Pathogen context reshapes antimicrobial peptide generation
You, S.; Zhang, C.; Han, Y.; Jiang, Q.; Guo, X.; Li, M.; Su, Y.; Dong, X.; Yang, M.; Lu, H.
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Antimicrobial peptide discovery is constrained less by the number of molecules that can be generated than by the choice of which few should be tested against a defined pathogen. Most peptide generators produce broadly antimicrobial-like sequences and leave target specificity to downstream filters. Here we show that pathogen context can be introduced during generation. AMPHORA conditions a peptide-native generator on target class, pathogen genome features and strain-description text. Matched, ablated and shuffled controls showed that aligned pathogen inputs redirected generated libraries beyond coarse activity labels, whereas global shuffling weakened this effect. Same-noise counterfactuals showed that strain descriptions drove larger sequence changes, whereas genome features more strongly affected predicted structural properties. Species-level analyses revealed target-dependent enrichment. Matched bacterial inputs also shifted APEX-predicted activity rankings relative to class-only generation. The resulting libraries remained diverse, largely non-memorizing and compatible with predicted peptide-like structural features. Together, these results establish pathogen-context conditioning as a new paradigm for computational library reshaping in antimicrobial peptide generation.
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