Discovering New Tyrosinase Inhibitors by Using In Silico Modelling, Molecular Docking, and Molecular Dynamics

Abstract

Background/Objectives: This study was used in silico modelling to search for potential tyrosinase protein inhibitors from a database of different core structures for IC₅₀ prediction. Methods: Four machine learning algorithms and topographical descriptors were tested for model construction. Results: A model based on multiple linear regression was the most robust, with only six descriptors, and validated by the Tropsha test with statistical parameters R² = 0.8687, Q²_LOO = 0.8030, and Q²_ext = 0.9151. From the screening of FDA-approved drugs and natural products, the pIC₅₀ values for 15,424 structures were calculated. The applicability domain analysis covered 100% of the external dataset and 71.22% and 73.26% of the two screening datasets. Fifteen candidates with pIC₅₀ above 7.6 were identified, with five structures proposed as potential tyrosinase enzyme inhibitors, which underwent ADME analysis. Conclusions: The molecular docking analysis was performed for the dataset used in the training-test process and for the fifteen structures from the screening dataset with potential pharmaceutical tyrosinase inhibition, followed by molecular dynamics studies for the top five candidates with the highest predicted pIC₅₀ values. The new use of these five candidates in tyrosinase inhibition is highlighted based on their promising application in melanoma treatment.

Keywords: QSAR; melanoma; molecular docking; molecular dynamics; tyrosinase.

Figure 1

The dataset was divided into 10 groups using Ward’s method to divide it into blue bars (training 74%) and black bars (test set 26%).

Figure 2

BOILED−Egg diagram for the model data set (a), and for the selected compounds from the screening data predicted (b).

Figure 3

Two−dimensional structures of the five best compounds obtained in the screening.

Figure 4

BOILED−Egg diagram for compounds 1S, 3S, 4S, and 5S.

Figure 5

Root mean square deviation (RMSD) of the plots of the screening ligands, compounds 1S to 5S (a), and RMSD of the protein (b), throughout the 200 ns molecular dynamics (MD) simulation.

Figure 6

Plots of the RMSFs of the screening ligands during the 200 ns MD simulation.

Figure 7

Number of hydrogen bonds between the enzyme tyrosinase and the screening ligands 1S to 5S, investigated as drug candidates over a 200 ns molecular dynamics simulation.

Figure 8

HB over time of compounds 1S to 5S.

Figure 9

Workflow for the study of the inhibitory capacity of organic compounds on the enzyme tyrosinase by QSAR modelling, docking and molecular dynamics.