Monophenolase assay using excitation-emission matrix fluorescence and ELMAN neural network assisted by whale...

Area of Science:

• Biochemistry
• Analytical Chemistry
• Computational Biology

Background:

• Tyrosinase is key in melanogenesis, with essential monophenolase and diphenolase activities.
• A real-time assay for monophenolase activity is critical for research and industry.
• Existing methods may face interference from diphenolase activity.

Purpose of the Study:

• To develop a novel, real-time assay for quantifying monophenolase activity.
• To accurately determine enzyme kinetics and screen inhibitors without diphenolase interference.
• To establish a robust analytical system using fluorescence spectroscopy and artificial intelligence.

Main Methods:

• Combined three-dimensional excitation-emission matrix (EEM) fluorescence spectra with artificial neural networks (ELMAN).
• Utilized Principal Component Analysis (PCA) for spectral data dimensionality reduction.
• Employed Whale Optimization Algorithm (WOA) for neural network parameter optimization.

Main Results:

• Developed a quantitation system for tyrosine in the presence of l-DOPA.
• Achieved a limit of detection (LOD) of 0.0113 U mL⁻¹ for monophenolase.
• Determined kinetic parameters (Km = 14.16 μM) and screened an inhibitor (kojic acid, IC50 = 3.49 μM).

Conclusions:

• The developed assay accurately and selectively measures monophenolase activity.
• The method provides a promising platform for characterizing monophenolase kinetics and identifying inhibitors.
• This approach offers a valuable tool for both scientific investigation and industrial applications in melanogenesis research.