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

Autor: Runthala A; Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India; Department of Integrated Research & Development, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India. Electronic address: ashish.runthala@gmail.com., Satya Sri PS; Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India., Nair AS; Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India., Puttagunta MK; Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India., Sekhar Rao TC; Department of Electronics & Communication Engineering, Sri Venkateswara College of Engineering, Tirupati, India., Sreya V; Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India., Sowmya GR; Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India., Reddy GK; Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India.
Jazyk: angličtina
Zdroj: Gene [Gene] 2025 Feb 05; Vol. 936, pp. 149091. Date of Electronic Publication: 2024 Nov 17.
DOI: 10.1016/j.gene.2024.149091
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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