetase (Phe-RS),  can be tested in the laboratory to determine its efficacy against M. hominis.46 Glycolysis/gluconeogenesis is perceived as a promising tar- get for new drugs against bacterial pathogens because many of the proteins involved in this pathway are significantly different from human proteins. Here, we identified two proteins of the glycolytic pathway as potential therapeutic targets—one is fruc- tose-bisphosphate aldolase (Fba) and the other is a hypothetical protein, MHO_3810, which is found to have 2,3-bisphospho- glycerate-independent phosphoglycerate mutase (iPGM) activ- ity. Fba catalyses an important reversible reaction required for both glycolysis and gluconeogenesis.47 Previously, this enzyme has been reported as a target of antifungal and antiprotozoal drugs. Downregulation of  iPGM using RNAi resulted in embryonic and larval lethality in Caenorhabditis elegans.48 In addition to druggable targets, we have also identified several other targets that are involved in crucial bacterial meta- bolic pathways. Bacterial protein secretion system pathway modulates biotic association as well as pathogenicity. There- fore, proteins from the bacterial secretion system were identi- fied as drug targets in many bacteria.49,50 This secretion system of M. hominis includes eight proteins. Among them three have been proposed as potential therapeutic targets in our study. The proposed three targets are SecD, SecY, and YidC/Oxa1 fam- ily membrane proteins, and this result is consistent with two previous studies.51,52 Extensive research is going on in the devel- opment of protein secretion inhibitors like salicylidene acylhy- drazides53 and 2-imino-5-arylidene thiazolidinone.54 Earlier studies reported that ABC transporters play an important role in bacterial physiological processes such as the import of impor- tant nutrients required for bacterial growth55 and export of toxic substances outside of the cell.56 Here, we have reported six ABC transporters as novel therapeutic targets. These transporters can be used for the development of antibacterial vaccines.57 Conclusion This study has identified several proteins that can be targeted for effective drug development. Since some of the identified http://www.la-press.com http://www.la-press.com/drug-target-insights-journal-j23 Identification of therapeutic targets in Mycoplasma hominis 61Drug TargeT InsIghTs 2014:8 drug targets play important roles in metabolism, a synchronized approach to develop new drugs would be promising to control M. hominis infection. In addition, results of this study can bring a substantial advancement to test the effectiveness of the cur- rently available drugs. Further wet lab experiments are essential to validate the obtained findings. Author Contributions Conceived and designed the experiments: MSH and MMP. Analyzed the data: MMP and MR. Wrote the first draft of the manuscript: MMP. Contributed to the writing of the manuscript: MR. 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