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BioFuelDB: a database and prediction server of enzymes involved in biofuels production

Abstract: 
In light of the rapid decrease in fossils fuel reserves and an increasing demand for energy, novel methods are required to explore alternative biofuel production processes to alleviate these pressures. A wide variety of molecules which can either be used as biofuels or as biofuel precursors are produced using microbial enzymes. However, the common challenges in the industrial implementation of enzyme catalysis for biofuel production are the unavailability of a comprehensive biofuel enzyme resource, low efficiency of known enzymes, and limited availability of enzymes which can function under extreme conditions in the industrial processes. Several studies and databases have reported enzymes that can be used for biofuel production (Choi et al., 2013; Lombard et al., 2014; Misra et al., 2016; Yin et al., 2012). For example, the database of enzymes of microbial biofuel feedstock (dEMBF) provides information related to algal biofuel research but is limited to only 15 sequenced microalgal species and their implication in lipid synthesis (Misra et al., 2016). At present, there is no comprehensive database which is dedicated for retrieving information on biofuel enzymes. Hence, in this work, we have developed BioFuelDB, a comprehensive database of enzymes with demonstrated or potential application(s) in biofuel production by searching the available scientific literature. Using this database, a prediction tool ‘Benz’ was constructed by exploiting the homology-based and profile-based approaches to search for the potential homologs of existing biofuel enzymes. BiofuelDB is freely available at http://metabiosys.iiserb.ac.in/biofueldb and http://metagenomics.iiserb.ac.in/biofueldb.
Author: 
Nikhil Chaudhary, Ankit Gupta, Sudheer Gupta, Vineet K. Sharma
Institution: 
NCBI
Year: 
2017
Input By: 
Christina Eilar
Affiliation: 
MIT
Domains-Issue Area: 
Region(s): 
Industry Focus: 
Extraction & Processing
Manufacturing
Chemical
Energy
Food & Agriculture
Datatype(s): 
Case Studies
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