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AMPpred-MFA: An Interpretable Antimicrobial Peptide Predictor with a Stacking Architecture, Multiple Features, and
Changjiang Li1, Quan Zou1, Cangzhi Jia2
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
Abstract:
Antimicrobial peptides (AMPs) are small molecular polypeptides that can be widely used in the prevention and treatment of microbial infections. Although many computational models have been proposed to help identify AMPs, a high-performance and interpretable model is still lacking. In this study, new benchmark data sets are collected and processed, and a stacking deep architecture named AMPpred-MFA is carefully designed to discover and identify AMPs. Multiple features and a multihead attention mechanism are utilized on the basis of a bidirectional long short-term memory (LSTM) network and a convolutional neural network (CNN). The effectiveness of AMPpred-MFA is verified through five independent tests conducted in batches. Experimental results show that AMPpred-MFA achieves a state-of-the-art performance. The visualization interpretability analyses and ablation experiments offer a further understanding of the model behavior and performance, validating the importance of our feature representation and stacking architecture, especially the multihead attention mechanism. Therefore, AMPpred-MFA can be considered a reliable and efficient approach to understanding and predicting AMPs.
Insights
A new deep learning model, AMPpred-MFA, accurately identifies antimicrobial peptides (AMPs) for infection treatment. This high-performance tool enhances understanding and prediction of these vital therapeutic molecules.
Area of Science:
- Biochemistry and Molecular Biology
- Computational Biology and Bioinformatics
- Infectious Diseases
Background:
- Antimicrobial peptides (AMPs) are crucial for combating microbial infections.
- Existing computational models for AMP identification lack high performance and interpretability.
- There is a need for advanced computational tools to discover and predict AMPs.
Purpose of the Study:
- To develop a high-performance and interpretable computational model for identifying antimicrobial peptides (AMPs).
- To leverage deep learning architectures and advanced feature representations for improved AMP prediction.
Main Methods:
- Collected and processed new benchmark datasets for AMP identification.
- Designed a stacking deep architecture, AMPpred-MFA, integrating bidirectional LSTM and CNN.
- Utilized multiple sequence features and a multihead attention mechanism within the model.
Main Results:
- AMPpred-MFA demonstrated state-of-the-art performance in identifying AMPs across five independent tests.
- Visualization and ablation studies confirmed the model's effectiveness and the importance of its components.
- The model's feature representation and stacking architecture, particularly the multihead attention, were validated.
Conclusions:
- AMPpred-MFA provides a reliable and efficient approach for understanding and predicting antimicrobial peptides.
- The developed model addresses the limitations of existing computational methods for AMP discovery.
- This work contributes to the advancement of computational strategies in antimicrobial research.
