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

Rajnish Kumar, Garima Yadav, Mohammed Kuddus, Ghulam Md Ashraf, Rachana Singh*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)48929-48947
Number of pages19
JournalEnvironmental Science and Pollution Research
Volume30
Issue number17
DOIs
Publication statusPublished - Apr 2023
Externally publishedYes

Keywords

  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Metagenomics
  • Microbiology

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