Unlocking the microbial studies through computational approaches: how far have we reached?
Rajnish KumarGarima YadavMohammed KuddusGhulam Md AshrafRachana SinghPublished in: Environmental science and pollution research international (2023)
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.
Keyphrases
- artificial intelligence
- machine learning
- deep learning
- nucleic acid
- big data
- microbial community
- molecular docking
- mass spectrometry
- single cell
- genome wide
- staphylococcus aureus
- circulating tumor
- convolutional neural network
- molecular dynamics simulations
- bioinformatics analysis
- escherichia coli
- single molecule
- drinking water
- electronic health record
- adverse drug
- binding protein
- life cycle
- dna methylation
- amino acid
- gene expression
- copy number
- emergency department
- optical coherence tomography
- risk assessment
- health information
- genome wide identification
- small molecule
- genetic diversity