Evidence map›Paper›PMID 36228026›Full record

ArticlePloS one2022

In silico functional annotation of hypothetical proteins from the Bacillus paralicheniformis strain Bac84 reveals proteins with biotechnological potentials and adaptational functions to extreme environments.

Md Atikur Rahman, Uzma Habiba Heme, Md Anowar Khasru Parvez

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Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

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0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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  11. Genome-wide identification, and gene expression analysis of NBS-LRR domain containing R genes inPhysiology and molecular biology of plants : an international journal of functional plant biology · 2024
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Md Atikur RahmanInstitute of Microbiology, Friedrich Schiller University Jena, Thuringia, Germany.ORCID 0000-0002-6132-2017
Uzma Habiba HemeFaculty of Biological Sciences, Friedrich Schiller University Jena, Thuringia, Germany.ORCID 0000-0002-5071-0444
Md Anowar Khasru ParvezDepartment of Microbiology, Jahangirnagar University, Savar, Dhaka, Bangladesh.ORCID 0000-0002-0246-3872

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Members of the Bacillus genus are industrial cell factories due to their capacity to secrete significant quantities of biomolecules with industrial applications. The Bacillus paralicheniformis strain Bac84 was isolated from the Red Sea and it shares a close evolutionary relationship with Bacillus licheniformis. However, a significant number of proteins in its genome are annotated as functionally uncharacterized hypothetical proteins. Investigating these proteins' functions may help us better understand how bacteria survive extreme environmental conditions and to find novel targets for biotechnological applications. Therefore, the purpose of our research was to functionally annotate the hypothetical proteins from the genome of B. paralicheniformis strain Bac84. We employed a structured in-silico approach incorporating numerous bioinformatics tools and databases for functional annotation, physicochemical characterization, subcellular localization, protein-protein interactions, and three-dimensional structure determination. Sequences of 414 hypothetical proteins were evaluated and we were able to successfully attribute a function to 37 hypothetical proteins. Moreover, we performed receiver operating characteristic analysis to assess the performance of various tools used in this present study. We identified 12 proteins having significant adaptational roles to unfavorable environments such as sporulation, formation of biofilm, motility, regulation of transcription, etc. Additionally, 8 proteins were predicted with biotechnological potentials such as coenzyme A biosynthesis, phenylalanine biosynthesis, rare-sugars biosynthesis, antibiotic biosynthesis, bioremediation, and others. Evaluation of the performance of the tools showed an accuracy of 98% which represented the rationality of the tools used. This work shows that this annotation strategy will make the functional characterization of unknown proteins easier and can find the target for further investigation. The knowledge of these hypothetical proteins' potential functions aids B. paralicheniformis strain Bac84 in effectively creating a new biotechnological target. In addition, the results may also facilitate a better understanding of the survival mechanisms in harsh environmental conditions.

Indexed as

BacillusAnti-Bacterial AgentsCoenzyme AExtreme EnvironmentsPhenylalanineSugarsAnti-Bacterial AgentsCoenzyme APhenylalanineSugars

Identifiers

PMID36228026
PMCPMC9560612

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.