AUTHOR=Achudhan Arunmozhi Bharathi , Kannan Priya , Saleena Lilly M. 

TITLE=Functional metagenomics uncovers nitrile-hydrolysing enzymes in a coal metagenome

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2023.1123902

DOI=10.3389/fmolb.2023.1123902

ISSN=2296-889X

ABSTRACT=Introduction:
Nitriles are the most toxic compounds which might lead to serious ailments for humankind by inhalation and consumption due to environmental pollution. Nitrilases can highly degrade the nitriles isolated from the natural ecosystem. In the current study, we have focused on discovering novel Nitrilases from coal metagenome by in silico mining. 
Methods:
The coal metagenomic DNA was isolated and sequenced in the Illumina platform. The quality reads were assembled using megahit, and statistics were checked using QUAST. The annotation was done using the automated tool SqueezeMeta. The annotated amino acid sequences were mined for Nitrilase from the unclassified organism. Sequence alignment and phylogenetic analysis were carried out using ClustalW and MEGA11. The conserved regions of amino acid sequences were identified using InterProScan and NCBI-CDD servers. Physiochemical properties of amino acids were measured using Expasy’s protparam. Further, NetSurfP was used for 2D structure prediction, while AlphaFold in Chimera X 1.4 was used for the 3D structure prediction. To check the solvation of the predicted protein, a dynamic simulation was conducted in the WEBGRO server. Ligands were extracted from PDB for molecular docking upon active site prediction using the CASTp server. 
Results and Discussion:
In silico mining of annotated metagenomic data consequently, revealed Nitrilase from unclassified Alphaproteobacteria. Using an artificial intelligence developed by AlphaFold, which predicted the 3D structure with a per-residue confidence statistic score of about 95.8% stability of the predicted model was verified with molecular dynamic for 100ns simulation. Molecular docking analysis determined the binding affinity of a novel Nitrilase with nitriles. The binding scores produced by the novel Nitrilase were approximately ± 0.5.