In silico approaches for the identification of virulence candidates amongst hypothetical proteins of Mycoplasma pneumoniae 309

Computational Biology and Chemistry 59 (2015) 67–80

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Computational Biology and Chemistry journal homepage: www.elsevier.com/locate/compbiolchem

Research article

In silico approaches for the identification of virulence candidates amongst hypothetical proteins of Mycoplasma pneumoniae 309 Mohd. Shahbaaza , Krishna Bisettya , Faizan Ahmadb , Md. Imtaiyaz Hassanb,* a b

Department of Chemistry, Durban University of Technology, Durban 4000, South Africa Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India

A R T I C L E I N F O

A B S T R A C T

Article history: Received 16 January 2015 Received in revised form 8 September 2015 Accepted 14 September 2015 Available online 18 September 2015

Mycoplasma pneumoniae type 2a strain 309 is a simplest known bacterium and is the primary cause of community acquired pneumonia in the children. It mainly causes severe atypical pneumonia as well as several other non-pulmonary manifestations such as neurological, hepatic, hemolytic anemia, cardiac diseases and polyarthritis. The size of M. pneumoniae genome (Accession number: NC_016807.1) is relatively smaller as compared to other bacteria and contains 707 functional proteins, in which 204 are classified as hypothetical proteins (HPs) because of the unavailability of experimentally validated functions. The functions of the HPs were predicted by integrating a variety of protein classification systems, motif discovery tools as well as methods that are based on characteristic features obtained from the protein sequence and metabolic pathways. The probable functions of 83HPs were predicted successfully. The accuracy of the diverse tools used in the adopted pipeline was evaluated on the basis of statistical techniques of Receiver Operating Characteristic (ROC), which indicated the reliability of the functional predictions. Furthermore, the virulent HPs present in the set of 83 functionally annotated proteins were predicted by using the Bioinformatics tools and the conformational behaviours of the proteins with highest virulence scores were studied by using the molecular dynamics (MD) simulations. This study will facilitate in the better understanding of various drug resistance and pathogenesis mechanisms present in the M. pneumoniae and can be utilized in designing of better therapeutic agents. ã 2015 Elsevier Ltd. All rights reserved.

Keywords: Hypothetical proteins Mycoplasma pneumoniae Function predictions Sequence analyses Virulence factors Molecular dynamics Simulations

1. Introduction Mycoplasma pneumoniae is one of the smallest self-replicating bacteria which belong to the family of Mycoplasmataceae. It causes acute respiratory tract infection known as atypical pneumonia, and various types of neurological, cardiac, hepatic and hemolytic manifestations (Razin et al., 1998). M. pneumoniae infection occurs worldwide in an endemic fashion and is occasionally epidemic. It accounts for more than 40% of community acquired pneumonia cases in children, and 18% of cases require hospitalization (Ferwerda et al., 2001). The M. pneumoniae also causes several extra-pulmonary infections and the central nervous system (CNS) manifestations like meningoencephalitis, encephalitis, polyradiculitis and aseptic meningitis, which are more common among patients suffering from pneumonia caused by M. pneumoniae (Leonardi et al., 2005). It also induces several chronic disease

* Corresponding author. E-mail address: [email protected] (M. I. Hassan). http://dx.doi.org/10.1016/j.compbiolchem.2015.09.007 1476-9271/ ã 2015 Elsevier Ltd. All rights reserved.

conditions in which the clearance of organism from host is a very complex process (Waites et al., 2008). Infection of M. pneumoniae causes various chronic diseases such as juvenile idiopathic arthritis, rheumatoid arthritis, asthma and Crohn’s disease (Waites et al., 2008). M. pneumoniae is an obligate surface pathogen which develop adherence to mucosal epithelium of respiratory and urogenital tracts of the host by using varieties of specialized surface proteins (Hu et al., 1977). The key adherence proteins present in the M. pneumoniae are P1 adhesins (Hu et al., 1977) which are the primary cause of its virulence (Layh-Schmitt and Herrmann, 1994). These adhesins are also significant in defending the parasite from mucociliary clearance (Krause, 1996). The current study aimed at the annotations of uncharacterized adhesins and virulence causing proteins in the genome of M. pneumoniae. The genome of M. pneumoniae contains 8,17,176 base pairs that forms 750 genes, 36 tRNAs genes, 4 non-coding RNA genes, and 1 rRNA operon (Kenri et al., 2012). These genes are translated into 707 proteins (Kenri et al., 2012) which are involved in varieties of functions. Around 204 proteins in the respective set are listed as “Hypothetical Proteins (HPs)” as no biochemical functions were

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assigned to them (Galperin and Koonin, 2004; Hawkins and Kihara, 2007). In most of the sequenced genomes, approximately half of the proteins are uncharacterized, and the knowledge of their biological functions will be helpful in completing the available genomic and proteomic information present in the databases (Loewenstein et al., 2009; Nimrod et al., 2008). To complete this fragmentary knowledge, the precise annotations of these HPs are necessary as it may lead to the discovery of new structures with new functions, which will be helpful in bringing out a list of undiscovered protein pathways present in the organisms (Nimrod et al., 2008). This is an active area of research for which we are constantly looking for novel drug targets in different pathogens (Hassan et al., 2007a,b; Kumar et al., 2015a, 2014; Thakur and Hassan, 2011; Thakur et al., 2013, 2014). Moreover, during the course of drug design, discovery and screening of novel therapeutic agents, HPs may also serve as markers and pharmacological targets (da Fonseca et al., 2012; Lubec et al., 2005; Minion et al., 2004). In this study, varieties of bioinformatics tools were utilized for the robust function predictions of the HPs through sequence analyses and comparisons (Shahbaaz et al., 2015b). The sequencebased search tools such as BLAST (Altschul et al., 1990), FASTA (Pearson and Lipman, 1988) and HMMER (Finn et al., 2011) are commonly used for functional analysis of gene products by the recognition of related homologues in various biological databases (Kumar et al., 2015b; Naqvi et al., 2015a,b; Shahbaaz et al., 2015a, 2013; Sinha et al., 2015). Similarly, the functional domains

characterization and motif analyses were also considered as the basis to identify the role of HPs in the biological processes. In the case of low sequence identities (<30%), detection of the conserved motifs and domains in protein sequences may provide a significant clues for the functional classification of HPs (Rost and Valencia, 1996). Therefore, domain and motif annotation tools such as Pfam (Bateman et al., 2002), CDART (Geer et al., 2002), SYSTERS (Krause et al., 2000), SBASE (Vlahovicek et al., 2003) and InterPro (Hunter et al., 2012) were explored for investigating functional sites in the sequences of the HPs. In a well-organized metabolic network, study of the protein interactions is crucial to comprehend the functional roles of proteins and can be studied by utilizing the tools such as STITCH (Kuhn et al., 2008) and STRING database (Szklarczyk et al., 2015). Furthermore, the putative virulence factors were identified and analyzed in the group of 204HPs by utilizing machine learning based tools and databases such as VirulentPred (Garg and Gupta, 2008), VFDB (Chen et al., 2005), DBETH database (Chakraborty et al., 2012), BTXpred (Saha and Raghava, 2007), Comprehensive Antibiotic Resistance (CARD) Database (McArthur et al., 2013) and AlgPred (Saha and Raghava, 2006) servers. Moreover, the accuracy of the prediction methods were assessed by utilizing a set of proteins with known functions (Tables S6 and S7) in Receiver Operating Characteristic (ROC) analyses (Metz, 1978), which confirmed the reliability of the predictions. The functions of 83HPs were predicted successfully. In this set of annotated proteins, the putative virulence factors were

Fig. 1. The computational workflow of the adopted pipeline used for annotating function of 204 HPs from M. pneumoniae. The protocol is divided into three phases, namely 1, 2 and 3. In first phase we have searched HPs using keyword “Hypothetical protein” and then Uniprot and other database were searched using the “Gene ID” and proteins showing the similar results were considered as “Redundant”. In the second phase, prediction of various properties such as physicochemical parameters, sub-cellular localization, function annotation, virulence and interaction pathway analysis were performed. Finally, in the third phase integration of various predicted results to predict the function of HP (Shahbaaz et al., 2013). There is also the selection of HPs with highest virulence score and then they were subjected to MD simulation to identify their conformational behaviour.

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classified. The HPs with highest virulence scores were subjected to MD simulations in order to understand their conformational behaviour in explicit water conditions. 2. Materials and methods The computational framework for the functional annotations of HPs was established by using the knowledge obtained from previous in silico studies (Mazandu and Mulder, 2012; Shahbaaz et al., 2015c,d, 2013). The elementary steps of the adopted pipeline were illustrated in Fig. 1. 2.1. Primary structure analyses The genome of M. pneumoniae contains 707 functional proteins and by utilizing the keyword searching in the NCBI database; around 204 proteins were classified as HPs (http://www.ncbi.nlm. nih.gov/genome/). The Uniprot IDs and amino acid sequences of each HP were retrieved by using the available ID mapping tools (http://www.uniprot.org/). In order to observe all the putative ORFs (Open Reading Frames) present in the hypothetical genes; we performed the six frame ORFs translation on the genomic sequence of M. pneumoniae. The minimum size of ORFs for putative encoding sequence was set to 102 nucleotides. The OrfPredictor (Min et al., 2005) analyze the genomic DNA and predicted the presence of 1224 ORFs in the hypothetical genes of M. pneumoniae. The OrfPredictor also predicted the Coding Sequence (CDS) and then translated to the respective proteins; which were compared with the deposited sequences of the HPs. Furthermore; the existence of 20HPs has been experimentally validated; while rest of the HPs were predicted at the biochemical level (illustrated in Table S1). On the basis of these analyses; we considered all the 204HPs for further analyses. The physiochemical properties such as isoelectric point, molecular weight, instability index, extinction coefficient, grand average of hydropathicity (GRAVY) and aliphatic index were predicted by using ProtParam server (http://web.expasy.org/ protparam/) (listed in Table S1). The membrane proteins are considered as possible vaccine targets, while the proteins localized in the cytoplasm are the potent drug targets (Vetrivel et al., 2011). In the absence of any experimental information about the HPs localizations, the varieties of sub-cellular localization prediction tools such as PSLpred (Bhasin et al., 2005), PSORTb (Yu et al., 2010) and LocTree3 (Goldberg et al., 2012) can be used. The LocTree3 uses hierarchical systems of SVM (Support Vector Machines) in which predictions are made via searching k-consecutive residues in the proteins with experimentally validated cellular localizations (Goldberg et al., 2012). This server also include the functionality of PSI-BLAST that identify the protein localization by searching the homologs (Goldberg et al., 2012). Additionally, LocTree2 module is used in the absence of significant PSI-BLAST hits in the databases (Goldberg et al., 2012). Similarly, the PSLpred predicts the subcellular localization of proteins in the bacteria cells by using the SVM algorithms. The SignalP 4.1 (Emanuelsson et al., 2007) and SecretomeP (Bendtsen et al., 2005) servers were used to identify signal peptides in HPs and their involvement in non-classical secretory pathways respectively. Furthermore, TMHMM (Krogh et al., 2001) and HMMTOP (Tusnady and Simon, 2001) servers were used in order to identify the trans-membrane helices in HPs. The predictions of sub-cellular localizations of 204HPs are listed in Table S2. 2.2. Functional analyses The primary step for functional annotations of proteins involved sequence similarity searches in diverse biological

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databases. The sequence similarity search methods such as BLAST (Altschul et al., 1990), FASTA (Pearson and Lipman, 1988) and HMMER (Finn et al., 2011) were used to identify functional homologues in the databases. In the BLAST searches, the HPs with low sequence identities (<20%) as well as low query coverage (<50%) were excluded. The proteins showed low sequence identities (<26%) were considered as remote homologues, while those with high sequence identities (>40%) were referred to as close homologues. In a search, if the desirable magnitudes of the respective parameters were obtained, then it is considered as the possible hit for a particular HP. We assumed the probable function to an HP, if the respective functionality appeared maximum number of times with e-value (<0.005). This procedure was adopted for every HP in order to obtain the reliable results in similarity search. Similarly, if FASTA search showed e-value (< 0.005) then it was considered as a reliable hit (Table S3). The HMMER uses pair-wise comparisons of profile HMMs for the detection of remote homologs in protein databases such as Pfam (Bateman et al., 2002), PDB (Bernstein et al., 1977; Bernstein et al., 1978) and INTERPRO. Furthermore, functional domains present in HPs were predicted by using various online databases such as Pfam (Finn et al., 2014), SUPERFAMILY (Gough et al., 2001), CATH (Orengo et al., 1997), SYSTERS (Meinel et al., 2005), CDART (Geer et al., 2002), SMART (Letunic et al., 2012) and SBASE (Vlahovicek et al., 2003) (Table S4). The SYSTERS is the collection of diverse clusters of protein families and functions of the HPs were predicted by placing them in a suitable cluster by using database searching tools such as BLAST. The SBASE server is an online compilation of protein domains and related prediction tools devised to ease the detection of functional homologues by using regular database search. After the evaluation of the outputs produced by BLAST searches and knowledge of biologically important similarities obtained from identified domain groups, the server performed the functional predictions and domain identifications. Likewise, SMART and CDART servers were used to perform the sequence similarity searches based on domain architecture and profiles. The SMART (Simple modular architecture research tool) server, searches the related domains in SP-TrEMBL (Bairoch and Apweiler, 2000), Swiss-Prot (Gasteiger et al., 2001) and stable ensemble (Hubbard et al., 2002) proteomes. Furthermore, the InterPro, a consortium of several integrated databases such as Pfam, SMART, SUPERFAMILY and CATH, was used to perform the motif discovery. The GPCRs (G-protein coupled receptors), being one of the leading super-families of membrane proteins which are considered as significant drug targets. The SVM based servers such as GPCRpred (Bhasin and Raghava, 2004) and the GPCRs class (Bhasin and Raghava, 2005) were used for predicting GPCRs’ families and subfamilies on the basis of the dipeptide composition of HPs. Since, in a biochemical network, the interacting proteins often modulate the activity and function of a protein. Therefore, knowledge about the behaviour of protein–protein interactions is considered as important information for predicting the function of the protein precisely. The STITCH (Kuhn et al., 2008) and STRING databases (version 9.05) (Szklarczyk et al., 2011) were used to predict functional partners of HPs in a biological network. The interaction data regarding experimental or co-expression, indirect (functional) and direct (physical) associations, are quantitatively integrated in STRING database for a large number of organisms, and the information between these organisms are transferred wherever applicable for the prediction of interacting proteins. Whereas, STITCH integrates data present in various database and literature about drug-target relationships, biological pathways and binding affinities in order to predict the interacting chemical and protein partners. Similarly, iPfam which is the collection of data regarding the interactions of 3D structures of protein families and

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domains submitted in the Pfam provided the insight about the interacting partner in the metabolic network. 2.3. Annotations of virulence factors The virulence factors (VFs) are essential for severity of infection caused by the pathogen (Baron and Coombes, 2007), therefore, they are considered as potential targets in the drug discovery. A variety of uncharacterized VFs were classified by using VirulentPred server, that uses the SVM based methods in order to predict the putative bacterial VFs with an accuracy of 81.8%. Similarly, BTXpred and AlgPred servers were used for the prediction of the bacterial toxins and potential allergens respectively. The predictions of the BTXpred were complimented by the results obtained from DBETH server that identifies the possible exotoxins for the human beings. Furthermore, the VF-related proteins were identified in the VFDB (virulence factor database) by using BLAST database searches. Moreover, Comprehensive Antibiotic Resistance Database (CARD) (McArthur et al., 2013) which is a collection of the antibiotic resistance genes, was searched for the identification of an antibiotic resistance protein in this set of uncharacterized proteins (Table S5). 2.4. Molecular modelling The HPs with the highest predicted virulence scores were selected for further structural analyses. The templates for the selected HPs were identified by using BLAST module of Discovery Studio (DS) (Accelrys, 2013). In case of low sequence homology (<25%), the 3-D structures were predicted by using the ab initio algorithms of the I-TASSER (Roy et al., 2010). The evaluations of the predicted models were performed by using TM scores (Zhang and Skolnick, 2005) and RMSD (root mean square deviations) values, calculated by the ITASSER. The best evaluated models were

selected for the energy minimization and optimization processes performed by using the refinement modules of DS. The modelled structures of virulent HPs were simulated by using GROMACS package (Van Der Spoel et al., 2005) (version 4.6.5) in explicit solvent conditions at 300 K. In order to improve the electrostatic interactions, the virulent HPs were solvated in the Single Point Charge (SPC) water model and simulated by using the Particle– Mesh–Ewald (PME) summation under Periodic Boundary Conditions (PBC). An initial structure of the protein was energetically minimized with a convergence criterion of 0.005 kcal mol within 2000 steps of steepest descent algorithms. The minimized structures were equilibrated for the time scale of 200 ps by using NVT and NPT ensemble conditions. The MD simulations were performed for 20 ns time scale by using the LINCS algorithm of GROMACS with a time step of 2 fs. All the resulting trajectories of the MD simulation were analyzed using the utilities present in the GROMACS package. 3. Results and discussion By using the available Bioinformatics tools, the sequences of 204HPs were extensively analyzed (Tables S3 and S4). The functions of 83HPs were successfully predicted that are listed in Tables 1–3. These functionally characterized HPs were further classified into 23 enzymes, 12 transport proteins, 27 lipoproteins, 10 binding proteins and the rest were involved in varieties of cellular process such as replication, transcription and translation (Fig. 2). The predictive accuracy of the adopted pipeline used for functional annotations of the HPs, was assessed by using the statistical methods of ROC. On the basis of this method the average accuracy was calculated to be 96% and average area under the ROC curve was found to be 0.704, which indicates the reliability of the predictions. Furthermore, the p-value for functionality assignment was set to 0.05, as we are assuming the probability of random

Table 1 List of predicted enzymes among the group of HPs from the genome of M. pneumoniae. S. No.

Uniprot ID

Function

Oxidoreductase 1.

H0PRG9

Peroxiredoxin, OsmC-like protein

Transferase 2. 3. 4. 5. 6. 7. 8.

H0PPK9 H0PPQ4 H0PPU3 H0PPU7 H0PQ82 H0PQ93 H0PQU2

GNAT family acetyltransferase 16S rRNA methyltransferase (regulation of transcription, DNA-templated) Adenine-specific DNA methyltransferase (nucleic acid binding) Site-specific DNA-methyltransferase DNA integrity-scanning diadenylate cyclase 2-C-Methyl-D-erythritol 4-phosphate cytidylyltransferase DNA polymerase III, delta subunit

Hydrolase 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

H0PPU4 H0PPU5 H0PPU6 H0PPW2 H0PQ39 H0PQA2 H0PQA7 H0PQC8 H0PQI2 H0PQI8 H0PQK3 H0PRB5

Type III restriction endonuclease Type III restriction endonuclease Type II restriction endonuclease Phosphodiesterase (phosphatase) Restriction endonuclease, S subunit Cof-like hydrolase Ribonuclease Y Type I restriction modification DNA specificity domain protein Type I restriction endonuclease subunit S Serine/threonine protein phosphatase Type I restriction modification DNA specificity domain protein Type I restriction endonuclease subunit S

Lyase 21.

H0PRH4

2C-Methyl-D-erythritol 2,4-cyclodiphosphate synthase

Kinase 22. 23.

H0PRD4 H0PRD5

Uridylate kinase Uridylate kinase

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Table 2 List of HPs involved in the transport mechanisms from the genome of M. pneumoniae. S. No.

Uniprot ID

Function

Transporter 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

H0PPR0 H0PQ52 H0PQA6 H0PQH2 H0PQH4 H0PQH8 H0PQN7 H0PQU0 H0PR04 H0PR05 H0PRG2

MFS family transporter ABC transporter permease PTS system glucose-specific EIICBA component -like domain (kinase activity) ABC exporter, permease subunit ABC exporter, permease subunit RDD family protein SecD like protein export protein Substrate-specific component FolT of folate ECF transporter Type IV secretory pathway VirD4 components-like protein Type IV secretory pathway VirD4 components-like protein Na-driven efflux pump

Carrier 12.

H0PQU7

CtaD, partial (electron transport)

function predictions to be 0.05. The proteins similar to these functionally annotated HPs in other strains of M. pneumoniae were identified by using sequence comparisons (listed in Table 4). We are now developing an online server on the basis of above explained pipeline, which will provide an interface for the functional predictions of uncharacterized proteins by utilizing all the discussed methods. The functional analyses through the adopted pipeline enabled us to understand the advantages and disadvantage of the available methods. Currently, we are searching for the suitable algorithms that combine the used Bioinformatics tools in the most efficient way. Once the algorithms are established, an online server will be available for other researchers. On the basis of predicted functionalities, we have considered that these HPs may play a significant role in the pathogenesis and survival of the M. pneumoniae, and several identified functional groups are described here separately.

mitochondrial peroxiredoxins from Trypanosoma cruzi act as antioxidants due to their peroxynitrite reductase and peroxidase activities, play an essential role in the pathogenesis and act as a drug target for the management of Chagas disease (Pineyro et al., 2008).

Mycoplasma does not contain any characteristic virulence factors such as toxins, invasins, and cytolysins (Pilo et al., 2005). A membrane positioned enzyme, L-a-glycerophosphate oxidase (GlpO) releases toxic by-products such as H2O2 which is formed by the metabolism of glycerol, are considered to be one of the primary cause of its pathogenesis (Pilo et al., 2005). Similarly, glycerophosphodiesterases such as GlpQ (MPN420) found in the Mycoplasma species also plays an important role in cell cytotoxicity by utilizing phosphatidylcholine present on the lung epithelia through the breakdown of deacylated phospholipids to choline and glycerol-3-phosphate (Schmidl et al., 2011). Therefore, enzymes produced by such pathogenic bacteria are considered to play a significant role in the survival and pathogenesis through modifications of local environment inside the host for the favourable growth (Spirig et al., 2011). There were 23HPs predicted to be enzymes in the group of 83 functionally annotated proteins. The categorized classes of enzymes are explained here in details.

3.1.2. Transferase Transferase enzymes are assumed to be important for biosynthesis of bacterial lipoprotein (Okugawa et al.. 2011). The lipoproteins play an essential role in virulence mechanism of pathogenic bacteria (Okugawa et al.. 2011). The HP H0PPK9 was predicted to be a Gcn5-related N-acetyltransferases (GNAT) family protein and may catalyzes the biochemical process of the acetyl group translocation from the “donor” acetyl coenzyme A to an “acceptor” primary amine (Vetting et al., 2005). While the HP H0PPQ4 was classified as a 16S rRNA methyltransferase and may be involve in the development of resistance against varieties of antimicrobial agents which inhibit the activities of proteins' synthesis in bacteria by irreversibly binding to the 16S ribosomal subunits present in bacterial cells (Hopkins et al., 2010). The HPs H0PPU3 and H0PPU7 were predicted to be DNA methyltransferases. This respective group of enzymes are essential for the bacterial viability as well as play an important role in its pathogenesis by regulating the synthesis of virulence factors (Heithoff et al., 1999). Furthermore, HP H0PQ82 was predicted to be a diadenylate cyclase, which may be involved in the conversion of ATP or ADP into cyclic diadenosine monophosphate (c-di-AMP). The c-di-AMP is recently identified as a signalling molecule and functioned as a ubiquitous second messenger in the regulation of signal transductions in bacteria (Bai et al., 2012). The HP H0PQ93 was annotated as 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase and was considered to be involved in the biosynthesis of isoprenoids (Obiol-Pardo et al., 2010). Moreover, HP H0PQU2 was predicted as delta subunit of DNA polymerase III (Table 1) and may be important for completing the process of DNA replication by serving as a sliding clamp unloader (Leu et al., 2000).

3.1.1. Oxidoreductase The HP H0PRG9 was found to be a peroxiredoxin, OsmC-like protein (Table 1). It may function as bacterial antioxidant and may be involved in detoxification of endogenously derived oxidative radicals during the interactions of pathogen with host (Saikolappan et al., 2011). OsmCs present in the bacteria such as M. tuberculosis and M. smegmatis are able to reduce peroxides like cumene hydroperoxide (CHP), hydrogen peroxide (H2O2) and tbutyl hydroperoxide (t-BHP) (Saikolappan et al., 2011). Therefore, this predicted OsmC may be capable of equally reducing organic and inorganic peroxides in the host. Similarly, the cytosolic and

3.1.3. Hydrolase The hydrolase enzymes play an important role in varieties of virulence mechanisms present in pathogenic bacteria. The hydrolases such as b-lactamase, which are crucial for the development of bacterial resistance against b-Lactam antibiotics such as penicillin, cephalosporins, carbapenems and monobactams (Poole, 2004). Similarly, the Kdo and nudix hydrolases which are involved in the pathogenesis in a variety of pathogens (Shahbaaz et al., 2013). HPs H0PPU4, H0PPU5 and H0PPU6 were annotated to be type III restriction endonuclease enzymes. The Type III restriction enzyme is a complex that contains two subunits, in which

3.1. Enzymes

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M. Shahbaaz et al. / Computational Biology and Chemistry 59 (2015) 67–80 Table 3 List of HPs involved in various cellular processes from the genome of M. pneumoniae. S. No.

Uniprot ID

Function

H0PQ41 H0PQL0 H0PR91

Adhesin P1 Adhesin P1 Adhesin P1

H0PPU2 H0PQB4 H0PQR6 H0PR43 H0PR46

Type II restriction-modification system modification subunit DNA-binding protein, YbaB/EbfC family Putative helix-turn-helix protein, YlxM (DNA binding) Recombinase Single-stranded DNA binding protein

H0PQW3 H0PRG5

Fatty acid-binding protein, DegV family Fatty acid-binding protein, DegV family

H0PPL2 H0PPL4 H0PQ77 H0PQ79 H0PQW9 H0PRC6

Transcription termination/antitermination protein NusB DJ-1/PfpI family protein GntR family transcriptional regulator Sporulation transcription regulator WhiA Transcriptional regulator TACO1-like LuxR regulatory protein

H0PPR6 H0PPR7 H0PQF5

Peptidase S7 family-like protein Peptidase S7 family-like protein Cell division ATPase

H0PPJ3 H0PPJ4 H0PPN7 H0PPY6 H0PQ00 H0PQ71 H0PQA9 H0PQB0 H0PQC1 H0PQK8 H0PQQ3 H0PQV7 H0PQV8 H0PQZ6 H0PRD9 H0PRE0 H0PRE1 H0PRE2 H0PRE3 H0PRE4 H0PRE5 H0PRE6 H0PRE7 H0PRE8 H0PRF0 H0PRF1 H0PRF5

Mycoplasma specific lipoprotein, Type 3 Mycoplasma specific lipoprotein, Type 3 Lipoprotein 3 family-like protein Lipoprotein 3 family-like protein Lipoprotein Lipoprotein Lipoprotein 3 family-like protein Lipoprotein 3 family-like protein Lipoprotein 10 family-like protein Lipoprotein 3 family-like protein Lipoprotein 3 family-like protein Lipoprotein 3 family-like protein Lipoprotein 3 family-like protein Lipoprotein 3 family-like protein Lipoprotein Lipoprotein Lipoprotein Lipoprotein Lipoprotein Lipoprotein Lipoprotein Lipoprotein Lipoprotein Lipoprotein Lipoprotein 3 family-like protein Lipoprotein 3 family-like protein Lipoprotein 3 family-like protein

H0PQD6 H0PR47

DivIVA domain-containing protein Trigger factor-like domain

Cytadherence

DNA binding proteins

Fatty acid binding proteins

Transcriptional regulator protein

ATP binding

Lipoprotein

Cell cycle and related proteins

methyltransferase subunit modifies the recognition site by using Sadenosyl-L-methionine (SAM) as a methyl donor, while adenosine50 -triphosphate (ATP)-dependent restriction activity requires both the subunits (Dryden et al., 2001). The HP H0PPW2 was found to be a phosphodiesterase enzyme, may be involve in the catalytic breakdown of 3,50 -adenosine cyclic monophosphate (cAMP) and may be considered as critically significant component for the cAMP protein kinase A (PKA) signalling pathway (Dousa, 1999). Furthermore, modulation of the protein activities by kinases and phosphatases through the phosphorylation/dephosphorylation

processes are considered as the fundamental steps in the regulation of cellular processes (Chopra et al., 2003). This indicates importance of HP H0PQI8 which was predicted to be a serine/threonine protein phosphatase (Table 1). The HPs H0PQ39, H0PQC8, H0PQI2, H0PQK3 and H0PRB5 were annotated as type I restriction endonucleases and may be considered as a component of the bacterial DNA restrictionmodification mechanisms that defend the pathogen against invading foreign DNA molecules by performing endonucleolytic cleavage at a particular positions (Sistla and Rao, 2004). Moreover, RNase Y is a novel endoribonuclease that plays a significant role in the initiation

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Fig. 2. The graphical representation of the various functional groups identified in the set of 204HPs from the genome of M. pneumoniae using information obtained from various bioinformatics tools for the functional annotation. The pie diagram shows that there are 24% enzymes, 14% proteins involved in transport processes and 58% involved in various cellular processes. The HPs belonging to these groups are further classified into various subclasses to understand their functionality more precisely.

of mRNA decay, which affect the stability of mRNA in Gram-positive organisms like Bacillus subtilis (Laalami et al., 2013). The HP H0PQA7 was predicted to possess the ribonuclease Y activity.

formation of UDP by reversible phosphorylation of UMP (MullerDieckmann and Schulz, 1994). 3.2. Transport protein

3.1.4. Lyase The HP H0PRH4 was successfully categorized as a lyase enzyme and prediction showed that it may possess the 2C-methyl-Derythritol 2,4-cyclodiphosphate synthase activity (Table 1). This category of enzyme take part in isoprenoid biosynthesis by catalyzing the breakdown of 2-phospho-4-(cytidine 50 -diphospho)-2-C-methyl-D-erythritol into 2-C-methyl-D-erythritol 2,4cyclodiphosphate and CMP (Herz et al., 2000). The biosynthesis of isoprenoids occurs through the mevalonate-independent ormevalonate pathway by the catalysis of isopentenyl diphosphate and the isomeric dimethylallyl diphosphate as substrates (Herz et al., 2000). 3.1.5. Kinase The kinase enzymes have established roles in the varieties of biochemical processes such as carbon metabolism, signal transductions and growth in the Gram positive bacteria like M. pneumoniae (Merzbacher et al., 2004). The Ndk (nucleoside diphosphate kinase) is an essential enzyme for the proper functioning of the bacteria. They catalyzes the transfer of terminal phosphate group from an NTPs such as ATP or GTP to any nucleoside diphosphate and leads to the generation of nucleoside triphosphates (Chakrabarty, 1998). Therefore, this enzyme plays an essential role in the bacterial pathogenicity, cell surface polysaccharide synthesis, growth processes and signal transductions (Chakrabarty, 1998). The uridylate kinase activities were observed in the HPs H0PRD4 and H0PRD5 (Table 1). These proteins may be involved in pyrimidine metabolism pathway and catalyze the

The proteins involved in the transport mechanisms play a crucial role in the cellular processes such as metabolism, virulence and intracellular survival of pathogen (Freeman et al., 2013). The HP H0PPR0 was predicted to be a member of major facilitator superfamily (MFS) (Table 2). MFS is classified into 17 distinct families which are generally involve in the transportation of compounds such as sugars, drugs, amino acids, inositols, nucleosides, Krebs cycle metabolites and organophosphate esters (Pao et al., 1998). The HPs H0PQ52, H0PQH2 and H0PQH4 showed similarities with ABC transporter proteins. These predicted ABC transporters may be involved in the virulence causing mechanisms of the bacteria because ABC transporters are coupled with the uptake of metal ions, and also facilitate the attachment of the bacteria to the surface of the mucosal cells (Garmory and Titball, 2004). The HP H0PQA6 was found to be similar to a PTS system glucose-specific EIICBA component-like domain, which indicated that this HP can be used as a drug target as M. pneumoniae utilize the fructose, glucose, and glycerol as the only carbon sources to sustain its propagation (Halbedel et al., 2004). Translocation of the proteins through the discharge of effector proteins to the extracellular environment, expression of virulence factors on the cell surface, organelle biogenesis and nutrient acquirement, are essential for the bacterial pathogenesis (Feltcher et al., 2010). The type IV secretion systems are involved in delivering the bacterial toxins directly into targeted portions in the host cells as well as in the biofilm formation and the horizontal spreading of antibiotic resistance genes (Zechner et al., 2012). The HPs H0PR04 and H0PR05 were predicted to be the components of

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Table 4 List of proteins belonging to different strains showing similarity to functionally annotated HPs of M. pneumoniae. S. No.

309

M129/ ATCC 29342

FH

M129-B7

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76.

H0PPJ3 H0PPJ4 H0PPK9 H0PPL2 H0PPL4 H0PPN7 H0PPQ4 H0PPR0 H0PPR6 H0PPR7 H0PPU2 H0PPU3 H0PPU4 H0PPU5 H0PPU6 H0PPU7 H0PPW2 H0PPY6 H0PQ00 H0PQ39 H0PQ41 H0PQ52 H0PQ71 H0PQ77 H0PQ79 H0PQ82 H0PQ93 H0PQA2 H0PQA6 H0PQA7 H0PQA9 H0PQB0 H0PQB4 H0PQC1 H0PQC8 H0PQD6 H0PQF5 H0PQH2 H0PQH4 H0PQH8 H0PQI2 H0PQI8 H0PQK3 H0PQK8 H0PQL0 H0PQN7 H0PQQ3 H0PQR6 H0PQU0 H0PQU2 H0PQU7 H0PQV7 H0PQV8 H0PQW3 H0PQW9 H0PQZ6 H0PR04 H0PR05 H0PR43 H0PR46 H0PR47 H0PR91 H0PRB5 H0PRC6 H0PRD4 H0PRD5 H0PRD9 H0PRE0 H0PRE1 H0PRE2 H0PRE3 H0PRE4 H0PRE5 H0PRE6 H0PRE7 H0PRE8

P75102 P75101 P75087 P75084 P75082 P75060 P75046 P75040 P75610 P75609 P75562 P75561 P75452 P75452 – P75451 P75349 P75138 P75583 Q50287 Q50284 P75555 P75538 P75532 P75530 P75528 P75519 P75511 P75507 P75506 P75505 P75505 P75502 P75495 P75488 P75481 P75465 P75445 P75443 P75439 P75435 P75429 P75416 P75412 P75411 P75387 P75373 P75363 Q50364 Q50362 P75328 P75317 P75316 P75312 P75306 P75281 P75273 P75272 P75227 P75224 P75223 Q50334 P75180 P75169 P75164 P75162 P75158 P47678 P75157 P75156 P75155 P75154 P75153 P75152 P75151 P75150

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

M. Shahbaaz et al. / Computational Biology and Chemistry 59 (2015) 67–80

75

Table 4 (Continued) S. No.

309

M129/ ATCC 29342

FH

M129-B7

77. 78. 79. 80. 81. 82. 83.

H0PRF0 H0PRF1 H0PRF5 H0PRG2 H0PRG5 H0PRG9 H0PRH4

P75148 P75147 P75137 P75130 P75127 P75123 P75118

– – – – – – –

– – – – – – –

Strains of Mycoplasma pneumoniae (The functionally annotated HPs are shown with purple background)

Type IV secretory pathways, therefore, may have the some clinical significance (Table 2). Furthermore, HP H0PQU7 was annotated as subunit I of cytochrome c oxidase (CtaD) and may be involved in the catalytic reduction of oxygen to water in the respiratory chains (Cooper et al., 1991). 3.3. Binding proteins There were 10HPs predicted to be involved in the binding processes with a variety of bio-molecules such as DNA, fatty acids and ATP (Table 3). The HPs H0PPU2, H0PR46 and H0PQB4 were classified as DNA binding proteins because they were predicted to be Type II restriction-modification system modification subunit, single-stranded DNA binding protein and YbaB/EbfC family protein respectively. Similarly, the HP H0PQR6 showed the similarities to the proteins containing helix-turn-helix. This common substructure found in a diverse transcription factors comprising of open trihelical bundle and binds to the DNA with the 3rd helix (Aravind et al., 2005). This functional domain of helix-turn-helix is involved in processes such as DNA repair, replication and regulation of transcription (Aravind et al., 2005). Moreover, the HP H0PR43 was predicted to be a recombinase, an enzyme involved in the process of genetic recombination (Guo et al., 1997). The lipids are crucial components for numerous biological mechanisms and play a vital role in the pathogenesis of several widespread metabolic diseases (Furuhashi and Hotamisligil, 2008). Therefore, the HPs H0PQW3 and H0PRG5 may be important for the pathogenesis as they showed the presence of fatty acid-binding activities (Table 3). Furthermore, the HPs H0PPR6, H0PPR7 and H0PQF5 were predicted to be involved in ATP binding processes (Table 3). The ATP binding proteins are known to be involved in the bacterial virulence as well as considered as the targets in antibacterial drug discovery and design (Garmory and Titball, 2004). 3.4. Transcriptional regulators The transcriptional factors can control varieties of biological functions in numerous pathogenic bacteria and are essential for virulence of these organisms (Haque et al., 2009). The HP H0PPL2 was found to be a transcription termination/anti-termination

protein, NusB. The NusB along with NusA is necessary for the N activities that discontinue the rho-dependent and rho-independent terminations (Altieri et al., 2000). The sporulation in the Streptomyces coelicolor, a filamentous bacterium, is a strictly controlled process which involve the chromosome segregation, aerial hyphae growth, spore maturation and septation (Kaiser and Stoddard, 2011). The WhiA regulates the transcription process of sporulation and contains two DNA binding regions (Kaiser and Stoddard, 2011). HP H0PQ79 was predicted to be a sporulation transcription regulator WhiA (Table 3). Similarly, the HP H0PRC6 was annotated as a LuxR transcriptional regulator and may be pivotal for Quorum Sensing as well as in controlling the expression of diverse genes that encodes virulence factors (Chen and Xie, 2011). Furthermore, the HPs H0PPL4, H0PQ77 and H0PQW9 showed similarities to DJ-1/PfpI family protein, GntR family transcriptional regulator and TACO1-like transcriptional regulator respectively (Table 3). The GntR family proteins are considered to be important as they include GntR (gluconate operon) repressor of the Bacillus subtilis as well as the FadR repressor that is involved in fatty acid degradation in E. coli and so on (Haydon and Guest, 1991). 3.5. Lipoproteins Lipoproteins present in the pathogenic bacteria are produced by lipid modifications of the proteins, which ease their attachment to the hydrophobic surfaces through the interactions of the connected acyl groups to phospholipids of the cell wall. These processes are considered important for many virulence and cellular phenomena. There were 27 predicted lipoproteins in the group of 83 functionally annotated HPs from the genome of M. pneumoniae (Table 3). Mycoplasma lipoproteins perform the modulation of the host's immune system for the its favourable growth (Zuo et al., 2009). The lipoproteins show a vast diversity of functions and are found to trigger the activation of macrophages, which discharges interleukin-6 (IL-6) and amplify the production of cell-coupled interleukin-1 (IL-1) (Zuo et al., 2009). The Mycoplasma lipoproteins are considered as a potent cytokine inducers for macrophages along with the cytolytic activity (Zuo et al., 2009). Lipoproteins also induce the activation of the host complement systems on affected cells as well as the maturation of

Table 5 The HPs with highest virulence score among the group of 83 functionally annotated proteins. S. No.

UNIPROT ID

VirulentPred score

Toxin protein (DBETH server)

BTXpred Server: Prediction of Bacterial Toxins

CARD server (antibiotic resistance)

Algpred server

1. 2. 3. 4. 5. 6.

H0PQ93 H0PQA7 H0PQC8 H0PQI2 H0PQI8 H0PQR6

(+) (+) (+) (+) (+) (+)

(+) (+) (+) (+) (+) (+)

(+) (+) (+) (+) (+) (+)

(+) (+) (+) (+) (+) (+)

(+) (+) (+) (+) (+) (+)

0.4052 1.1311 0.9060 1.0961 0.7416 0.9933

The HPs selected for MD simulations are highlighted in green background.

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dendritic cells (Zuo et al., 2009). Therefore, the understanding of these predicted lipoproteins can be exploited for the formulation of novel therapeutic agents against the infection caused by M. pneumoniae (Kovacs-Simon et al. 2011). 3.6. Structural analyses of virulence factors The first step in M. pneumoniae infection is attachment to host's respiratory epithelium (Layh-Schmitt et al., 2000). Its adherence to the host cells involves the utilization of an attachment organelle present in the polar region of M. pneumoniae cell, which triggers the interactions of the pathogen with the host cells (Layh-Schmitt et al., 2000). The P1 adhesins form the majority of this attachment organelle and is crucial for the process of cytadherence (LayhSchmitt et al., 2000). The HPs H0PQ41, H0PQL0 and H0PR9 showed high similarities to P1 adhesins, indicating that these proteins may be involved in the cytadherence of the M. pneumoniae (Table S3). The VirulentPred showed 171HPs may contain some virulence associated features (Table S5). While DBETH showed 188HPs may be human pathogenic toxin proteins. Furthermore, 139HPs were classified as exotoxins, and 5HPs were annotated as endotoxins by the BTXpred server. The predicted exotoxins may cause harm by disrupting normal cellular metabolism and by destroying cells of the host (Ryan et al., 2004). The absence of cell wall in the M. pneumoniae makes it inherently resistant to b-lactams as well as other cell wall targeting antibiotics (Bebear et al., 2011). The M.

pneumoniae is sensitive to macrolides and similar antibiotics, but the strains developing resistance to macrolides have recently emerged and are spreading across the world (Bebear et al., 2011). Therefore, the Comprehensive Antibiotic Resistance Database (CARD) was used, which predicted 44HPs as antibiotic resistant proteins (Table S5). AlgPred server was used for the prediction of potential allergens in the group of HPs, as more than 50% of the chronic but stable asthma patients show evidence of the respiratory airway infection with M. pneumoniae. There were 145HPs that may act as potential allergens (Table S5). By utilizing the outcomes of functional and virulence predictions, the six HPs were classified as putative virulencefactors among the set of 83 functionally characterized HPs (Table 5). The HPs H0PQA7 and H0PQI2 showed highest scores of 1.1311 and 1.0961, respectively, in the hybrid search of VirulentPred, were selected for further analyses. The structure of HP H0PQA7 was predicted using ab initio protocol of I-TASSER. This protein shows all alpha topology (Fig. 3A) with 23 a-helices. While, three dimensional (3-D) models of HP H0PQI2 were predicted by using the type I restriction-modification S subunit of M. genitalium (PDB ID—1YDX). The obtained structure is subdivided into an N terminal domain, a central domain and a group of two antiparallel a-helices dividing the two globular domains (Fig. 4A). In order to observe the conformational behaviour of HPs with highest virulence scores, the MD simulations were performed at 300 K under explicit solvent conditions. The resulted behaviours of both the proteins were

Fig. 3. (A) Predicted 3-D model of HP H0PQA7 showing all a-helix topology. (B) The RMSD values observed during 20 ns MD simulation showing sharp fluctuations. (C) The plot of radius of gyration against the time intervals indicating the unstable nature of the protein. (D) The RMSF of Ca atoms for HP H0PQA7 exhibiting higher fluctuations.

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Fig. 4. (A) The 3-D structure of HP H0PQI2 using type I restriction-modification S subunit from M. genitalium (PDB ID—1YDX) as template. (B) The sharp fluctuations observed in the RMSD values during 20 ns MD simulation using water as solvent. (C) The plot of radius of gyration against the time intervals indicting HP H0PQI2 is unstable in the natural conditions. (D) RMSF plot of Ca atoms indicating higher fluctuations of the residues.

reported in the form of RMSD, Rg and RMS fluctuations (Figs. 3 and 4). The HP H0PQA7 showed a sharp increase in RMSD values up to the time interval of 10 ns, while sharp fluctuations were continued till 20 ns (Figs. 3 and 4B). Similarly, HP H0PQI2 illustrated a continuous increase in the RMSD values with fluctuations throughout the 20 ns time scale. The average RMSD values for HP H0PQA7 and HP H0PQI2 were calculated to be 1.133 nm and 0.765 nm respectively. Similarly, the radius of gyration showed steep fluctuations in the values (Figs. 3 and 4C) with an average Rg score of 5.869 nm and 2.624 nm for HP H0PQA7 and HP H0PQI2, respectively. This indicates the unstable nature of both the proteins. These results were complemented by the higher values of RMS fluctuations (Figs. 3 and 4D). These analyses showed that HP H0PQA7 and HP H0PQI2 are highly unstable. Our previous work suggested that virulence nature can be associated with the stability of the proteins, as virulent proteins generally showed unstable behaviour (Shahbaaz et al., 2015b,c,d). The bacterial toxins are produced through a variety of secretory mechanisms present in the bacteria (Bendtsen et al., 2005; Henkel et al., 2010). The disordered regions in the secreted proteins are essential for the virulence (Hu et al., 2014). There is no evidence of direct correlation between the structure stability and the virulence of the bacterial proteins. This correlation is only based upon the evidences obtained from MD simulations. Therefore, these HPs can also be considered as the virulence factors and can be utilized for further experimental studies.

4. Conclusions Using in silico pipeline, we successfully analyzed the sequences of 204 HPs from the genome of M. pneumoniae and predicted the functions and analyzed the virulence characteristics of 83 HPs. Due to the unavailability of the experimental validations and less precision of the available in silico methods, a high degree of uncertainty is present in the virulence and functional predictions of the HPs. These findings are complemented by the characterization of the proteins that may be involved in the signalling as well as secretory pathways. Although, the exact molecular function of an HP cannot be deduced computationally, but our study can provides a framework in order to allocate the probable molecular function by utilizing the sequences of the proteins. In this study, the conformational behaviours of the virulence factors were also analyzed by using the Molecular dynamics techniques, which were helpful in understanding the dynamical behaviour of virulent HPs under explicit water conditions. Our study further facilitates a rapid identification of the HPs that are potential therapeutic targets and may play a significant role in better understanding of host-pathogen interactions. Once these HPs are established as a novel drug/vaccine targets, further research for new inhibitors and vaccines can be conducted. Since these proteins may play a significant role in the host-pathogen interactions and can be considered as the putative drug targets for the design of better therapeutic agents, such information can be utilized to discover

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new inhibitor molecules and vaccines for the clinically important organisms. Conflict of interest Authors declare no conflict of interest regarding any financial and personal relationships with other people or organizations that could inappropriately influence (bias) this work. Acknowledgements Authors sincerely thank Indian Council of Medical Research for financial assistance (Project No. BIC/12(04)/2012). We also express our gratitude towards Centre for High Performance Computing (CHPC), South Africa for providing the computational infrastructure. The central instrumentation facility of Jamia Millia Islamia is highly acknowledged for providing high speed server. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. compbiolchem.2015.09.007. References Accelrys, 2013. Discovery Studio Modeling Environment, Release 3.5. Accelrys Software Inc., San Diego. Altieri, A.S., Mazzulla, M.J., Horita, D.A., Coats, R.H., Wingfield, P.T., Das, A., Court, D. L., Byrd, R.A., 2000. The structure of the transcriptional antiterminator NusB from Escherichia coli. Nat Struct. Biol. 7, 470–474. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J., 1990. Basic local alignment search tool. J. Mol. Biol. 215, 403–410. Aravind, L., Anantharaman, V., Balaji, S., Babu, M.M., Iyer, L.M., 2005. The many faces of the helix-turn-helix domain: transcription regulation and beyond. FEMS Microbiol. Rev. 29, 231–262. Bai, Y., Yang, J., Zhou, X., Ding, X., Eisele, L.E., Bai, G., 2012. Mycobacterium tuberculosis Rv3586 (DacA) is a diadenylate cyclase that converts ATP or ADP into c-di-AMP. PLoS One 7, e35206. Bairoch, A., Apweiler, R., 2000. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28, 45–48. Baron, C., Coombes, B., 2007. Targeting bacterial secretion systems: benefits of disarmament in the microcosm. Infect. Disord. Drug Targets 7, 19–27. Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R., Griffiths-Jones, S., Howe, K.L., Marshall, M., Sonnhammer, E.L., 2002. The Pfam protein families database. Nucleic Acids Res. 30, 276–280. Bebear, C., Pereyre, S., Peuchant, O., 2011. Mycoplasma pneumoniae: susceptibility and resistance to antibiotics. Future Microbiol. 6, 423–431. Bendtsen, J.D., Kiemer, L., Fausboll, A., Brunak, S., 2005. Non-classical protein secretion in bacteria. BMC Microbiol. 5, 58. Bernstein, F.C., Koetzle, T.F., Williams, G.J., Meyer Jr., E.F., Brice, M.D., Rodgers, J.R., Kennard, O., Shimanouchi, T., Tasumi, M., 1977. The Protein Data Bank. A computer-based archival file for macromolecular structures. Eur. J. Biochem. 80, 319–324. Bernstein, F.C., Koetzle, T.F., Williams, G.J., Meyer Jr., E.F., Brice, M.D., Rodgers, J.R., Kennard, O., Shimanouchi, T., Tasumi, M., 1978. The Protein Data Bank: a computer-based archival file for macromolecular structures. Arch. Biochem. Biophys. 185, 584–591. Bhasin, M., Raghava, G.P., 2004. GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors. Nucleic Acids Res. 32, W383–9. Bhasin, M., Raghava, G.P., 2005. GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors. Nucleic Acids Res. 33, W143–7. Bhasin, M., Garg, A., Raghava, G.P., 2005. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 21, 2522–2524. Chakrabarty, A.M., 1998. Nucleoside diphosphate kinase: role in bacterial growth, virulence, cell signalling and polysaccharide synthesis. Mol. Microbiol. 28, 875– 882. Chakraborty, A., Ghosh, S., Chowdhary, G., Maulik, U., Chakrabarti, S., 2012. DBETH: a database of bacterial exotoxins for human. Nucleic Acids Res. 40, D615–20. Chen, J., Xie, J., 2011. Role and regulation of bacterial LuxR-like regulators. J. Cell. Biochem. 112, 2694–2702. Chen, L., Yang, J., Yu, J., Yao, Z., Sun, L., Shen, Y., Jin, Q., 2005. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 33, D325–8. Chopra, P., Singh, B., Singh, R., Vohra, R., Koul, A., Meena, L.S., Koduri, H., Ghildiyal, M., Deol, P., Das, T.K., Tyagi, A.K., Singh, Y., 2003. Phosphoprotein phosphatase of Mycobacterium tuberculosis dephosphorylates serine-threonine kinases PknA and PknB. Biochem. Biophys. Res. Commun. 311, 112–120.

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