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