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Deep2Pep: A deep learning method in multi-label classification of bioactive peptide

文献类型： 外文期刊

作者： Chen, Lihua 1 ; Hu, Zhenkang 1 ; Rong, Yuzhi 1 ; Lou, Bao 2 ;

作者机构： 1.Shanghai Inst Technol, Sch Perfume & Aroma Technol, Shanghai 201418, Peoples R China

2.Zhejiang Acad Agr Sci, Inst Hydrobiol, Hangzhou 310021, Peoples R China

关键词： Bioactive Peptide; Long short term memory (LSTM); Bidirectional Encoder Representation from; Transformers (BERT); Attention; Multi -label

期刊名称：COMPUTATIONAL BIOLOGY AND CHEMISTRY （ 影响因子：3.1； 五年影响因子：2.7 ）

ISSN： 1476-9271

年卷期： 2024 年 109 卷

页码：

收录情况： SCI

摘要： Functional peptides are easy to absorb and have low side effects, which has attracted increasing interest from pharmaceutical scientists. However, due to the limitations in the laboratory funding and human resources, it is difficult to screen the functional peptides from a large number of peptides with unknown functions. With the development of machine learning and Deep learning, the combination of computational methods and biological information provides an effective method for identifying peptide functions. To explore the value of multifunctional active peptides, a new deep learning method named Deep2Pep (Deep learning to Peptides) was constructed, which was based on sequence encoding, embedding, and language tokenizer. It can achieve predictions of peptides on antimicrobial, antihypertensive, antioxidant and antihyperglycemic by converting sequence information into digital vectors, combined BiLSTM, attention-residual algorithm, and BERT Encoder. The results showed that Deep2Pep had a Hamming Loss of 0.095, subset Accuracy of 0.737, and Macro F1-Score of 0.734. which outperformed other models. BiLSTM played a primary role in Deep2Pep, which BERT encoder was in an auxiliary position. Deep learning algorithms was used in this study to accurately predict the four active functions of peptides, and it was expected to provide effective references for predicting multi-functional peptides.

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