ПРИКЛАДНАЯ БИОХИМИЯ И МИКРОБИОЛОГИЯ, 2015, том 51, № 1, с. 15-23
УДК 576.85:577.15
APPLICATION OF BIOINFORMATICS TOOLS AND DATABASES IN MICROBIAL DEHALOGENATION RESEARCH (A REVIEW)
© 2015 г. R. Satpathy*, V. B. Konkimalla**, and J. Ratha*
*School of Life Science, Sambalpur University, Jyoti vihar, Burla, Odisha, 768019 India **School of Biological Sciences, National Institute of Science Education and Research (NISER), Bhubaneswar, Odisha,
751005 India e-mail: rnsatpathy@gmail.com Received March 4, 2014
Microbial dehalogenation is a biochemical process in which the halogenated substances are catalyzed enzy-matically in to their non-halogenated form. The microorganisms have a wide range of organohalogen degradation ability both explicit and non-specific in nature. Most of these halogenated organic compounds being pollutants need to be remediated; therefore, the current approaches are to explore the potential of microbes at a molecular level for effective biodegradation of these substances. Several microorganisms with dehalogenation activity have been identified and characterized. In this aspect, the bioinformatics plays a key role to gain deeper knowledge in this field of dehalogenation. To facilitate the data mining, many tools have been developed to annotate these data from databases. Therefore, with the discovery of a microorganism one can predict a gene/protein, sequence analysis, can perform structural modelling, metabolic pathway analysis, biodegradation study and so on. This review highlights various methods of bioinformatics approach that describes the application of various databases and specific tools in the microbial dehalogenation fields with special focus on dehalogenase enzymes. Attempts have also been made to decipher some recent applications of in silico modeling methods that comprise of gene finding, protein modelling, Quantitative Structure Biode-gradibility Relationship (QSBR) study and reconstruction of metabolic pathways employed in dehalogena-tion research area.
DOI: 10.7868/S0555109915010146
Halogenated compounds either obtained naturally from organisms or produced from industry are highly essential in day-to-day life [1, 2]. The propensity for the accumulation of chlorinated aliphatic and aromatic hydrocarbons in the environmental habitats such as groundwater and sediments represents a global ecological danger. For example, dichlorodiphenyltrichlo-roethane, polychlorinated biphenyl, dioxins and halo-genated flame retardants have destructive impact on human health [3, 4]. Many of these substances have also been reported to be persistent in nature [5]. A crucial step in the degradation of organohalides is the cleavage of the carbon-halogen bond known as dehalogenation, that is possessed by a diverse group of microorganisms [6, 7] as described in Fig. 1. The basis of microbial dehalogenation is presence of dehalogenas-es and other allied enzymes that catalyze the cleavage of carbon-halogen bonds [8—10]. Microbiological de-halogenation reactions have immense particular practical interests because of their potential biotechnolog-ical applications in the bioremediation of halogenated environmental pollutants and production of precursor materials in the pharmaceuticals, fine chemical and medical industries [11—13]. Traditionally, many mi-crobial enzymes involved in dehalogenation process have been known for their potential environmental applications but many of them are yet to be studied for
industrial utilization [14, 15]. To carry out successful effort in bioremediation using microbial dehalogena-tion it is essential to obtain deeper understanding at molecular level. Consequently, it creates a worthy cause for application of bioinformatics to this field. Implementation of computational methods in molecular data is used for both mining of the data and development of new database and tools to facilitate the process [16—18]. There is a substantial number of sequence information available in the public database comprising of whole genome, proteome and metagenome information. This bulk of data provides a greater opportunity to annotate and evaluate the novel information associated with them [19, 20].
To make a reasonable prediction in bioinformatics-based analysis one needs integration of huge amounts of different types data such as the chemical structure and reactivity of organic compounds, gene and protein sequence information, enzyme data and a broad environmental microbiology repository [21, 22].
By the advanced molecular biological approach, hundreds of microbial genomes have been sequenced and stored in public database; hence appropriate use of suitable computational tools can be applied to these data to understand better the complex molecular regulation and control of the microorganisms [23—28]. The development of a large number of databases, soft-
Fig. 1. Categories of microbial dehalogenation reactions occurred in nature.
ware tools to analyze data in the past few years has facilitated the researcher for a wide range of applications including to understand the microbial dehalogenation process (Table 1). Recently, the function BT2127 from Bacteroides thetaiotaomicron that was previously unknown was predicted and assigned to haloalkanoate dehalogenase superfamily of inorganic pyrophos-phatase by applying integrated bioinformatics methods. This was further validated by experimental methods using substrate specificity profiling and site-directed mutagenesis [29]. Similarly, another report based on the crystal structure proposed the reaction kinetics and the residues responsible for the enzymatic activity of l-haloacid dehalogenase from a marine Rhodobacteraceae [30].
The present review aims at providing a concise, conceptual and technical constructs for use and application of different existing databases and bioinformat-ics methods to study the microbial dehalogenation process.
Bioinformatics databases for the microbial dehalo-
genation study. The databases are the archives of life science information which are often gathered by the curated data (published literature) and from various experiments like whole genome sequencing, microar-ray analysis, etc. They contain different kinds of information generated from genomics, proteomics, and me-tabolomics studies which upon further experimental support are ultimately taken up by the industry [31, 32]. To illustrate the biocatalytic process like the microbial
dehalogenation, there is a requirement for information from many categories of the databases (Table 2). Especially 4 categories of databases are extremely helpful for this purpose: i) databases of enzymes and metabolic pathways ii) sequence and structure databases iii) databases of molecules and iv) databases of organisms.
Databases of enzymes and metabolic pathways.
There exist many online databases on enzymes and reactions that are freely accessible. These data repositories contain both the synthetic and degradation routes along with the enzymes involved in the metabolic process. The enzymatic database contains the classification of the enzymes, organism sources, the kinetic properties of the corresponding reactants and products. Braunschweig Enzyme Database (BRENDA) is one such database of enzymes that was first developed in 1987 at the former German National Research Centre for Biotechnology (www.brenda-enzymes.org/A It is having the molecular and biochemical information on enzymes from the IUBMB Enzyme Nomenclature List (http://www.enzyme-database.org/). For metabolic data, one of the important databases is the University of Minnesota Biocatalysis/Biodegradation Database (UM-BD), which focuses specifically on the novel enzymes and metabolic pathways useful in environmental and industrial biotechnology (http://umbbd.ethz.ch/). MetaCyc (http://metacyc.org/) is an alternative metabolic pathway database that stores experimentally determined pathways. MetaCyc also serves as a reference data set for computationally predicting the metabolic pathways oforganisms from their sequenced genomes and
Table 1. Example of representative set of some commonly used software to study microbial dehalogenation process
Tools Type of data analysis Availability
PathPred Prediction of metabolic pathway from a query compound http://www.genome.jp/tools/pathpred
Mass-Metasite Automatic identification of metabolites from LC-MS data http://www.moldiscovery.com/soft_mass-meta-site.php
AutoDock Molecular docking study autodock.scripps.edu
CAVER Tunnel detection in dehalogenase enzyme www.caver.cz
GROMACS Molecular dynamics simulation of enzymes www.gromacs.org/
MOPAC Quantum mechanics calculation of organohalide substances openmopac.net
TRITON Rational engineering of enzymes www.ncbr.muni.cz/triton/
MegAlign Gene sequence alignment www.dnastar.com/t-megalign.aspx
MEGA 4 Phylogenetic analysis www.megasoftware.net/mega4
E-zyme Prediction of potential EC numbers from chemical transformation pattern http://www.genome.jp/tools/e-zyme
MetaRouter Identifying the biodegradative pathways for chemical compounds http://pdg.cnb.uam.es/MetaRouter
VLifeMDS QSBR analysis http://www.vlifesciences.com
Molegro Suitable for QSBR analysis, molecular modelling, docking, etc. www.molegro.com
MODELLER Homology modelling of proteins http://salilab.org/modeller
CellDesigner Metabolic pathway reconstruction and simulation www.celldesigner. org
MATLAB Modelling, simulation, artificial intelligence techniques www.mathworks.com
comparing the pathways across different species. The WIT (What Is There) (http://wit.mcs.anl.gov/WIT2/) database system has been designed with a support of comparing the sequenced genomes to generate metabolic reconstructions based on chromosomal sequences and metabolic modules. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a popular and sophisticated online database (www.genome.jp/kegg/) that manages information like genomes, enzymatic pathways, and other biochemical data. Similarly
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