BLSAM-TIP: Improved and robust identification of tyrosinase inhibitory peptides by integrating bidirectional LSTM with self-attention mechanism
Issued Date
2025-01-01
Resource Type
eISSN
19326203
Scopus ID
2-s2.0-105018271435
Pubmed ID
41060986
Journal Title
Plos One
Volume
20
Issue
10
Rights Holder(s)
SCOPUS
Bibliographic Citation
Plos One Vol.20 No.10 (2025) , e0333614
Suggested Citation
Ahmed S., Schaduangrat N., Chumnanpuen P., Mahmud S.M.H., Goh K.O.M., Shoombuatong W. BLSAM-TIP: Improved and robust identification of tyrosinase inhibitory peptides by integrating bidirectional LSTM with self-attention mechanism. Plos One Vol.20 No.10 (2025) , e0333614. doi:10.1371/journal.pone.0333614 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112641
Title
BLSAM-TIP: Improved and robust identification of tyrosinase inhibitory peptides by integrating bidirectional LSTM with self-attention mechanism
Corresponding Author(s)
Other Contributor(s)
Abstract
Tyrosinase plays a central role in melanin biosynthesis, and its dysregulation has been implicated in the pathogenesis of various pigmentation disorders. The precise identification of tyrosinase inhibitory peptides (TIPs) is critical, as these bioactive molecules hold significant potential for therapeutic and cosmetic applications, including the treatment of hyperpigmentation and the development of skin-whitening agents. To date, computational methods have received significant attention as a complement to experimental methods for the in silico identification of TIPs, reducing the need for extensive material resources and labor-intensive processes. In this study, we propose an innovative computational approach, BLSAM-TIP, which combines a bidirectional long short-term memory (BiLSTM) network and a self-attention mechanism (SAM) for accurate and large-scale identification of TIPs. In BLSAM-TIP, we first employed various multi-source feature embeddings, including conventional feature encodings, natural language processing-based encodings, and protein language model-based encodings, to encode comprehensive information about TIPs. Secondly, we integrated these feature embeddings to enhance feature representation, while a feature selection method was applied to optimize the hybrid features. Thirdly, the BiLSTM-SAM architecture was specially developed to highlight the crucial features. Finally, the features from BiLSTM-SAM was fed to deep neural networks (DNN) in order to identify TIPs. Experimental results on an independent test dataset demonstrate that BLSAM-TIP attains superior predictive performance compared to existing methods, with a balanced accuracy of 0.936, MCC of 0.922, and AUC of 0.988. These results indicate that this new method is an accurate and efficient tool for identifying TIPs. Our proposed method is available at https://github.com/saeed344/BLSAM-TIP for TIP identification and reproducibility purposes.