Extending the computational and experimental analysis of lipase active site selectivity

Area of Science:

• Biocatalysis
• Computational Chemistry
• Green Chemistry

Background:

• Enzyme-substrate interactions are crucial for developing valuable products from oils and fats.
• Molecular docking is a key computational tool for predicting these interactions.
• Efficient biocatalyst selection is vital for sustainable chemical synthesis.

Purpose of the Study:

• To utilize molecular docking for screening lipases for butyl ester production.
• To evaluate the catalytic performance of Burkholderia cepacia (BCL), Porcine pancreatic (PPL), and Candida rugosa (CRL) lipases.
• To optimize the solvent-free esterification of Licuri oil free fatty acids with butanol.

Main Methods:

• In silico molecular docking simulations were performed on three commercial lipase preparations.
• Lipase-substrate interactions were analyzed, focusing on lauric acid, the primary fatty acid in Licuri oil.
• Experimental esterification reactions were conducted to validate docking predictions.

Main Results:

• Molecular docking predicted BCL and CRL as highly efficient, with BCL showing superior performance.
• BCL demonstrated the highest catalytic activity (1294.83 μmol/h.mg) in butyl ester synthesis.
• PPL exhibited the lowest efficiency due to preferential interaction with minor fatty acids.

Conclusions:

• Molecular docking effectively screened lipases, identifying BCL as the optimal biocatalyst for butyl ester production from Licuri oil.
• The study highlights the utility of molecular docking in selecting efficient enzymes for sustainable chemical processes.
• BCL's high performance supports its application in producing biolubricants and other valuable esters.