Open access peer-reviewed chapter

Identification of Functional Diversity in the Enolase Superfamily Proteins

By Kaiser Jamil and M. Sabeena

Submitted: November 8th 2010Reviewed: June 8th 2011Published: September 2nd 2011

DOI: 10.5772/20250

Downloaded: 1434

1. Introduction

The Escherichia coli K12 genome is a widely studied model system. The members of the Enolase superfamily encoded by E.coli catalyze mechanistically diverse reactions that are initiated by base-assisted abstraction of the α-proton of a carboxylate anion substrate to form an enodiolate intermediate (Patricia C,1996). Six of the eight members of the Enolase superfamily encoded by the Escherichia coli K-12 genome have known functions (John F, 2008). The members share a conserved tertiary structure with a two-domain architecture, in which three carboxylate ligands for the Mg2+ ion as well as the acid/base catalysts are located at the C-terminal ends of the β-strands in a (β/α)7β-barrel [modified (β/α)8- or TIM-barrel] domain and the specificity-determining residues are located in an N-terminal α+β capping domain.

The rapid accumulation of data has led to an extraordinary problem of redundancy, which must be confronted in almost any type of statistical analysis. An important goal of bioinformatics is to use the vast and heterogeneous biological data to extract patterns and make discoveries that bring to light the ‘‘unifying’’ principles in biology. (Kaiser Jamil, 2008)Because these patterns can be obscured by bias in the data, we approach the problem of redundancy by appealing to a well known unifying principle in biology, evolution. Bioinformatics has developed as a data-driven science with a primary focus on storing and accessing the vast and exponentially growing amount of sequence and structure data (Gerlt JA, 2005)

Protein sequences and their three-dimensional structures are successful descendants of evolutionary process. Proteins might have considerable structural similarities even when no evolutionary relationship of their sequences can be detected (Anurag Sethi, 2005). This property is often referred to as the proteins sharing only a ‘‘fold”. Of course, there are also sequences of common origin in each fold, called a ‘‘superfamily”, and in them groups of sequences with clear similarities, are designated as ‘‘family”.

The concept of protein superfamily was introduced by Margaret Dayholff in the 1970 and was used to partition the protein sequence databases based on evolutionary consideration (Lindahl E, 2000). The objective of this study was to analyse the functional diversity of the enolase gene superfamily. The gene superfamily consisting of twelve genes possess enzymatic functions such as L-Ala-D/L-Glu epimerase, Glucarate dehydratase, D-galactarate dehydratase, 2-hydroxy-3-oxopropionate reductase,].o-succinylbenzoate synthase, D-galactonate dehydratase,[12]. 5-keto-4-deoxy-D-glucarate aldolase, L-rhamnonate dehydratase, 2-keto-3-deoxy-L-rhamnonate aldolase, Probable galactarate transporter, and Probable glucarate transporter (Steve EB,1998)

This study was carried out to determine the Probable glucarate transporter (D-glucarate permease) features relating enolase superfamily sequences to structural hinges, which is important for identifying domain boundaries, and designing flexibility into proteins functions also helps in understanding structure-function relationships.

2. Methodology

Flowchart represents the materials and methods

2.1. UniProt KB for genomic sequence analysis

Enolase sequence from E.coli formed the basis for this study. The protein sequences were derived from UniProt KB, we found twelve sequences (Table 1). Most of the sequences in UniProt KB were derived from the conceptual translation of nucleotide sequences. The advantage of using UniProt KB was that it provides a stable, comprehensive, freely accessible central resource on protein sequences and functional annotation. UniProt comprises of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. We used this knowledge based computational analysis which helps for the functional annotation for the gene sequences shown below:

Table 1.

Enolase Sequences from E.coli –K12 Strain (from UNIPROT-KB in Fasta format)

2.2. BLAST program for sequence analysis and alignment

Basic Local Alignment Search Tool (BLAST) is one of the most heavily used sequence analysis tools we have used to perform Sequence Analysis and Alignment. BLAST is a heuristic that finds short matches between two sequences and attempts to start alignments. In addition to performing alignments, BLAST provides statistical information to help decipher the biological significance of the alignment as ‘expect’ value. (Scott McGinnis, 2004). Using this BLAST program the twelve gene sequences were aligned against archaea and bacteria. The sequences were sorted out according to the existing gene names with similarity and the fused genes were removed.

2.3. Clustal W program for multiple sequence alignment

Multiple sequence alignments are widely acknowledged to be powerful tools in the analysis of sequence data.( Sabitha Kotra et al 2008) Crucial residues for activity and for maintaining protein secondary and tertiary structures are often conserved in sequence alignments. Hence, multiple sequence alignment was done for all the enolase gene sequences based on the ClustalW algorithm using the tool BioEdit software program. We determined the alignments which is the starting points for evolutionary studies. Similarity is a percentage sequence match between nucleotide or protein sequences. The basic hypothesis involved here was that similarity relates to functionality, if two sequences are similar, they will have related functionalities.

Realigned the obtained Multiple Sequence Alignments (MSA) using ClustalW (Muhummad Khan and Kaiser Jamil, 2010). Using MSA we could obtain high score for the conserved regions, compared to the reported query sequences. So we viewed the multiple alignment result using a program ‘Jalview’ which improved the multiple alignment. With this program we could extract and get the complete alignment of all sequences for realigning to the query sequence to get better results (Fig. 1). Jalview is a multiple alignment editor written in Java. It is used widely in a variety of web pages which is available as a general purpose alignment editor. The image below shows the result when Jalview has taken the full length sequences and realigned them (using Clustalw) to the query sequence. The alignment has far fewer gaps and more similarities to the entire portion of the query sequences.

Figure 1.

Multiple Sequence Alignment as shown in Jalview

2.4. SCI –PHY server for superfamily and subfamily prediction

Using SCI-PHY server we found subfamilies/subclasses present in the aligned sequences, which merged into five groups. The corresponding pattern for each group of subfamily sequences was found by using ScanProsite and PRATT. A low-level simple pattern-matching application can prove to be a useful tool in many research settings (Doron Betel, 2000). Many of these applications are geared toward heuristic searches where the program finds sequences that may be closely related to the query nucleotide/protein sequences.

2.5. ConSurf server for conservation analysis

For each subfamily sequences the corresponding PDB ID using ConSurf Server was determined. ConSurf-DB is a repository of ConSurf Server which used for evolutionary conservation analysis of the proteins of known structures in the PDB. Sequence homologues of each of the PDB entries were collected and aligned using standard methods. The algorithm behind the server takes into account the phylogenetic relations between the aligned proteins and the stochastic nature of the evolutionary process explicitly. The server assigned the conservation level for each position in the multiple sequence alignment (Ofir Goldenberg, 2002). Identified specific pattern for each of the FASTA format sequence from PDB files using ScanProsite and some of the key residues that comprise the functionally important regions of the protein (Ofir Goldenberg, 2002). We determined the residues present in each of PDB files denoting subfamilies using Swiss PDB Viewer. Mapped out all the residues in color with the help of Rasmol by finding the specific pattern.

3. Results and discussion

This study is an attempt to determine the functional diversity in enolase superfamily protein. The approach we used is a all pairwise alignment of the sequences followed by a clustering of statistically significant pairs into groups or subfamilies by making sure that there is a common motif holding all the members together. Multiple sequence alignment and pattern recognition methods were included in this. The study analyzed the possible subfamilies in Enolase protein superfamily which shares in organisms such as archaea, bacteria with respect to E.coli and finally predicted five superfamilies which may play a role in functional diversity in Enolase superfamily protein.

Generally a protein’s function is encoded within putatively functional signatures or motifs that represent residues involved in both functional conservation and functional divergence within a set of homologous proteins at various levels of hierarchy that is, super-families, families and sub-families. Protein function divergence is according to local structural variation around the active sites (Changwon K, 2006). Even when proteins have similar overall structure, the function could be different from each other. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design. Proteins might have considerable structural similarities even when no evolutionary relationship of their sequences can be detected. This property is often referred to as the proteins sharing ie; a ‘‘fold”. Of course, there are also sequences of common origin in each fold, called a ‘‘superfamily”, and in them there are groups of sequences with clear similarities designated as ‘‘family”. These sequence-level superfamilies can be categorized with many Bioinformatics approaches (LevelErik L, 2002)

3.1. Functional/ structural validation

The functions of the five identified protein family include:

3.1.1. Group 1

Mandelate racemase / muconate lactonizing enzyme family signature-1: which is an independent inducible enzyme cofactor. Mandelate racemase (MR) and muconate lactonizing enzyme (MLE) catalyses separate and mechanistically distinct reactions necessary for the catabolism of aromatic acids Immobilization of this enzyme leads to an enhanced activity and facilitates its recovery

MR_MLE_1 Mandelate racemase / muconate lactonizing enzyme family signature 1: (Fig.2)

Polymer: 1

Type: polypeptide(L)

Length: 405 Chains:A, B, C, D, E, F, G, H

Functional Protein: PDB ID: 3D46 chain A in E-val 0.0.

Possible amino acid pattern found in chain A

I-x(1,3)-Q-P-D-[ALV]-[ST]-H-[AV]-G-G-I-[ST]-E-x(2)-K-[IV]-A-[AGST]-[LM]-A-E-[AS]-[FY]-D-V-[AGT]-[FLV]-[AV]-[LP]-H-C-P-L-G-P-[IV]-A-[FL]-A-[AS]-[CS]-L-x-[ILV]-[DG]Key Residues

THR 136, SER 138, CYS 139,VAL 140, Asp 141, ALA 143, LEU 144, ASP 146, LEU 147, GLY 149, LYS 150, PRO 155, VAL 156, LEU 159, LEU 160, GLY 161

Figure 2.

Functional Protein Information (PDB Id: 3D46) The residues in yellow colour represents the identified functional residues in Group 1

3.1.2. Group 2

TonB-dependent receptor proteins signature-1 : TonB-dependent receptors is a family of beta-barrel proteins from the outer membrane of Gram-negative bacteria. The TonB complex senses signals from outside the bacterial cell and transmits them via two membranes into the cytoplasm, leading to transcriptional activation of target genes

TONB_DEPENDENT_REC_1 TonB-dependent receptor proteins signature 1 : (Fig.3)




Chains:A, B

Functional Protein: PDB ID: 3LAZ

Possible amino acid pattern found in 3LAZ


Key Residues

GLU 88, SER89, VAL91, VAL92, PRO94, GLU95

Figure 3.

Functional Protein Information (PDB Id: 3LAZ). The residues in yellow colour represents the identified functional residues in Group 2

3.1.3. Group 3

3-hydroxyisobutyrate dehydrogenase signature : This enzyme is also called beta-hydroxyisobutyrate dehydrogenase. This enzyme participates in valine, leucine and isoleucine degradation.

3_HYDROXYISOBUT_DH 3-hydroxyisobutyrate dehydrogenase signature : (Fig.4. a and Fig.4. b)




Chains:A, B

Functional Protein: PDB ID: 1YB4

Possible amino acid pattern found in 1YB4


Key Residues

PHE5, ILE6, GLY7, LEU8, GLY 9, GLY 12, ALA 16, ASN 18





Alternate: 3_HYDROXYISOBUT_DH 3-hydroxyisobutyrate dehydrogenase signature :

Functional Protein: PDB ID: 1VPD

Possible amino acid pattern found in1VPD




Key Residues

PHE7, ILE8, GLY9, LEU10, GLY11, GLY14, SER18, ASN20

Figure 4.

a. Functional Protein Information (PDB Id: 1YB4) The residues in yellow colour represents the identified functional residues in Group 3. Also. b Functional Protein Information (PDB Id: 1VPD). The residues in yellow colour represents the identified functional residues in Group 3

3.1.4. Group 4

Enolase signature : Enolase, also known as phosphopyruvate dehydratase, is a metalloenzyme responsible for the catalysis of the conversion of 2-phosphoglycerate (2-PG) to phosphoenolpyruvate (PEP), the ninenth and penultimate step of glycolysis. Enolase can also catalyze the reverse reaction, depending on environmental concentrations of substrates.




Chains:A, B, C, D

Functional Protein: PDB Id: 1E9I

ENOLASE Enolase signature: (Fig.5. a and Fig.5. b)

Possible amino acid pattern found in 1E9I


Key Residues

ILE 338, LEU339, ILE340, LYS341, ASN343, GLN344, ILE 345, GLY346, SER347, LEU348, THR349, GLU350, THR351

Alternate : ENOLASE Enolase signature




Chains:A, B

Functional Protein: PDB ID: 2PA6

Possible amino acid pattern found in 2PA6


Key Residues

LEU 336, LEU337, LEU338, LYS339, ASN341, GLN342, ILE343, GLY344,THR345, LEU 346, SER347, GLU348, ALA 349

Figure 5.

a Functional Protein Information (PDB Id: 1E91) The residues in yellow colour represents the identified functional residues in Group 4. Also. b Functional Protein Information (PDB Id: 2PA6). The residues in yellow colour represents the identified functional residues in Group 4

3.1.5. Group 5

Glycerol-3-phosphate transporter (glpT) family of transporters signature :(Fig.6)

The major facilitator superfamily represents the largest group of secondary membrane transporters in the cell.

Molecule:Glycerol-3-phosphate transporter





Functional Protein: PDB ID: 1PW4

Possible amino acid pattern found in 1PW4


Key Residues

GLU153, ARG154, GLY155, SER159, VAL160, TRP161, ASN162, ALA164, ASN166, VAL167, GLY168, GLY169

Figure 6.

Functional Protein Information (PDB Id: 1PW4). The residues in yellow colour represents the identified functional residues in Group 5

4. Conclusion

Identification of the specificity-determining residues in the various protein family studies has an important role in bioinformatics because it provides insight into the mechanisms by which nature achieves its astonishing functional diversity, but also because it enables the assignment of specific functions to uncharacterized proteins and family prediction. Genomics has posed the challenge of determination of protein function from sequence or 3-D structure. Functional assignment from sequence relationships can be misleading, and structural similarity does not necessarily imply functional similarity. Our studies on the analysis of the superfamily revealed, for the first time, that in these species (archaea and bacteria) using E. coli. as a genomic model, we can contribute important insights for understanding their structural as well as functional relationships. The computational prediction of these functional sites for protein structures provides valuable clues for functional classification.


The authors gratefully acknowledge the support from JNIAS for the successful completion of this project.

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Kaiser Jamil and M. Sabeena (September 2nd 2011). Identification of Functional Diversity in the Enolase Superfamily Proteins, Computational Biology and Applied Bioinformatics, Heitor Silverio Lopes and Leonardo Magalhães Cruz, IntechOpen, DOI: 10.5772/20250. Available from:

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