LAUSR

Introduction: Nitriles are the most toxic compounds that can lead to serious human illness through inhalation and consumption due to environmental pollution. Nitrilases can highly degrade nitriles isolated from the natural ecosystem. In the current study, we focused on the discovery of novel nitrilases from a coal metagenome using in silico mining. Methods: Coal metagenomic DNA was isolated and sequenced on the Illumina platform. Quality reads were assembled using MEGAHIT, and statistics were checked using QUAST. Annotation was performed using the automated tool SqueezeMeta. The annotated amino acid sequences were mined for nitrilase from the unclassified organism. Sequence alignment and phylogenetic analyses were carried out using ClustalW and MEGA11. Conserved regions of the amino acid sequences were identified using InterProScan and NCBI-CDD servers. The physicochemical properties of the amino acids were measured using ExPASy’s ProtParam. Furthermore, NetSurfP was used for 2D structure prediction, while AlphaFold2 in Chimera X 1.4 was used for 3D structure prediction. To check the solvation of the predicted protein, a dynamic simulation was conducted on the WebGRO server. Ligands were extracted from the Protein Data Bank (PDB) for molecular docking upon active site prediction using the CASTp server. Results and discussion: In silico mining of annotated metagenomic data revealed nitrilase from unclassified Alphaproteobacteria. By using the artificial intelligence program AlphaFold2, the 3D structure was predicted with a per-residue confidence statistic score of about 95.8%, and the stability of the predicted model was verified with molecular dynamics for a 100-ns simulation. Molecular docking analysis determined the binding affinity of a novel nitrilase with nitriles. The binding scores produced by the novel nitrilase were approximately similar to those of the other prokaryotic nitrilase crystal structures, with a deviation of ±0.5.