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Research Article
Open Access
Role of Cation-π Interactions in the Structural Stability of Bacterial Exotoxins
Anand Anbarasu* and P.S. Shrivaishnavi
Bioinformatics Division, School of Biosciences & Technology, VIT University, Vellore-632014, India
*Corresponding author:
Dr. Anand Anbarasu, Bioinformatics Division,
School of Biosciences & Technology,
VIT University, Vellore-632014, India,
Tel       : +91-416-2202556/2574/2608,
Fax      : +91-416-2243092,
E-mail : aanand@vit.ac.in
Received December 20, 2009; Accepted December 28, 2009; Published December 29, 2009
Citation: Anbarasu A, Shrivaishnavi PS (2009) Role of Cation-π Interactions in the Structural Stability of Bacterial Exotoxins. J Microbial Biochem Technol 1: 022-029. doi:10.4172/1948-5948.1000005
 
Copyright: © 2009 Anbarasu A, 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.
 
Abstract
Bacterial exotoxins secreted by bacteria can cause damage to the host by destroying cells or disrupting normal cellular metabolism. They may exert their effect locally or produce systemic effects. We have presented the results observed with cation-π interactions in bacterial exotoxins in relation to environmental preferences like secondary structure, solvent accessibility, and stabilization centers. Arg have a higher occurrence than Lys in the cationic group and Tyr contributes in higher numbers than Phe and Trp in π group. Our results might be useful for understanding the stability patterns of exotoxins in bacteria.

Keywords
Exotoxins; Cation-π interactions; Secondary structure; Conservation patterns; Stabilization centers

Abbreviations:
Arg: Arginine; Lys: Lysine; Phe: Phenyl alanine; Tyr: Tyrosine; Trp: Tryptophan;
DSSP: Dictionary of Secondary Structure in Proteins.

Introduction
Exotoxins are a group of soluble proteins that are secreted by the bacterium, enter host cells, and catalyze the covalent modification of host cell components to alter the host cell physiology. Both Gram-negative and Gram-positive bacteria produce exotoxins. A specific bacterial pathogen may produce a single exotoxin or multiple exotoxins. Each exotoxin possesses a unique mechanism of action, which is responsible for the elicitation of a unique pathology. Bacterial exotoxins catalyze specific chemical modifications of host cell components, such as the ADPribosylation reaction or the deamidation reaction. These chemical modifications may either inhibit or stimulate the normal action of the target molecule to yield a clinical pathology. Many bacterial exotoxins can be chemically modified to toxoids that no longer express cytotoxicity, but retain immunogenicity. Bacterial toxins can also be genetically engineered to toxoids, which may lead to a wider range of vaccine products. Exotoxins have also been used as therapeutic agents to correct various disorders, including the treatment of muscle spasms. Nontoxic forms of exotoxins have been used as carriers for the delivery of heterologous molecules to elicit an immune response and as agents in the development of cell-specific chemotherapy. In addition, bacterial toxins have been used as research tools to assist in defining various eukaryotic metabolic pathways, such as G proteinmediated signal transduction (Barbieri, 2009).

The three-dimensional structure of a protein is determined by a delicate balance of non-covalent interactions. Hydrogen bonds, salt bridges, and the hydrophobic effect all play roles in folding a protein and establishing its final structure. Cation-π interactions (Gallivan and Dougherty, 1999; Ma and Dougherty, 1997; Dougherty, 1996) are increasingly recognized as an important
non-covalent binding interaction relevant to structural biology. Cation–π interactions are formed when a cation binds to the π face of an aromatic structure through an electrostatic attraction between a positive charge and quadrupole moment of the aromatic (Dougherty, 1996). Many earlier works have reported theseinteractions to be significant in determining protein structure (Gallivan and Dougherty, 2000), association process of biomolecules like the antigen–antibody binding (Pellequer et al., 2000) and receptor–ligand interaction (Dougherty and Stauffer, 1990; Mecozzi et al., 1996a; Mecozzi et al., 1996b). The significance of these interactions in the folding of polypeptides (Shi et al., 2002), stability of membrane proteins (Gromiha and Suwa, 2005; Gromiha, 2003), DNA-binding proteins (Gromiha et al., 2004), and thermophilic proteins (Gromiha et al., 2002) is also being stated. We assume there are no reports on the structural stability studies of exotoxins using bioinformatics tools in relation to other environmental preferences like secondary structure, solvent accessibility, sequential distance, stabilization centers and conservation patterns. Hence, we undertook this study to systematically analyze the non-covalent interactions and their energetic contribution in bacterial exotoxins through bioinformatics methods. The importance of non-covalent interactions in the structural stability of inflammatory proteins (Anbarasu et al., 2006) and conjugated proteins has been reported from our group (Anbarasu and Rao, 2007). Ours is probably the first such report on the bioinformatics aspects of bacterial exotoxins and our results might be useful in further understanding the structural stability patterns in exotoxins.

Materials and Methods

Data set
All available crystal structures of exotoxins from PDB (Berman et al., 2000) were taken for the present study and the PDB IDs included in the present study are presented in Table 1. The selection criteria for an exotoxin to be included in the dataset were based on the following criteria.

Table 1: Energetic contribution from cation-π interactions.

(i) The source should be from bacteria.

(ii) The sequence identity among the proteins in the dataset was less than 40%.

(iii) Three dimensional structures of these proteins have been solved with ≤ 2.9 A°.

Cation-π interactions
The number of cation–π interaction in each exotoxin in the dataset was computed by the program cation pi trends using realistic electrostatics [CAPTURE] (Gallivan and Dougherty, 1999). All exotoxins that had energetically significant interactions (Ecat–π ≤2 kcal/mol) were selected for the computational analysis. The total cation–π interaction energy (Ecat–π) was divided into electrostatic (Ees) and van der Waals energy (Evw). The electrostatic energy (Ees) was calculated using the equation below:

                   Ees = qiqje2/rij

Where qi and qj were the charges for the atoms i and j, respectively, and rij was the distance between them. The van der Waals energy was computed using the equation given below:

             Evw = 4 εij [(σij12/rij12) – (σij6/rij6)]

Where σij=(σii σjj)1/2 and εij=(εii εjj)1/2; σ and ε were the van der Waals radius and well depth, respectively.

Sequential distance
For a given residue, the comparison of the surrounding residue was analyzed in terms of the location at the sequence level. The contribution from <± 4 were treated as short-range contacts,>± 4 to <± 20 as medium-range contacts and >± 20 were treated as long range contacts (Gromiha and Selvaraj, 1997; Selvaraj and Gromiha, 2003; Gromiha et al., 2004). This classification enabled us to evaluate the contribution of short, medium and long-range contacts in the formation of cation-π interactions. This classification also provides clues so as to understand the importance of these non-covalent interactions in the structural stability of secondary structural elements and their importance in the local and global conformational stability.

Solvent accessibility patterns
For more than three decades, experimenters and theorists had tried to understand the kind of interactions that govern first stages of protein folding and lead to the formation of folding intermediates, and the forces that maintain protein stability (Munoz and Serrano, 1996). The main questions concern the relative importance of hydrophobic versus more specific interactions, and of local versus non-local interactions along the sequence. It was indeed possible to detect stability changes caused by mutation, in the folded and transition states, by measuring and comparing the changes in unfolding and activation free energies. Whereas now there are a lot of experimental data on folding free energy changes upon mutation obtained by site-directed mutagenesis experiments only a few theoretical methods have been developed to predict such stability changes. Some of these methods were based on detailed atomic models and others on rougher descriptions of protein structure (Sippl, 1995). Their performances were in general, evaluated by comparing the calculated folding free energies to the measured ones and were reasonably good. In most studies, the mutated residues were buried in the protein core; since hydrophobic interactions dominate in these regions, the energetic criteria obviously involve hydrophobicity. In the few reported studies analyzing mutations of solvent accessible residues, the stability changes are correlated with statistical propensities of single amino acids to be in α-helices or β- strands (Munoz and Serrano, 1994), or with distance-dependent residue-residue potentials (Gilis and Rooman, 1996; Gilis and Rooman, 1997). Hence, we thought it would be meaningful to analyze the solvent accessibility patterns of residues that are involved in cation-π interactions. The solvent accessibility parameters were obtained from DSSP (Kabsch and Sander, 1983).

Secondary structure preferences
The structural preferences of amino acids were introduced and calculated a long time ago, and it was known that different amino acids have distinct preferences for the adoption of helical, strand and turn conformation (Chou and Fasman, 1974; Chou and Fasman, 1978; Padmanabhan et al., 1990). Although much were known about secondary and tertiary protein structure and folding, the process of folding is not understood completely. The molecular mechanism of protein self-assembly is still an open question (Fitzkee et al., 2005). It is believed that the energetics of side chain interactions dominate protein folding processes. However, it was shown that secondary structure can determine native protein conformation, devoid of side chains (Fleming et al., 2006). Recently, a backbone-based theory of protein folding was proposed, where the protein folding mechanism is based on backbone hydrogen bonding (Rose et al., 2006), while α-helix and β-sheet propensities are closely connected with the energetics of peptide H-bonds (Baldwin, 2007). We thought it would be useful to study the secondary structure preferences of amino acid residues involved in cation-π interactions so that the importance of these interactions in the structural stability with respect to global and local conformations can be clearly understood. Secondary structure types were assigned by DSSP (Kabsch and Sander, 1983) and are denoted using letters: H for α-helix, B for isolated β-bridge, E for extended strand, G for 3-helix, I for 5- helix, T for turn, and S for bend. Secondary structure types was often reduced to only three; helix, strand, and turns (Rost, 2001; Kloczkowski et al., 2002) and hence we restricted to these three secondary structural conformations and used data from DSSP for our analysis.

Stabilization centers
For predicting the stabilization centers, we used profiles extracted from multiple alignments as input to the network (Dosztányi et al., 1997; Dosztányi et al., 2003). The alignments were taken from the HSSP data bank (Sander and Schneider, 1991). For each residue the frequency of occurrence was computed for the 20 amino acids at each position in the alignments; thus, the input group contained 20 real values reflecting the statistics on amino acid occurrences at the given sequential position (Dosztányi et al., 2003). There was one additional input unit for the conservation weight for each residue that reflected the conservation of the given position in the alignment. These weights were also included in the HSSP files. The teaching and the training procedure was similar to the one applied in case of single sequences (Dosztányi et al., 2003). To estimate the significance of the calculated amino acid composition of the set of residues involved in long range interactions and in the stabilization centers, standard deviations were calculated in the following way: datasets were randomized 1000 times and distributions were calculated from all cases. The standard deviation was derived from the resulting Gaussian-like distribution (Dosztányi et al., 2003).

Conservation score
The degree to which an amino acid position is recessive to substitutions is strongly dependent on its structural and functional importance. An amino acid that plays an essential role, e.g. in enzymatic catalysis, were likely to remain unaltered in spite of the random evolutionary drift. Hence, the level of evolutionary conservation was used often as indicator for the importance of the position in maintaining the protein’s structure and or function (Glaser et al., 2003). For computing conservation score the following methodology was adopted:

(i) The amino acid sequence was extracted from the PDB file.
(ii) Homologous sequences in the SWISS-PROT database (Boeckman et al., 2003) were searched and collected using PSI-BLAST (Altschul et al., 1997).
(iii) A multiple sequence alignment (MSA) of these sequences was constructed using CLUSTAL W (Thompson et al., 1994).
(iv) A phylogenetic tree was re-constructed based on the MSA, using the neighbor-joining algorithm (Saitou and Nei, 1987) as implemented in the Rate4Site program (Pupko et al., 2002).
(v) Position-specific conservation scores were computed using the empirical Bayesian (Mayrose et al., 2004) or maximumlikelihood (Pupko et al., 2002) algorithms.
(vi) The continuous conservation scores were divided into a discrete scale of 9 grades. Grade 1 contained the most variable positions; grade 5 contained intermediately conserved positions; and grade 9 contained the most conserved positions (Glaser et al., 2003; Landau et al., 2005).

Results and Discussion

Cation-π interactions
Cation-π interactions are computed for all the interacting pairs and the cation-π interacting pairs along with their energies are tabulated in Table 1. There are a total of 5334 amino acid residues and 61 energetically significant cation-π interactions in the data set studied. Hence there is an average of one cation-π interaction for every 87 residues and an average of four interactions per exotoxin in the data set. Among the donor cationic amino acids there is an increased preference for Arg over Lys in exotoxins. 59% of the residues are Arg, while the remaining 41% is Lys. This phenomenon may be due to the fact that the side chain of Arg is larger and less well water-solvated than that of Lys, it
likely benefits from better van der Waals interactions with the aromatic ring (Gallivan and Dougherty, 1999). In addition, the side chain of Arg may still donate several hydrogen bonds while simultaneously binding to an aromatic ring (if it is stacked) whereas Lys would typically have to relinquish hydrogen bonds to bind to an aromatic (Mitchell et al., 1994). Moreover, the occurrence of Arg in the exotoxins is slightly higher than Lys as presented in Table 2. In the acceptor π-group Tyr residues are higher than Phe and Trp in the data set. The increased number of cation-π interactions involving Tyr must be attributable to effects, such as the ability of the OH group of the tyrosine to act as a hydrogen bond donor (Gallivan and Dougherty, 1999). If the Tyr OH donates a hydrogen bond, it substantially potentiates the cation-π binding ability of the phenolic ring (Mecozzi et al., 1996). Also, the negative electrostatic potential on the oxygen could directly contribute to cation binding. In addition the increased occurrence of Tyr in exotoxins may also contribute to higher frequencies of Tyr in energetically significant cation-π interactions.

In terms of pair wise interactions the preferences are similar to the above discussed patterns and the highest pairing of cation- π interactions is with the Arg-Tyr pairs as shown in Figure 1. The percentages of cation-π interacting pairs in exotoxins are 27.87, 18.03, 13.12, 18.03, 13.11 and 09.84 respectively for Arg- Tyr, Arg-Phe, Arg-Trp, Lys-Tyr, Lys-Phe and Lys-Trp. It is interesting to note that, even though the percentage occurrence of Trp in exotoxins is very minimal when compared with the other two π residues as represented in Table 2, there is a significant number of Arg-Trp and Lys-Trp interacting pairs. The higher number of interactions with Trp residues in spite of its lower occurrence in exotoxins may be due to the larger size of Trp allowing it to an increased number of contacts with cations in relation to Tyr and Phe (Gallivan and Dougherty, 1999). Thus Trp if present has higher chances to form cation-π interactions and provide stability through these interactions. In exotoxins the contribution of Arg is higher in the cationic side and Tyr contributes more among the π acceptors. The cation-π interacting pair (Arg 755 - Tyr 642) in PDB ID 1K8T_A is shown in Figure 2.

Figure 1: Cation-π interacting pairs in exotoxins.

Figure 2: Cation-π interaction between R755 and Y642 in Bacillus anthracis. [PDB ID -1K8T_A]

Table 2: Occurrence of cation-π residues in exotoxins.

The energetic contribution from Arg-Trp is higher when compared with other interacting pairs and the energetic contribution from Lys-Phe is minimal. The energetic contributions from each cation-π interaction in exotoxins are represented in Table 1. Thus in the context of energetic contribution, the role of Arg-Trp pairs might be important. The highest cation-π energetic contribution [-10.51 Kcal/mol] is with the Arg 243 and Trp 257 (PDB ID 1ZM3_A) and the lowest [-1.16 Kcal/mol] is with Lys 716 and Phe 712 (PDB ID K93_A). The average cation-π interaction energy per interaction is around- 4.70 Kcal/mol and there is an average of-18.80 Kcal/mol cation-π interaction energy per protein in exotoxins. Thus cation-π interactions might play an important role in the structural stability of exotoxins in addition to hydrogen bonds and other covalent interactions.

Sequential distance
The overall three dimensional arrangement of all atoms in a protein is referred to as the tertiary structure. The term “secondary structure” refers to the spatial arrangement of amino acid residues that are adjacent in the primary structure (stabilized by short range interactions); tertiary structure includes longer-range aspects of amino acid sequence. Amino acids that are far apart in the polypeptide sequence and that reside in different types of secondary structure may interact within the completely folded structure of a protein. The location of bends in the polypeptide chain and the direction and angle of these bends are determined by the number and location of specific bend producing residues. Interacting segments of polypeptide chains are held in their characteristic tertiary positions by different kinds of non-covalent interactions between the segments (Nelson and Cox, 2005). Hence, we computed the sequential distance of cation-π interacting residues in exotoxins to determine the role of these interactions in the protein secondary and tertiary structures. The computed sequential distance results are depicted in Figure 3. 59% of the cation-π interacting residues are in long-range contacts and thus these interactions might contribute significantly to the stabilization of the native structure of the protein molecule and might help in maintaining the optimal conformation during binding of extoxins to cellular receptors in host cells. Hence any structural stability studies on the native protein molecule in exotoxins should also take cation-π interactions into consideration along with hydrogen bonds and other stabilizing interactions.

Figure 3: Sequential distance between cation-π interacting pairs.

Solvent accessibility patterns
An interesting question concerns the location of cation-π interactions within protein structures. Cationic residues generally prefer to be on the surface of proteins whereas aromatic amino acids prefer to remain in the hydrophobic core. Because a cation- π interaction contains both a cation and an aromatic, it is not clear whether the interacting pairs should prefer to be located on the surfaces of proteins or in the cores (Gallivan and Dougherty, 1999). Solvent accessibility determines the importance of local versus non-local interactions along the protein sequence (Gilis and Rooman, 1996; Gilis and Rooman, 1997). Solvent accessibility is also an important parameter in determining the structural stability of a protein molecule (Gilis and Rooman, 1997). Hence, we carried a systematic analysis of the solvent accessibility patterns for the cation-π interacting residues using DSSP and the results are presented in Figure 4. Our results suggest that most of the residues involved in cation-π interactions prefer to be in the buried regions. These results are consistent with our earlier studies on inflammatory proteins (Anbarasu et al., 2006) but however they are different when compared with the results from conjugated proteins (Anbarasu and Rao, 2007). From our results on the solvent accessibility patterns in exotoxins we suggest that these interactions might stabilize the inner core of the protein molecule and may play an important role in the local conformational stability of exotoxins.

Figure 4: Solvent accessibility patterns in cation-π interacting residues.

Secondary structure preferences
To understand the interactions that confer secondary structural conformational stability in proteins it is important to know the conformational preferences of amino acids. Hence, we did a systematic analysis of the secondary structure preferences of the cation-π interacting residues in exotoxins. The secondary structure preferences are obtained from DSSP and the results are presented in Figure 5. The cation-π interacting residues are found to stabilize both the regular and non-regular secondary structural elements in exotoxins. The helices are predominantly stabilized by cationic interactions with Tyr and Trp, while the coils and sheets are stabilized by cationic interactions with Phe. Hence, the preference of an amino acid to form cation-π interaction in particular secondary structure is not the same as the preference of the amino acid for a particular secondary structure (Malkov et al, 2008). From our results in extoxins we assume that the stabilization patterns of these regular and non-regular secondary structures are dependent on protein type and independent of the amino acid class.

Figure 5: Secondary structure preferences of cation-π interacting residues.

Stabilization centers

Stabilization centers can be defined as clusters of residues that are involved in medium or long range interactions (Dosztányi et al., 1997; Dosztányi et al., 2003). Any residue is considered part of stabilization center if it is involved in medium or long range interactions and if two supporting residues could be selected from both of their flanking tetra peptides, which together with the central residues form at least seven out of the nine possible contacts (Dosztányi et al., 2003). The results of stabilization centers for cation-π interacting residues in the present study are shown in Figure 6. The percentages of cation-π interacting residues with located stabilization centers in exotoxins are 33.33, 24.00, 36.84, 25.00 and 14.28 respectively for Arg, Lys, Phe, Tyr and Trp. From the results observed, we infer that the cation-π interacting residues might contribute additional stability to exotoxins apart from their participation in cation-π interactions.

Figure 6: Stabilization centers of cation-π interacting residues in exotoxins.

Conservation score
Conservation score is a useful parameter for the identification of conserved residues in a protein sequence (Glaser et al., 2003; Landau et al., 2005). The conservation score of cation-π interacting amino acid residues in each bacterial exotoxin studied is computed using the ConSurf program (Glaser et al., 2003; Landau et al., 2005) and the results are depicted in Figure 7. The percentages of cation-π interacting residues above the cut off conservation scores of 6 are 65.22, 53.84, 40.00, 86.67 and 57.15 respectively for Arg, Lys, Phe, Tyr and Trp. From our results we assume that the majority of the residues involved in of cation-π interactions are evolutionarily conserved and might have a significant contribution towards the stability of exotoxins.

Figure 7: Conservation scores of cation-π interacting residues.

Conclusion
Cation-π interactions play an important role in the structural stability of exotoxins. We find that there is an average of four cation-π interactions per protein and also there is an average of one significant one cation-π interaction for every 87 residues in exotoxins studied. The preference of an amino acid to form cation- π interaction in particular secondary structure is not the same as the preference of the amino acid for a particular secondary structure. Cation-π interacting residues might stabilize the inner core of the protein molecule as majority of the residues are in the solvent buried state. The residues involved in cation-π interactions might be evolutionarily conserved as they have a conservation score above the cut off value. Cation-π interacting residues may contribute additional stability to exotoxins apart from their participation in cation-π interactions as they have additional stabilization centers. Majority of the cation-π interactions are in long range and they are important for the tertiary structural stability of exotoxins. On the whole our observations might be useful to understand the stability patterns in bacterial exotoxins and will be a good starting point for researchers working in the field of exotoxin mediated pathogenesis and other related fields of exotoxins.

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