MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.

Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics, yet early translation is often hindered by the perceived coupling between antibacterial potency and mammalian toxicity. This assumption complicates prioritization: highly active candidates are frequently suspected to be hemolytic, while existing multi-task predictors rarely reveal where selectivity resides in sequence space. Here, we present Multifunctional AMP Learning Engine (MAPLE), an interpretable dual-stream framework for AMP identification and systematic category-specific functional profiling across 14 activity categories directly from peptide sequences. MAPLE combines protein language model embeddings with explicit physicochemical descriptors, enabling robust task-specific prediction under severe label imbalance. Across the benchmark dataset and a sequence-non-overlapping independent validation set, MAPLE achieves consistently well-balanced performance, including on low-prevalence but clinically relevant endpoints. Building on this predictive basis, we conduct systematic k-mer enrichment to map motif-level selectivity and show that potency-hemolysis coupling is motif-regime-dependent rather than universal. Motifs most strongly enriched for antibacterial activity exhibit reduced hemolytic overlap and occupy a physicochemical regime characterized by moderate cationicity, lower hydrophobicity, and higher amphipathicity. We further provide a proof-of-concept prioritization workflow leveraging antibacterial-selective motifs, with structural modeling yielding conformations consistent with amphipathic α-helices. Despite limitations of predominantly binary annotations and incomplete structural integration, MAPLE offers reproducible sequence-level hypotheses and prioritization principles to support the engineering of potent and safer AMPs.