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Article

K-Nearest Neighbor and Random Forest-Based Prediction of Putative Tyrosinase Inhibitory Peptides of Abalone Haliotis diversicolor

1
Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
2
Department of Genetics, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
3
Omics Center for Agriculture, Bioresources, Food and Health, Kasetsart University (OmiKU), Bangkok 10900, Thailand
4
Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
*
Authors to whom correspondence should be addressed.
Academic Editors: Sabina Podlewska, Rita Guedes and Stanisław Jastrzębski
Molecules 2021, 26(12), 3671; https://doi.org/10.3390/molecules26123671
Received: 17 May 2021 / Revised: 6 June 2021 / Accepted: 15 June 2021 / Published: 16 June 2021
Skin pigment disorders are common cosmetic and medical problems. Many known compounds inhibit the key melanin-producing enzyme, tyrosinase, but their use is limited due to side effects. Natural-derived peptides also display tyrosinase inhibition. Abalone is a good source of peptides, and the abalone proteins have been used widely in pharmaceutical and cosmetic products, but not for melanin inhibition. This study aimed to predict putative tyrosinase inhibitory peptides (TIPs) from abalone, Haliotis diversicolor, using k-nearest neighbor (kNN) and random forest (RF) algorithms. The kNN and RF predictors were trained and tested against 133 peptides with known anti-tyrosinase properties with 97% and 99% accuracy. The kNN predictor suggested 1075 putative TIPs and six TIPs from the RF predictor. Two helical peptides were predicted by both methods and showed possible interaction with the predicted structure of mushroom tyrosinase, similar to those of the known TIPs. These two peptides had arginine and aromatic amino acids, which were common to the known TIPs, suggesting non-competitive inhibition on the tyrosinase. Therefore, the first version of the TIP predictors could suggest a reasonable number of the TIP candidates for further experiments. More experimental data will be important for improving the performance of these predictors, and they can be extended to discover more TIPs from other organisms. The confirmation of TIPs in abalone will be a new commercial opportunity for abalone farmers and industry. View Full-Text
Keywords: anti-tyrosinase peptides; bioinformatics; machine learning; random forest; k-nearest neighbor; abalone anti-tyrosinase peptides; bioinformatics; machine learning; random forest; k-nearest neighbor; abalone
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MDPI and ACS Style

Kongsompong, S.; E-kobon, T.; Chumnanpuen, P. K-Nearest Neighbor and Random Forest-Based Prediction of Putative Tyrosinase Inhibitory Peptides of Abalone Haliotis diversicolor. Molecules 2021, 26, 3671. https://doi.org/10.3390/molecules26123671

AMA Style

Kongsompong S, E-kobon T, Chumnanpuen P. K-Nearest Neighbor and Random Forest-Based Prediction of Putative Tyrosinase Inhibitory Peptides of Abalone Haliotis diversicolor. Molecules. 2021; 26(12):3671. https://doi.org/10.3390/molecules26123671

Chicago/Turabian Style

Kongsompong, Sasikarn, Teerasak E-kobon, and Pramote Chumnanpuen. 2021. "K-Nearest Neighbor and Random Forest-Based Prediction of Putative Tyrosinase Inhibitory Peptides of Abalone Haliotis diversicolor" Molecules 26, no. 12: 3671. https://doi.org/10.3390/molecules26123671

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