In Silico Analysis of Heavy Metal Assimilation Behaviors in the Genome of Methanosarcina barkeri str. Fusaro

Title:In Silico Analysis of Heavy Metal Assimilation Behaviors in the Genome of Methanosarcina barkeri str. Fusaro

Volume: 10 Issue: 1

Author(s): P. Chellapandi

Affiliation:

Keywords: Cell behavior, heavy metal assimilation, metals detoxification, methanogens, molecular evolution, pathway model, protein function, transporters.

Abstract: A computational systems biology representation was utilized for understanding the molecular and metabolic behaviors of heavy metal assimilation system in the genome Methanosarcina barkeri str. Fusaro. A known functional protein of this system was identified by text mining. A combined functional annotation approach was employed to discover the missing proteins with unknown function. Herein, assimilation systems of cadmium, nickel and copper ions have been predicted by using bioinformatics resources. Metabolic flux balances of each pathway model have been optimized for its cellular behaviors in response to excessive heavy metal ions in the environment. Many heavy metals have been utilized by a typical assimilation system on producing the precursors required for methane biosynthesis, and other energy driven processes of this genome. Metal transporter and accessory proteins of methanogens were phylogenetically corresponded with similar proteins in the members of proteobacteria, but metal-containing enzymes are very exclusive to closely related methanogenic archaea. The evolutionary and metabolic relationships of heavy metal transporter system have been observed among archaea and some members in the proteobacteria. Thus, the metabolic models obtained from this study will optimistically be useful in understanding the heavy metal assimilation mechanism of this genome for producing methane, and applying in bioremediation process.

Cite this article as:

Chellapandi P., In Silico Analysis of Heavy Metal Assimilation Behaviors in the Genome of Methanosarcina barkeri str. Fusaro, Current Bioinformatics 2015; 10 (1) . https://dx.doi.org/10.2174/157489361001150309141416