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Journal of Proteomics & Bioinformatics

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In Silico Analysis of Alkaline Shock Proteins in Enterobacteria

Seetharaaman Balaji1*, V. Murali Krishnan2
1Lecturer, Department of Biotechnology, Manipal Institute of Technology, Manipal  University, Manipal -   576104, India
2Lecturer, Department of Biochemistry, PSG College of Arts and Science, Coimbatore,India
Corresponding Author : Dr. S.Balaji, Lecturer, Department of Biotechnology
Manipal Institute of Technology, Manipal University,
Manipal - 576104, India,
Email    :  [email protected]  
Received April 20, 2008; Accepted May 15, 2008; Published May 25, 2008
Citation: Seetharaaman B, Krishnan VM (2008) In Silico Analysis of Alkaline Shock Proteins in Enterobacteria.
J Proteomics Bioinform S1: S021-S037. doi:10.4172/jpb.s1000005
Copyright: © 2008 Seetharaaman B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Alkaline shock proteins (ASPs) adapt to atypical conditions, which are categorized into a family of small proteins. Alkaline shock proteins are identified in many bacteria and they are involved in stress response. The molecular basis for the survival of bacteria under extreme conditions in which growth is inhibited is a question of great current interest. A preliminary study was carried out to determine residue pattern conservation among the alkaline shock proteins of enteric bacteria, responsible for extreme alkaline tolerance especially in Staphylococcus and Streptococcus. To decipher, is there any secret hidden in the alkaline shock proteins? Bioinformatics approach was used and the molecular evidence proved the relationship between Staphylococcus and Streptococcus with respect to ASP. The sequence, structure and phylogenetic analyses inferred the relationships of various bacteria with respect to the conserved motif (VDNNKAK) of ASPs. Automated subsystem functional annotation of 172 homologous ASPs was done for various bacteria. Currently the structure of the ASP is not available in the Protein Data Bank (PDB). Since the Staphylococcus species formed the root of the phylogenetic tree, the structure of the Staphylococcus aureus (strain bovine RF122) was modeled in order to understand further about the structure and mechanism.

Alkaline Shock Protein (ASP); Staphylococcus; Streptococcus; Structure; Phylogeny
Bacteria can switch its gene expression to adapt to various environments. Albeit the physiological role of alkaline shock proteins is not clear, it plays a key role in alkaline pH tolerance. Though such alkaline resistance mechanism is found in some bacteria, it is not the characteristic feature of all microbes. The response to alkaline pH can be characterized as alkaline shock (exposure to alkaline pH for up to 30 min) and alkaline adaptation (exposure to alkaline pH for periods of more than 60 min). Adaptation of Salmonella or E. coli to alkaline conditions is accompanied by induced thermotolerance, increased resistance to bile salts, and increased resistance to high pH (Taglicht et al., 1987; Flahaut et al., 1997; Goodson & Rowbury, 1990; Humphrey et al., 1991). Conversely, alkaline adaptation of S. enterica serovar enteritidis or E. coli sensitizes the cells to acid stress (Rowbury et al., 1993) and vice versa (Rowbury & Hussain, 1996). Staphylococcus aureus is a member of mammalian body surface normal flora and occasionally causes pyogenic infections, from simple skin suppuration to life-threating septicemia and may co-regulate the production of its virulence factors in response to environmental stresses such as heat shock, osmotic shock, and anaerobiosis (Mekalanos, 1992). Among these factors, pH is emerging as an important mean of regulating gene expression in S. aureus, because alkaline pH decreases expression of the Accessory Gene Regulator (agr) which affects the expression of numerous exoproteins, including E-hemolysin, toxic shock syndrome toxin 1, protein A, and the staphylococcal enterotoxins types B, C and D (Regassa & Betley, 1992). In order to clarify whether alkaline shock correlates with the expression of these exoproteins (Kuroda et al. 1995) examined the effect of pH shift on the composition of cytosolic proteins in S. aureus. A protein of molecular mass of 23 KDa was remarkably enhanced by a pH up-shift from 7 to 10. This alkaline shock protein (ASP 23) was isolated and purified. The deduced primary sequence of ASP23 comprised 169 amino acids with a calculated molecular weight of 19,191. At least three sigma factors, sigA, (Deora & Misra, 1996) sigB, (Kullik & Giachino, 1997; Wu et al., 1996) and SA0492, (Morikawa et al., 2003) have been identified in S. aureus. Each sigma factor recognizes a different promoter sequence and allows the RNA polymerase to initiate site-specific transcription for a specific group of genes. Although the primary sigma factor, sigA, is constitutively maintained, the activity of the alternative sigma factor, sigB, depends on growth-phase and various environmental stresses, suggesting that staphylococcal sigB may regulate some stress responses (Bateman et al., 2001; Nair et al., 2003). The alkaline shock protein gene, asp23, is under the sole control of SigB and its expression is often used as an indicator of sigB activity (Giachino et al., 2001). The aim of this study is to understand the properties of alkaline shock proteins because the molecular mechanism of such pH tolerant properties should be elucidated because the production of the virulence factors was greatly affected by environmental pH (Kuroda et al., 1995). Since the protein structure for the ASP is not available in the Protein Data Bank (PDB) (Berman et al., 2000) and moreover currently no work has been done so far to predict the structure, it limits enthusiasm to understand the mechanism through the structure. Hence we took initiative and developed a structure for asp23 of Staphylococcus aureus, strain bovine RF122 (because of analogy to other bacterial pathogens) by prediction-based threading method.
Materials and Methods
Sequence Data
The alkaline shock protein sequences were collected from UniProt  Knowledgebase, SwissProt and TrEMBL (Table 1 under supplementary  material).The key word ‘alkaline shock protein’ yielded  70 hits of protein sequences from SwissProt and TrEMBL (on  the month of June, 2006) [URL]. There  were only 9 sequences in SwissProt and 61 sequences in TrEMBL.  In addition to that 175 putative proteins were also collected and  annotated using National Microbial Pathogen Data Resource  (NMPDR).
Sequence Alignment
A multiple sequence alignment was done by using Clustal X Ver.1.83 (Thompson, et al., 1997), the gap opening was set at 10.00, the gap extension at 0.20 with 30% delay divergent sequences  and Gonnet series weight matrix was used. From the multiple sequence alignment, the guide tree was derived. To justify  the confidence of the clades, re-sampling method (bootstrap)  was used with 10000 trails. Web logo (ver 2.8.2) was used to  identify the conserved pattern in the ASPs of pathogenic bacteria.  Alignments were analysed and phylogenetic relationships  among the sequences were established using different procedures:  Neighbour-Joining (NJ) (Saitou & Nei, 1987), Fast Minimum  Evolution (FastME) (Desper & Gascuel, 2002), Unweighted Pair  Group Method with Arithmetic Mean (UPGMA) (Sokal & Michener, 1958). The final tree was displayed by using MEGA  3.1 (Kumar, et al., 2004).
Phylogenetic Alignment
Alignments were analysed and phylogenetic relationships  among sequences were established using different procedures: Neighbour-Joining (NJ), Fast Minimum Evolution (FastME),  Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and Fitch-Margoliash (FM). Trees and genetic distances were  based on 10,000 replicates in order to assess the degree of confidence  for each branch on the trees. Heuristic searches were completed  with maximum parsimony. Absolute distances and pairwise  distances were calculated for all pairwise combinations of operational  taxonomic units (OTUs).
Protein Structure Prediction
Identification of Structurally Homologous Proteins
In searching for structural homologues for alkaline shock protein  (asp23) of Staphylococcus aureus (strain bovine RF122), the complete amino acid sequence of length 124aa (EMBL Protein:  AJ938182 /TrEMBL: Q2YXJ1) was primarily submitted to Basic Local Alignment Tool (BLAST) (Altschul et al., 1990).  Nevertheless, it does not yield any significant templates from Protein Data Bank (PDB). Therefore it was re-submitted to the  PredictProtein server (Rost et al., 2004). This server returns a multiple sequence alignment and predictions of secondary structure,  residue solvent accessibility, and the location of transmembrane  helices. The secondary structure of asp was then threaded,  using this information against proteins in the PDB. The  PredictProtein program detects remote homologues (0–25% sequence  identity) by a novel prediction-based threading method  (Rost et al., 1997). To recognize folds by threading, the  PredictProtein program evaluates the amino acid sequence of a  protein and determines how well it fits into the 3D configuration  of proteins whose structures are known. The goal is to detect  similar motifs of secondary structure and accessibility between a  sequence of unknown structure and a known fold. Proteins with  known 3D structure and the highest degree of structural homology  to asp23 were identified by Predict-Protein, which also provided  summary information on these proteins from the server via  e-mail.
Identification and Alignment of Structurally Conserved Regions (SCRs)
The multiple sequence alignment function in PredictProtein is  automatically returned in the report from PredictProtein and is built up in two steps (Sander & Schneider, 1991). In sweep 1, sequences are aligned consecutively to the search sequence by a  standard dynamic programming method. After each sequence has  been added, a profile is compiled and used to align the next sequence.  In sweep 2, after all sequences with significant structural  homology have been selected from SWISSPROT, the profile  is recompiled and the dynamic programming algorithm commences  once again to align the sequences consecutively, this time  using the conservation profile as derived after completion of  sweep 1. The output consists of structurally homologous proteins  with regions automatically aligned to asp23. In addition,  the known and the predicted secondary structures of the PDB  proteins and asp23 are shown. With this information, we manually  highlighted areas of predicted secondary structure in asp23  that were identical to the known structural homologues.
Assignment of Coordinates
DeepView (Swiss-PdbViewer) is tightly linked to SWISSMODEL  (Guex & Peitsch, 1997), an automated homology modeling server that was used in combination with the downloaded  asp23 sequence. With these two programs it is possible to thread a protein primary sequence onto a 3D template and obtain an  immediate feedback of how well the threaded protein will be accepted by the reference structure prior to submitting a request  to build missing loops and refine side chain packing. The PDB  files of the three best-fitting structural homologues of asp23 identified  by PredictProtein were downloaded and individually manually  aligned to asp23, according to the alignment suggested by  PredictProtein. Secondary structures were predicted around the  asp23 sequences by using nnPredict (Kneller et al., 1990) that  was found to be structurally homologous to the other  PredictProtein listed proteins. With Swiss-PdbViewer, manually  and carefully adjusted the alignment and transmitted the project  file to SWISS-MODEL through SwissModel optimise mode. Coordinates  were then assigned based on the known reference protein  structure (1IXH). All coordinates were transferred if the side  chains of the reference and model proteins were at the same corresponding  locations along the sequence of the structurally conserved  region. However, if these locations differed, only the backbone  coordinates were transferred and the side chain atoms were  automatically replaced to preserve the asp23 model’s residue  types. These replaced residues were first aligned to the backbone  of the original residue; the dihedral angles in common with the  residue being replaced were also aligned. This allowed the conformation  side chain to be preserved as much as possible.
Loop Generation
Since the asp23 protein had structural homology to the known  proteins, and gaps existed in between the alignment, loops had to  be generated. This was done using the method described by  (Shenkin et al. 1987). Briefly, a conformational search with random  settings of Phi and Chi angles was made in order to build a  peptide backbone chain connecting two conserved peptide segments.  A set of six distances was defined using two atoms in the  start residue of the loop at the amino-terminal as well as two  atoms at the carboxylterminal stop residue of the loop. These  distances must meet specific criteria for the loop to be acceptably  closed. The loops were generated by using the “Build Loop”  option of the DeepView, which uses energy information (computed  with a partial implementation of the GROMOS Force-Field  (van Gunstern & Berendsen, 1977) and a mean force potential  value (PP) computed from a “Sippl-like” mean force potential  (Sippl, 1990). This process also used the “Scan Loop” option  that gave the name of PDB files that contain a suitable loop, the  chain identifier, the starting residue, the sequence of the possible  fragment, and the resolution (in Å) at which the structure had  been solved. The similarity score for the fragment was also computed  from the PAM200 matrix. In addition to those, a clash score,  and the number of residues from the source loop that have bad  phi/psi angles were also obtained to sort the loops by energies or  clashes to ease the process of identifying the best loop. Finally,  an energy minimization was performed and the geometry of the  loop was checked for proper chirality and steric overlap violations,  accepting those conformations that close the loop.
Structure Validation
To assess the geometric correctness of the theoretical structure,  the following programs were used; VERIFY 3D (Eisenberg et al., 1997) for assessment of asp23 model with three-dimensional  profiles, WHATIF (Vriend, 1990) to validate asp23 structure,  PROCHECK (Laskowski et al, 1996) to check the stereochemical  quality of asp23 and plots its overall and residue-byresidue  geometry, WHATCHECK (Hooft et al., 1996) to find  out errors in asp23 structure. These programs checked the protein-  specific bond lengths, angles, and torsions of the theoretical  asp23 model. The parameters checked included phi-psi angles,  chi1 dihedral angles, chi2 dihedral angles, main-chain parameters,  side-chain parameters, residue properties, main-chain bond  length and bond angle distributions, RMS distance from planarity  and distorted geometry plots etc. This process not only assessed  the geometric validity of the proposed structures, but also  focused attention on problem areas in the structure.
Results and Discussions
Automated functional annotation was performed for 175 proteins  by using the tools provided by the National Microbial Pathogen  Data Resource (NMPDR) (McNeil, 2007) which was useful  to study the comparative functional analysis of genomes and biological  subsystems, with an emphasis on pathogenic species of  Campylobacter, Listeria, Staphylococcus, Streptococcus, and  Vibrio.
Graphical Representation of Protein Encoding Genes (PEGs)
In the graphical representation of the gene’s structure, blue  genes are located within 6 kb upstream or 6 kb downstream of the green gene of focus in at least four other species. Whether or  not the gene of interest appears to be clustered in its genome,  homologs of this gene may occur in clusters in other genomes.  The organisms in which the homologous gene were clustered  with others, ordered by the size of the cluster. The neighboring  proteins were also predicted and the numbers were computed.  The functional clustering scores, which are approximately equal  to the number of different species (not strains), were also predicted.  The graphical representation of the homologous chromosomal  clusters (Fig. 1) makes easy understanding of the gene  architecture and illustrates functional clustering.
Homologs of the focus peg are red, labeled 1, and aligned in  the center of the page. All of the genes within about 8 kb of this central peg are shown. Numerical labels correspond to rank ordered  frequency of co-localization with the focus protein-encoding  gene (peg). Non-homologous proteins and RNAs are grey,  although sometimes very small proteins that share real homology  are grey because the homology score, which is a function of  chain length, is below the cut-off imposed on this figure. The homologous pegs were compared with UniProt and NMPDR  annotations. The strategy for finding genes was based on chromosomal  clustering and occurrence profiling. The abbreviations  used to represent bacteria in the hromosomal clusters (Fig.1)  are Sym.the.IA (Symbiobacterium thermophilum), (Bacillus  anthracis), Bac.cer.ZK (Bacillus cereus strain ZK), Bac.cla  (Bacillus clausii), Bac.hal (Bacillus halodurans), Bac.lic (Bacillus  licheniformis), Bac.sub (Bacillus subtilis), Bac.thu (Bacillus  thuringiensis), Geo.Kau (Geobacillus kaustophilus), Oce.ihe  (Oceanobacillus iheyensis), Lis.mon (Listeria monocytogenes).
Sequence Analysis
A preliminary multiple sequence alignment was carried out  among all ASPs of all available sequence of gram positive bacteria  and a few gram negative bacteria as well. Multiple sequence  alignment of the ASPs showed many conserved residue patterns  in a regular interval at the N-terminal region (Fig.2). As the alignment  approached the C-terminal end, the number of conserved  residues was gradually decreased (Fig. 3), indicating that the Nterminal  region of this protein has a much active role when compared  to the carboxyl terminal end. The motif VDNNKAK is  considered to play an important role, sequence alignment analysis  confirmed that the motif VDNNKAK was well conserved  within the entire ASPs of different bacteria (at the amino terminal).  It proved that the VDNNKAK motif is not only unique for  Staphylococcus aureus but also for the other bacteria such as  Arthrobacter sp., Streptococcus sp., Listeria sp., Rhodococcus  sp., Clostridium sp., Lactobacillus sp., Clostridium sp., Bacillus  sp., Lactobacillus sp., Symbiobacterium sp., Streptococcus sp.,  Lactobacillus sp., Clostridium sp., Staphylococcus sp., Streptococcus  sp., Deinococcus radiodurans, Geobacillus  thermodenitrificans, Clavibacter michiganensis and Oenococcus  oeni.
We also extended our analysis to address the pattern conservation  among the other ASPs involved in alkaline resistance mechanism and found that the pattern is still partially conserved.  This pattern conservation also depicts that the function is highly  dependant on the pattern used for the alkaline resistance. The  strong motif conservation could be the reason for the extreme  alkaline resistance of the analysis dataset. The pattern was also  searched against the PROSITE database and found match with  all Staphylococcus sp.
Phylogenetic Analysis
Phylogenetic trees were built based on the multiple sequence  alignment. The trees were constructed with 10000 bootstrap trials  (Fig. 4) shows that the ASPs of similar species form individual  clusters which deviate from other ASPs. The similar trend  was also observed in phylogenies obtained by using different  methods (NJ, UPGMA and FastME). The ASP sequence of Staphylococcus  aureus was found to be the root for all other bacterial  ASPs. The convergence of different Staphylococcus aureus  (P0A0P8) strains such as strain COL (Q5HE23), strain Mu50 /  ATCC 700699 (P0A0P6), strain N315 (P99157), strain MRSA252  (Q6GEP7), strain MSSA476 (Q6G7D2), strain MW2 (P0A0P7),  strain USA300 (Q2FEV0) and strain bovine RF122 (Q2YYG3)  was also observed. From Fig. 5, it is clear that Staphylococcus  aureus and their strains form the root of the tree (0.0) to all operational  taxonomical units (OTUs). All other bacteria were dispersed  (diverged) equally from the origin. Minimum degree of  divergence from the root corresponds to the Streptococcus group  (0.090910) such as Streptococcus pyogenes serotype M3  (Q7CER3), Streptococcus mutans (Q8DW47) and Streptococcus  pyogenes serotype M28 Q48RF5. The species epidermidis  (strain ATCC 35984 / RP62A and strain ATCC 12228) of the  genus Staphylococcus (Q5HM47 and Q8CNG0) showed little  divergence from the Staphylococcus group (0.156630). The species  saprophyticus of the genus Staphylococcus (Q49ZC7), Staphylococcus  saprophyticus subsp. Saprophyticus (strain ATCC  15305 / DSM 20229) was also deviated (0.224810) from the Staphylococcus  group. The rest of all bacteria diverge from the root  in the range of (0.6 to 0.94). The root (0.0) and the closely related  cluster (0.09 - 0.22) have the conserved VDNNKAK motif.  The conservation decreases slightly with respect to other clusters  or distantly related /orthologous ASPs might be due to the  interruption of indels in the ASP coding gene. The pattern analysis  shows the strong relationship of Staphylococcus and Streptococcus,  they are members of the group of potentially pathogenic  bacteria (PPB). This finding is also supported by the phylogenetic  analysis (Fig. 6) of ASP cluster which is congruent with the  high degree of identity between Staphylococcus and Streptococcus  groups, shows the close pathogenic association and the alkaline  tolerance of the two genuses.
The phylogenetic tree of alkaline shock proteins from  various bacterial strains. The bootstrap values are shown at the branch points, 10000 replicates were done in order to access the  confidence of the clades.
The Radial Phylogram of alkaline shock proteins from  various bacterial strains which are dispersed (diverged) equally from the root (Staphylococcus aureus).
The Phylogram of alkaline shock proteins from various bacterial strains. The branch length were evaluated with 10000 replicates shows the equal divergence of the clades.
Protein Structure Prediction
The phylogenetic analysis proved that the Staphylococcus  serves as a root for all ASPs of the analysis data set. Hence the ASP of Staphylococcus aureus (strain bovine RF122) has been  modeled by using prediction based threading, the complete amino  acid sequence of length 124aa (EMBL Protein: AJ938182 /  TrEMBL: Q2YXJ1) was primarily submitted to Basic Local  Alignment Tool (BLAST). Nevertheless, it does not yield any  significant templates from Protein Data Bank (PDB). Therefore  it was re-submitted to the PredictProtein server. The secondary  structure of the ASP was then threaded, using this information  against proteins in the PDB. The PredictProtein program detects  remote homologues by a novel prediction-based threading  method. To recognize folds by threading, the PredictProtein program  evaluates the amino acid sequence of a protein and determines  how well it fits into the 3D configuration of proteins whose  structures are known. The Predict Protein program identified 20  closest structural homologues from threading-based prediction  TOPITS (Threading One-dimensional Predictions In to Three-  Dimensional Structures) and provided a z score for each. This  score is highly dependent on the similarity of characteristics including  alignment length, compositions of secondary structure, and accessibility of amino acids between the protein of known 3D structure and the protein of interest. The higher the z score, the higher the probability that the first hit is correct. In general,  an alignment z score (ZALI) with z > 3 is more reliable. Threading  one-dimensional predictions in to three-dimensional structures  (TOPITS) yielded twenty different templates. Nevertheless,  based on the ZALI score first three templates were chosen. The  first three ranks of protein templates to model ASP are Phosphate- Binding Protein [PDB: 1ixh], GTP cyclohydrolase [PDB: 1a8r], and OMPF porin from E.coli [PDB: 2omf]. Only the first  template was considered as a better template based on the statistical  significance. By using PIR pairwise alignment the template  was found to have a Smith-Waterman score of 64 and have  32.231% identity with the template. The reliability was compared  by ZALI score of the templates, and in addition a better template  with higher ZALI score (2.51) among the scored templates was  selected and used for this modeling procedure. In the absence of  the best template, we have selected the first scored template [PDB: 1BK0]. Homology models were constructed using a combination  of the Swiss-PDB viewer (Deep View) and the Swiss-Model online modeling server (project-optimize mode) for assigning  atomic coordinates. Prediction of the secondary structure for ASP  is based on the sequence and structure comparison taken into  account. In order to validate our method to predict the secondary  structure, we have first used the 1ixh (template) as a test sequence.  Using the software SOPMA (Geourjon & Deleage, 1995) we  obtain correctly predicted structural elements. Such prediction  results provide confidence with respect to the reliability of the  ASP structure prediction. The secondary structure elements of  the template and the structural prediction of the ASP are shown  in Fig. 7. The comparison between the secondary structure elements  observed in the template crystal structure and the prediction  made for ASP (Fig. 8) shows that beta strands and alpha  helices are almost perfectly superimposed, though some residues  of the template are not aligned because of the large sequence  length. The alignment shows 32.231% sequence identity, with  insertions and deletions (Indels) primarily localized in loops.
The alignment of the better template [PDB: 1ixh] with ASP (target)  was done and the positions of the experimentally observed secondary structure elements of the template and of the predicted  secondary structure of ASP were superimposed on the aligned  sequences. The structures are depicted in the Fig. 9. The aligned  template [1ixh] with target [ASP] was used to build and refine  the ASP model. The final model (Fig. 9) was iteratively minimized  for energy and subjected to structure verification and evaluation.  The Sasisekaran - Ramakrishnan - Ramachandran diagram  (or simply “Ramachandran plot”) of PROCHECK (Laskowski  et al, 1996) showed 83.6% residues in most favored regions with  1.8% residues in disallowed regions (Table 2). The Ramachandran  plot (Fig. 10) shows the phi-psi torsion angles for all residues in  the structure (except those in chain termini).
Glycine residues are separately identified by triangles. The shading on  the plot represents the different regions described in Morris et al., (1992).  The darkest areas correspond to the “core” regions representing the most  favourable combinations of phi-psi values. The modeled structure has  83.6% of the residues in the core region. The model can be compared  with other bacterial ASP models (if available) in order to find the structural  divergence especially the VDNNKAK motif.
Structural alignment of template [PDB ID: 1ixh] and target  (ASP, Q2YXJ1). Aligned target regions are shown in blue color, whereas unaligned regions are shown in yellow color.
(A) The final model of the alkaline shock protein (Q2YXJ1)  in ribbon model (B) Schematic representation.
The Ramachandran plot of the modeled alkaline shock protein.
Multiple sequence alignment of all available ASPs showed  many conserved residue patterns at regular intervals at the N-terminal  region. It was observed that when the alignment approaches towards  the C-terminal, there is a decline in the number of conserved residues,  indicating that the N-terminal region of this protein has much active role  when compared to the carboxyl terminal. The motif, VDNNKAK is  well conserved in the entire ASP at the amino terminal. The motif is also
partially conserved among other distantly related bacteria but involved  in alkaline resistance mechanism. Phylogenetic cluster analysis proves  the relationship of Staphylococcus and Streptococcus with other bacteria.  The model presented here shows for the first time details of the  unique structural features of ASP. This model should be used with caution,  because of the lack of x-ray diffraction data. Moreover in our predicted  protein, positions of atoms are far from precise, and geometries  are often non-ideal, so an x-ray structure of the ASP with high resolution  may reveal new, unexpected features of ASP. The predicted model  will be compared with the actual crystal data when available. The characterization  of the ASP binding site could shed light and insight into an  interesting perspective to predict mechanism of interaction with other  biomolecules. The production of the virulence factors was greatly affected  by environmental pH. Inhibitors of this regulatory protein are not  currently available. Therefore, this model would be helpful to initiate  structure related studies, which provide guidance for rational drug discovery.
This work was supported by Bharathidasan University,  Trichirappalli, Tamilnadu, India, as a part of my M.Phil in Bioinformatics.
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