Interpretable Antimicrobial Peptide Identification using Retrieval-Augmented Generation
Abstract
Introduction: Antimicrobial resistance (AMR) is making conventional antibiotics less effective, driving interest in antimicrobial peptides (AMPs) as alternatives. While protein language models (PLMs) predict AMPs accurately, they rarely explain why a sequence is classified as antimicrobial, limiting their value for research and experimental validation. Research Question: The research question of this study is “How can a retrieval-augmented generation (RAG) framework be used to generate biologically grounded, post-hoc explanations for peptide sequences predicted as antimicrobial or non-antimicrobial by a PLM?” Two sub-questions guide the evaluation are (1) “How well does the fine-tuned PLM classify peptide sequences as AMP or non-AMP?” and (2) “How faithfully do the RAG-generated explanations reflect the retrieved biological evidence?” Method: We fine-tuned a ProtBERT-BFD classifier and integrated a RAG module that retrieves similar sequences from curated sources, passing them to Mistral-7B-Instruct large language model (LLM) to generate biological explanations. We evaluated classifier performance using F1-score and assessed explanation faithfulness using two independent LLM judges. Results: The classifier achieved an F1-score of 0.903 on the held-out test set. The RAG module generated biological explanations achieved mean faithfulness scores of 0.888 (Qwen2.5:7b) and 0.823 (Claude Haiku). Discussion: Overall, pairing PLM-based prediction with knowledge retrieval makes AMP…
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