Computational Identification of Structural Determinants Associated with Transesterification Activity in Microbial Lipases

Triacylglycerol acylhydrolases (lipases; EC 3.1.1.3) are versatile enzymes capable of catalysing both hydrolysis and synthetic reactions, including transesterification. Their broad catalytic properties make them valuable biocatalysts for various industrial and pharmaceutical applications. However, despite the availability of thousands of annotated lipase sequences in public databases, the functional characteristics and transesterification potential of many lipases remain poorly understood. Identifying the molecular and structural features associated with transesterification activity is therefore important for improving the functional characterization and prioritization of lipases for further investigation.

In the present study, an integrated computational approach was employed to characterize lipases and identify candidates with potential transesterification activity. Curated lipases with available experimental information were collected and analysed to investigate sequence motifs, conserved residues, and structural characteristics. Comparative analysis of representative lipases was performed to identify functionally important residues, substrate-binding features, and structural architectures associated with transesterification activity. These characteristics were subsequently used to investigate and prioritize poorly characterized lipase candidates. Furthermore, a candidate lipase exhibiting favourable sequence and structural characteristics, particularly substrate-binding features, was identified as a promising candidate for further investigation.

Overall, this study demonstrates that the integration of sequence and structural analyses provides an effective computational strategy for characterizing poorly understood lipases and identifying candidates with potential transesterification activity. The findings contribute to a better understanding of sequence–structure–function relationships in lipases and provide a basis for the computational prioritization of promising biocatalysts for subsequent experimental validation and potential industrial applications.