ArticleApplied biochemistry and biotechnology2021
Homology Modeling and Probable Active Site Cavity Prediction of Uncharacterized Arsenate Reductase in Bacterial spp.
Article in Applied biochemistry and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 18 citations in OpenAlex.
- Article
- Structural-functional analysis and molecular characterization of arsenate reductase from3 Biotech · 2023Article
- In silico comparative structural and functional analysis of arsenite methyltransferase from bacteria, fungi, fishes, birds, and mammals.Journal, genetic engineering & biotechnology · 2023Article
- Characterising ATP binding activity of PilB ATPase ofJournal of biosciences · 2023Article
- A novel causative functional mutation in GATA6 gene is responsible for familial dilated cardiomyopathy as supported by in silico functional analysis.Scientific reports · 2022Article
- Identification of Potential Inhibitors of MurD Enzyme ofMolecules (Basel, Switzerland) · 2021Article
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Authors and funding
6 authors at 4 institutions in 4 countries.
Funding
Abstract
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.
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