BPFun: a deep learning framework for bioactive peptide function prediction using multi-label strategy by transformer-driven and sequence rich intrinsic information

Bioactive peptides are beneficial or have physiological effects on the life activities of biological organisms. The functions of bioactive peptides are diverse, usually with one or more, so accurately detecting the multiple functions of multi-functional peptides is extremely important. Traditional e...

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Vydáno v:	BMC bioinformatics Ročník 26; číslo 1; s. 187 - 27
Hlavní autoři:	Zhu, Lun, Sun, Hao, Yang, Sen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 21.07.2025 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Accuracy Algorithms Amino Acid Sequence Amino acids Analysis Angiogenesis Antihypertensives Bioactive peptides Bioinformatics Biological activity Biomedical and Life Sciences Biomolecules Classification Computational Biology - methods Computational Biology/Bioinformatics Computational linguistics Computer Appl. in Life Sciences Convolutional neural network Data augmentation Datasets Deep Learning Electric transformers Functionals Identification Identification and classification Identification methods Language processing Life Sciences Machine learning Methods Microarrays Multi-label learning Natural language interfaces Neural networks Peptides Peptides - chemistry Peptides - pharmacology Performance evaluation Physiological aspects Physiological effects Predictions Recurrent neural network Support vector machines Transformer China Puerto Rico Taiwan Multi-label learning Transformer Recurrent neural network Bioactive peptides Convolutional neural network
ISSN:	1471-2105, 1471-2105
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Bioactive peptides are beneficial or have physiological effects on the life activities of biological organisms. The functions of bioactive peptides are diverse, usually with one or more, so accurately detecting the multiple functions of multi-functional peptides is extremely important. Traditional experimental identification methods are time-consuming, laborious and costly. To overcome these problems, we adopt a computational biology approach and propose a new model BPFun based on deep learning, which can predict seven functions including anticancer, antibacterial, antihypertensive and so on. In BPFun, we obtained the features of bioactive peptides from different aspects, including biological and physicochemical features. Meanwhile, adopting data augmentation to solve the problem of data imbalance. We combine convolutional networks of different scales and Bi-LSTM layers to obtain high-level feature vectors of different features. Finally, the prediction performance is improved by combining these fused features and combining the self-attention mechanism and the Bi-LSTM layer. Our experiments show that BPFun based on five types of sequence features significantly improves the prediction performance of bioactive peptides. Experiments on the test dataset showed that BPFun gets the accuracy and absolute truth value of 0.6577 and 0.6573 on the dataset of seven functional classifications and was superior to other methods. Codes and data are available at https://github.com/291357657/BPFun .
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-025-06190-5