Bacterial laccases exhibit relatively high optimal reaction temperatures and possess a broad substrate spectrum, thereby expanding the range of potential applications for laccase enzymes. This study aims to investigate the molecular evolution of bacterial laccases using computational simulation tools such as AlphaFold2, Metal3D, AutoDockVina, and Rosetta. We isolated a bacterium with laccase activities from fecal samples from a Hermann´s tortoise ( Testudo hermanni), identified it as Klebsiella michiganensis using 16S rRNA sequencing and nanopore genome sequencing, and then identified a sequence encoding a laccase with a predicted molecular weight of approximately 27.5 kDa. Expression of the corresponding, chemically synthesized DNA fragment resulted in the isolation of an active laccase. The enzyme showed considerable thermostability, retaining 21% of its activity after boiling for 30 min. Using state-of-the-art information technology and machine learning techniques, we conducted simulations on this sequence, predicted its copper-ion binding sites, and validated these predictions through site-directed mutagenesis and expression. Subsequently, we performed computer-aided evolution studies on this sequence and found that 90% of the results from simulations exhibited improved performance. In summary, this study not only revealed a novel laccase but also demonstrated an efficient approach for advancing research on the molecular evolution of bacterial laccases using cutting-edge machine learning, next-generation sequencing, traditional bioinformatics approaches, and laboratory techniques, providing an effective strategy for discovering and design new bacterial laccases.
