Homology Modeling and Probable Active Site Cavity Prediction of Uncharacterized Arsenate Reductase in Bacterial spp.
Active site
Bioinformatics
Bioremediation
Predicted model
arsC gene
Journal
Applied biochemistry and biotechnology
ISSN: 1559-0291
Titre abrégé: Appl Biochem Biotechnol
Pays: United States
ID NLM: 8208561
Informations de publication
Date de publication:
Jan 2021
Jan 2021
Historique:
received:
04
04
2020
accepted:
16
07
2020
pubmed:
19
8
2020
medline:
27
5
2021
entrez:
19
8
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32809107
doi: 10.1007/s12010-020-03392-w
pii: 10.1007/s12010-020-03392-w
doi:
Substances chimiques
Bacterial Proteins
0
Arsenate Reductases
EC 1.20.-
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-18Subventions
Organisme : National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning
ID : Grant No: 2016R1E1A1A01940995
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