Unlocking the microbial studies through computational approaches: how far have we reached?

Artificial intelligence Deep learning Machine learning Metagenomics Microbiology

Journal

Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769

Informations de publication

Date de publication:
Apr 2023
Historique:
received: 23 03 2022
accepted: 24 02 2023
medline: 18 4 2023
pubmed: 16 3 2023
entrez: 15 3 2023
Statut: ppublish

Résumé

The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.

Identifiants

pubmed: 36920617
doi: 10.1007/s11356-023-26220-0
pii: 10.1007/s11356-023-26220-0
pmc: PMC10016191
doi:

Substances chimiques

DNA 9007-49-2

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

48929-48947

Informations de copyright

© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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Auteurs

Rajnish Kumar (R)

Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India.
Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA.

Garima Yadav (G)

Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India.

Mohammed Kuddus (M)

Department of Biochemistry, College of Medicine, University of Hail, Hail, Saudi Arabia.

Ghulam Md Ashraf (GM)

Department of Medical Laboratory Sciences, College of Health Sciences, and Sharjah Institute for Medical Research, University of Sharjah, Sharjah , 27272, United Arab Emirates.

Rachana Singh (R)

Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India. rsingh1@lko.amity.edu.

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