Decoding transaminase motifs: Tracing the unknown patterns for enhancing the accuracy of computational screening methodologies.

Transaminases, enzymes known for their amino group transfer capabilities, encompass four distinct subfamilies: D-alanine transaminase (DATA), L-selective Branched chain aminotransferase (BCAT), and 4-amino-4-deoxychorismate lyase (ADCL) and R-selective aminotransferase (RATA). RATA enzymes are particularly valuable in biocatalysis for synthesizing chiral amines and resolving racemic mixtures, yet their identification in sequence databases is challenging due to the lack of robust motif-based screening methods. Constructing a sequence dataset of transaminases, and categorizing them to various subfamilies, the conserved motifs are screened over the experimentally known ones, and the novel motifs are explored. Phylogenetic clustering of these subfamilies and structural localization of the identified motifs on the Alphafold-predicted protein models of the representative sequences validate their functional importance. For the ADCL, BCAT, DATA, and RATA datasets, we identified 5, 7, 10, and 2 novel motifs, with 3, 5, 7, and 2 motifs localized on secondary structures, confirming their structural importance. Furthermore, the analysis revealed 1, 3, 2, and 1 unique residue patterns of 293-KxxxR-297; 336-KxxxxY-341, 379-ExxxxNxF-386, and 453-ExFxxGT-459; 187-HxxRL-191, and 284-DxRWxxCDIK-293; and 191-HxxRL-195, integrating of which in the known computational tools would improve their accuracy. The conserved residue pattern or motif-based computational approach for robustly screening the transaminases holds promise for unveiling the novel RATA enzymes, facilitating their exploitation in biocatalytic applications.