| Literature DB >> 31442709 |
Abstract
Nitrilases, member of nitrilase superfamily catalyse the hydrolysis of different nitriles to corresponding amides and acids. In this article, we demonstrate two-fold computational comparative analysis on coding gene sequences, amino acid sequences, three-dimensional structure of the nitrilases from different species and discovered conserved motifs linked with related species. A large ensemble-based dataset was utilized from bacteria, fungi, plants and animals. Here, we used comparative genomics, motif analyses and Bayesian phylogenetic analyses in combination with structural analyses [molecular dynamics simulation, principal component analysis (PCA), dynamic cross correlation (DCCM), root mean squared inner product (RMSIP), free energy surface (FES)] to investigate the evolution, ecological relationship and structure-function association of nitrilase family. The inferred evolutionary tree displayed nitrilase gene clusters to be shared among bacteria, fungi and plants. Structural analysis revealed that the folding of catalytic sites is similar among species; however, the loop region varies. We provide evidence based on PCA that the nitrilases are clustered into different clades due to variation in side chains. Numerous of significant correlations were found between sequence clades and the structural discriminating properties of nitrilases originating from different species. The results are consistent with the hypothesis that bacterial nitrilases are in ecological and evolutionary relationships with fungi and plants during plant-pathogen interaction to large extent. This compact and detail results also open new dimensions for further studying and improvement of industrially important nitrilase enzymes.Entities:
Keywords: Catalytic motif analysis; Conformational flexibility; Dynamic Cross Correlation (DCCM); Nitrilase; Principal Component Analysis (PCA)
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Year: 2019 PMID: 31442709 DOI: 10.1016/j.compbiolchem.2019.107095
Source DB: PubMed Journal: Comput Biol Chem ISSN: 1476-9271 Impact factor: 2.877