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

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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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Funding

This research was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant No: 2016R1E1A1A01940995).

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Author notes

  1. Md. Shahedur Rahman, Md. Saddam Hossain and Subbroto Kumar Saha contributed equally to this work.

Authors and Affiliations

  1. Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh

    Md. Shahedur Rahman, Md. Saddam Hossain & Soikat Rahman

  2. Biological Research Division, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh

    Md. Saddam Hossain

  3. Department of Stem Cell and Regenerative Biotechnology, Konkuk University, 120 Neungdong-Ro, Seoul, 05029, Korea

    Subbroto Kumar Saha

  4. Department of Gynecology and Obstetrics, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA

    Subbroto Kumar Saha

  5. Department of Bioscience, Arctic Research Centre (ARC), Aarhus University, Frederiksborgvej 399, PO Box 358, DK-4000, Roskilde, Denmark

    Christian Sonne

  6. Department of Civil & Environmental Engineering, Hanyang University, 222 Wangsimni-Ro, Seoul, 04763, Korea

    Ki-Hyun Kim

Authors
  1. Md. Shahedur Rahman
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Correspondence to Md. Shahedur Rahman or Ki-Hyun Kim.

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Rahman, M.S., Hossain, M.S., Saha, S.K. et al. Homology Modeling and Probable Active Site Cavity Prediction of Uncharacterized Arsenate Reductase in Bacterial spp.. Appl Biochem Biotechnol 193, 1–18 (2021). https://doi.org/10.1007/s12010-020-03392-w

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