AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model

Antimicrobial resistance is a growing health concern. Antimicrobial peptides (AMPs) disrupt harmful microorganisms by nonspecific mechanisms, making it difficult for microbes to develop resistance. Accordingly, they are promising alternatives to traditional antimicrobial drugs. In this study, we developed an improved AMP classification model, called AMP-BERT. We propose a deep learning model with a fine-tuned didirectional encoder representations from transformers (BERT) architecture designed to extract structural/functional information from input peptides and identify each input as AMP or non-AMP. We compared the performance of our proposed model and other machine/deep learning-based methods. Our model, AMP-BERT, yielded the best prediction results among all models evaluated with our curated external dataset. In addition, we utilized the attention mechanism in BERT to implement an interpretable feature analysis and determine the specific residues in known AMPs that contribute to peptide structure and antimicrobial function. The results show that AMP-BERT can capture the structural properties of peptides for model learning, enabling the prediction of AMPs or non-AMPs from input sequences. AMP-BERT is expected to contribute to the identification of candidate AMPs for functional validation and drug development. The code and dataset for the fine-tuning of AMP-BERT is publicly available at .

Automated summary

Antimicrobial resistance is a growing health concern, and antimicrobial peptides (AMPs) are considered promising alternatives to traditional antimicrobial drugs because of their nonspecific mechanisms that make it difficult for microbes to develop resistance. In this study, we developed a deep learning model, AMP-BERT, which utilizes the BERT architecture to extract information from input peptides and accurately classify them as AMP or non-AMP. AMP-BERT outperformed other machine/deep learning methods and provided interpretable feature analysis to identify specific residues contributing to peptide structure and antimicrobial function. AMP-BERT is expected to aid in the identification and validation of candidate AMPs for drug development.