SPECIES AND TOPIC-AWARE REPRESENTATION LEARNING FOR ANTIMICROBIAL PEPTIDE ACTIVITY

Antimicrobial peptides (AMPs) play a crucial role in addressing the growing threat of antimicrobial resistance. However, their activity varies markedly across microbial targets, indicating that primary peptide sequence data alone cannot fully explain antimicrobial function. To overcome this limitation, we present STAMP: a Species- and Topic-aware representation-learning framework that models AMP activity by integrating sequence, motif, and biological context. STAMP unifies contextual protein language model representations, latent motif composition, and microbial species information within a single model, addressing data sparsity by enabling cross-species training. We evaluated STAMP on four benchmark datasets curated from DBAASP, dbAMP, and DRAMP. As a single cross-species model, STAMP matches methods that rely on tens of independently trained species-specific models and outperforms the most recently published benchmark, reaching a maximum Pearson correlation coefficient (PCC) of 0.837 and an R2 of 0.698. To demonstrate its utility for candidate prioritization, we applied STAMP to score a large library of computationally generated peptides. Among the 18 candidates selected for experimental validation, 16 exhibited experimentally measured MIC values below 10 μM against E. coli, while 17 exhibited MIC values below 10 μM against S. epidermidis, providing experimental support for the predicted antimicrobial activity. Additionally, experiments with Tilapia Piscidin 4 (TP4)-derived peptides and residue-level attribution analysis revealed that STAMP captures target-dependent activity shifts and pinpoints sequence regions crucial for species-conditioned predictions. Collectively, these results demonstrate the value of integrating molecular and microbial context to model AMP activity, supporting computationally guided and efficient AMP discovery.