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A BERT-based approach for identifying anti-inflammatory peptides using sequence information
Teng Xu1, Qian Wang2, Zhigang Yang1
1Institute of Translational Medicine, Baotou Central Hospital, Baotou, China.
Heliyon
|July 11, 2024
Summary
Computational methods accelerate the discovery of anti-inflammatory peptides (AIPs). BertAIP, a BERT-based tool, accurately predicts AIPs from amino acid sequences, aiding drug development for inflammatory diseases.
Area of Science:
- Computational biology
- Drug discovery
- Immunology
Background:
- Anti-inflammatory peptides (AIPs) offer therapeutic potential for inflammatory diseases.
- Experimental identification of AIPs is costly and challenging.
- Computational approaches are emerging as a promising alternative for AIP discovery.
Purpose of the Study:
- To develop a novel computational method, BertAIP, for predicting anti-inflammatory peptides (AIPs).
- To evaluate BertAIP's performance against existing methods for AIP prediction.
- To enhance the interpretability of the prediction model.
Main Methods:
- Utilized a bidirectional encoder representation from transformers (BERT) model for feature extraction from amino acid sequences.
- Employed a fully connected feed-forward network for AIP classification.
- Trained and evaluated the model using AIP datasets from the Immune Epitope Database.
Main Results:
- BertAIP achieved an accuracy of 0.751 and a Matthews correlation coefficient of 0.451.
- Performance metrics surpassed those of commonly used prediction methods.
- Independent testing confirmed BertAIP's superiority over existing AIP predictors.
- Identified and visualized key amino acids influencing AIP prediction.
Conclusions:
- BertAIP is an effective tool for predicting anti-inflammatory peptides (AIPs) based solely on amino acid sequences.
- The model demonstrates superior performance compared to current predictors.
- BertAIP can facilitate large-scale screening and the identification of novel AIPs for therapeutic research and drug development in inflammatory diseases.
