Resolution of physics and deep learning-based protein engineering filters: A case study with a lipase for industrial substrate hydrolysis.

ABSTRACT: Computational enzyme design remains a powerful yet imperfect tool for optimizing biocatalysts, especially when targeting non-natural substrates. Using design tools we investigated Pseudomonas aeruginosa LipA, a lipase with a flexible lid domain crucial for substrate binding and turnover, aiming to enhance its hydrolysis of the industrially relevant substrate Roche ester. We generated an initial set of single-point mutations based on structural proximity to the active site and evaluated their effects using a computational pipeline integrating molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and ensemble-based energy scoring. While we identified several active variants, attempts to rank them by activity using structural features, such as hydrogen bond format

SUBMITTER: Gardiner S 

PROVIDER: S-EPMC12431253 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature