Predicting BAHD Acyltransferase Function Through Phylogenetic Analysis and Molecular Docking

Understanding substrate-enzyme interactions is critical for advancing biochemical research and drug development. This study focuses on BAHD enzymes, a family known for acylating various substrates and its extensive expansion in plant genomes, offering a reliable model for studying these interactions. The research aims to address the gap in understanding how specific BAHD enzymes modify substrates. To explore the functional characteristics of BAHD enzymes, a Python script was developed to automate the retrieval of protein sequences from the GenBank database. This script compiled over 200 sequences into a FASTA file, facilitating the construction of a phylogenetic tree to analyze evolutionary relationships among BAHD enzymes. Notably, an enzyme labeled BAHD6 was identified, whose function remained unclear. To investigate BAHD6’s potential substrates, molecular docking was conducted using two models of the enzyme. The preparation included structure refinement so a Python script was created for energy minimization, ensuring the 3D model’s stability for accurate molecular docking simulations. Substrates including coniferyl alcohol, benzyl alcohol, hexanol, and others were examined to predict the enzyme’s substrate specificity. The docking simulations revealed that BAHD6 model 2 shares a substrate range with the characterized enzyme obCAAT2. In particular, both have similar predicted binding affinities for aromatic alcohols. However, BAHD6 may exhibit a unique preference for benzyl alcohol. The results highlight the potential of computational approaches in predicting enzyme functions and guiding experimental investigations.

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