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AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model
Hansol Lee1, Songyeon Lee1, Ingoo Lee1
1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea.
Abstract:
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 https://github.com/GIST-CSBL/AMP-BERT.
Insights
Antimicrobial peptides (AMPs) show promise against drug-resistant microbes. A new deep learning model, AMP-BERT, accurately identifies AMPs, aiding drug discovery and development efforts.
Area of Science:
- Biochemistry
- Computational Biology
- Drug Discovery
Background:
- Antimicrobial resistance is a critical global health threat.
- Antimicrobial peptides (AMPs) offer a novel therapeutic strategy due to their unique mechanisms of action.
- Developing effective methods for identifying AMPs is crucial for combating resistance.
Purpose of the Study:
- To develop an advanced computational model for classifying antimicrobial peptides (AMPs).
- To leverage deep learning, specifically a fine-tuned BERT architecture, for enhanced AMP prediction.
- To provide an interpretable analysis of peptide features contributing to antimicrobial activity.
Main Methods:
- Developed AMP-BERT, a deep learning model utilizing a fine-tuned bidirectional encoder representations from transformers (BERT) architecture.
- Trained and evaluated AMP-BERT on a curated dataset, comparing its performance against other machine and deep learning models.
- Employed BERT's attention mechanism for interpretable feature analysis to identify key residues in AMPs.
Main Results:
- AMP-BERT achieved superior prediction accuracy compared to existing models on an external dataset.
- The model effectively captured structural and functional information from peptide sequences.
- Interpretable analysis identified specific amino acid residues critical for AMP structure and function.
Conclusions:
- AMP-BERT demonstrates high efficacy in predicting antimicrobial peptides (AMPs) from sequence data.
- The model's interpretability aids in understanding the structural basis of AMP activity.
- AMP-BERT is a valuable tool for accelerating the discovery and development of novel AMP-based therapeutics.
