Extending the computational and experimental analysis of lipase active site selectivity.

Molecular docking is an important computational analysis widely used to predict the interaction of enzymes with several starting materials for developing new valuable products from several starting materials, including oils and fats. In the present study, molecular docking was used as an efficient in silico screening tool to select biocatalysts with the highest catalytic performance in butyl esters production in a solvent-free system, an eco-friendly approach, via direct esterification of free fatty acids from Licuri oil with butanol. For such purpose, three commercial lipase preparations were used to perform molecular docking studies such as Burkholderia cepacia (BCL), Porcine pancreatic (PPL), and Candida rugosa (CRL). Concurrently, the results obtained in BCL and CRL are the most efficient in the esterification process due to their higher preference for catalyzing the esterification of lauric acid, the main fatty acid found in the licuri oil composition. Meanwhile, PPL was the least efficient because it preferentially interacts with minor fatty acids. Molecular docking with the experimental results indicated the better performance in the synthesis of esters was BCL. In conclusion, experimental results analysis shows higher enzymatic productivity in esterification reactions of 1294.83 µmol/h.mg, while the CRL and PPL demonstrated the lowest performance (189.87 µmol / h.mg and 23.96 µmol / h.mg, respectively). Thus, molecular docking and experimental results indicate that BCL is a more efficient lipase to produce fatty acids and esters from licuri oil with a high content of lauric acid. In addition, this study also demonstrates the application of molecular docking as an important tool for lipase screening to achieve more sustainable production of butyl esters with a view synthesis of biolubricants.

Experimental and Computational Analysis of Synthesis Conditions of Hybrid Nanoflowers for Lipase Immobilization.

This work presents a framework for evaluating hybrid nanoflowers using Burkholderia cepacia lipase. It was expanded on previous findings by testing lipase hybrid nanoflowers (hNF-lipase) formation over a wide range of pH values (5-9) and buffer concentrations (10-100 mM). The free enzyme activity was compared with that of hNF-lipase. The analysis, performed by molecular docking, described the effect of lipase interaction with copper ions. The morphological characterization of hNF-lipase was performed using scanning electron microscopy. Fourier Transform Infrared Spectroscopy performed the physical-chemical characterization. The results show that all hNF-lipase activity presented values higher than that of the free enzyme. Activity is higher at pH 7.4 and has the highest buffer concentration of 100 mM. Molecular docking analysis has been used to understand the effect of enzyme protonation on hNF-lipase formation and identify the main the main binding sites of the enzyme with copper ions. The hNF-lipase nanostructures show the shape of flowers in their micrographs from pH 6 to 8. The spectra of the nanoflowers present peaks typical of the amide regions I and II, current in lipase, and areas with P-O vibrations, confirming the presence of the phosphate group. Therefore, hNF-lipase is an efficient biocatalyst with increased catalytic activity, good nanostructure formation, and improved stability.

Computational and experimental analysis on the preferential selectivity of lipases for triglycerides in Licuri oil.

In the present study, we demonstrated the use of molecular docking as an efficient in silico screening tool for lipase-triglyceride interactions. Computational simulations using the crystal structures from Burkholderia cepacia lipase (BCL), Thermomyces lanuginosus lipase (TLL), and pancreatic porcine lipase (PPL) were performed to elucidate the catalytic behavior with the majority triglycerides present in Licuri oil, as follows: caprilyl-dilauryl-glycerol (CyLaLa), capryl-dilauryl-glycerol (CaLaLa), capryl-lauryl-myristoyl-glycerol (CaLaM), and dilauryl-myristoyl-glycerol (LaLaM). The computational simulation results showed that BCL has the potential to preferentially catalyze the major triglycerides present in Licuri oil, demonstrating that CyLaLa, (≈25.75% oil composition) interacts directly with two of the three amino acid residues in its catalytic triad (Ser87 and His286) with the lowest energy (-5.9 kcal/mol), while other triglycerides (CaLaLa, CaLaM, and LaLaM) interact with only one amino acid (His286). In one hard, TLL showed a preference for catalyzing the triglyceride CaLaLa also interacting with His286 residue, but, achieving higher binding energies (-5.3 kcal/mol) than found in BCL (-5.7 kcal/mol). On the other hand, PPL prefers to catalyze only with LaLaM triglyceride by His264 residue interaction. When comparing the computational simulations with the experimental results, it was possible to understand how BCL and TLL display more stable binding with the majority triglycerides present in the Licuri oil, achieving conversions of 50.86 and 49.01%, respectively. These results indicate the production of fatty acid concentrates from Licuri oil with high lauric acid content. Meanwhile, this study also demonstrates the application of molecular docking as an important tool for lipase screening to reach a more sustainable production of fatty acid concentrates from vegetable oils.