TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides.

BACKGROUND: Tyrosinase is an enzyme involved in melanin production in the skin. Several hyperpigmentation disorders involve the overproduction of melanin and instability of tyrosinase activity resulting in darker, discolored patches on the skin. Therefore, discovering tyrosinase inhibitory peptides (TIPs) is of great significance for basic research and clinical treatments. However, the identification of TIPs using experimental methods is generally cost-ineffective and time-consuming. RESULTS: Herein, a stacked ensemble learning approach, called TIPred, is proposed for the accurate and quick identification of TIPs by using sequence information. TIPred explored a comprehensive set of various baseline models derived from well-known machine learning (ML) algorithms and heterogeneous feature encoding schemes from multiple perspectives, such as chemical structure properties, physicochemical properties, and composition information. Subsequently, 130 baseline models were trained and optimized to create new probabilistic features. Finally, the feature selection approach was utilized to determine the optimal feature vector for developing TIPred. Both tenfold cross-validation and independent test methods were employed to assess the predictive capability of TIPred by using the stacking strategy. Experimental results showed that TIPred significantly outperformed the state-of-the-art method in terms of the independent test, with an accuracy of 0.923, MCC of 0.757 and an AUC of 0.977. CONCLUSIONS: The proposed TIPred approach could be a valuable tool for rapidly discovering novel TIPs and effectively identifying potential TIP candidates for follow-up experimental validation. Moreover, an online webserver of TIPred is publicly available at http://pmlabstack.pythonanywhere.com/TIPred .

Computer-Aided Virtual Screening and In Vitro Validation of Biomimetic Tyrosinase Inhibitory Peptides from Abalone Peptidome.

Hyperpigmentation is a medical and cosmetic problem caused by an excess accumulation of melanin or the overexpression of the enzyme tyrosinase, leading to several skin disorders, i.e., freckles, melasma, and skin cancer. Tyrosinase is a key enzyme in melanogenesis and thus a target for reducing melanin production. Although abalone is a good source of bioactive peptides that have been used for several properties including depigmentation, the available information on the anti-tyrosinase property of abalone peptides remains insufficient. This study investigated the anti-tyrosinase properties of Haliotis diversicolor tyrosinase inhibitory peptides (hdTIPs) based on mushroom tyrosinase, cellular tyrosinase, and melanin content assays. The binding conformation between peptides and tyrosinase was also examined by molecular docking and dynamics study. KNN1 showed a high potent inhibitory effect on mushroom tyrosinase with an IC50 of 70.83 µM. Moreover, our selected hdTIPs could inhibit melanin production through the reductions in tyrosinase activity and reactive oxygen species (ROS) levels by enhancing the antioxidative enzymes. RF1 showed the highest activity on both cellular tyrosinase inhibition and ROS reduction. leading to the lower melanin content in B16F10 murine melanoma cells. Accordingly, it can be assumed that our selected peptides exhibited high potential in medical cosmetology applications.

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

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.