In silico analysis to identify novel amidase enzymes from unclassified bacteria present in coal metagenome

Amidase enzymes are essential for converting amide groups into carboxylic acids, which have a variety of applications in various industries, including food, flavorings, and pharmaceuticals. The purpose of this study is to utilize metagenomic analysis to identify and analyze novel amidase enzymes produced by unclassified bacteria present in the coal. Initially, metagenomic DNA was extracted from coal and sequenced using the Illumina platform. Analysis of the annotated coal metagenomic data revealed two novel amidase enzymes from microorganisms belonging to the unclassified Oxalobacteraceae family and the unclassified Betaproteobacteria class. These enzymes belong to the amidase signature family, as identified using the InterProScan database. The three-dimensional (3D) structures of these enzymes were predicted with high per-residue confidence scores of approximately 97.34 and 95.31 using the neural network-based AlphaFold2. Molecular docking analysis suggested caprolactam has the highest binding affinity with the enzymes. Molecular dynamics simulations were performed for 200 ns to assess the protein stability. The modelled protein Amidase 1 and Amidase 2 complexes showed higher binding free energy and better stability when compared with the PDB database amidase structures. Overall, this study demonstrates the identification of novel enzymes from unclassified bacteria belonging to the amidase signature family through in-silico mining of the coal metagenome. Novel amidase enzymes found in the coal metagenome exhibit good stability and high substrate binding affinity compared to the crystal structures in the PDB, indicating potential applications in various industries.