Homology Modeling and Probable Active Site Cavity Prediction of Uncharacterized Arsenate Reductase in Bacterial spp

Homology Modeling and Probable Active Site Cavity Prediction of Uncharacterized Arsenate Reductase in Bacterial spp

The arsC gene–encoded arsenate reductase is a vital catalytic enzyme for remediation of environmental arsenic (As). Microorganisms containing the arsC gene can convert pentavalent arsenate (As[V]) to trivalent arsenite (As[III]) to be either retained in the bacterial cell or released into the air. T...

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Bibliographic Details
Published in:Applied biochemistry and biotechnology Vol. 193; no. 1; pp. 1 - 18
Main Authors: Rahman, Md. Shahedur, Hossain, Md. Saddam, Saha, Subbroto Kumar, Rahman, Soikat, Sonne, Christian, Kim, Ki-Hyun
Format: Journal Article
Language:English
Published: New York Springer US 01-01-2021
Springer Nature B.V
Subjects:
Amino acid sequence
Amino acids
Arsenate reductase
Arsenates
Arsenic
Arsenite
Bacteria
Biochemistry
Biotechnology
Cavities
Chemistry
Chemistry and Materials Science
Enzymes
Holes
Homology
Lysine
Microorganisms
Reductases
Remediation
Bioremediation
arsC gene
Bioinformatics
Active site
Predicted model
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Summary:The arsC gene–encoded arsenate reductase is a vital catalytic enzyme for remediation of environmental arsenic (As). Microorganisms containing the arsC gene can convert pentavalent arsenate (As[V]) to trivalent arsenite (As[III]) to be either retained in the bacterial cell or released into the air. The molecular mechanism governing this process is unknown. Here we present an in silico model of the enzyme to describe their probable active site cavities using SCFBio servers. We retrieved the amino acid sequence of bacterial arsenate reductase enzymes in FASTA format from the NCBI database. Enzyme structure was predicted using the I-TASSER server and visualized using PyMOL tools. The ProSA and the PROCHECK servers were used to evaluate the overall significance of the predicted model. Accordingly, arsenate reductase from Streptococcus pyogenes , Oligotropha carboxidovorans OM5, Rhodopirellula baltica SH 1, and Serratia ureilytica had the highest quality scores with statistical significance. The plausible cavities of the active site were identified in our examined arsenate reductase enzymes which were abundant in glutamate and lysine residues with 6 to 16 amino acids. This in silico experiment may contribute greatly to the remediation of arsenic pollution through the utilization of microbial species.
ISSN:0273-2289
1559-0291
DOI:10.1007/s12010-020-03392-w