ArticleFrontiers in microbiology2026
tsAMP: a strain-level antimicrobial peptide identification framework based on large language models and pathogen genomic variation.
Article in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Facing the global threat of multidrug-resistant bacteria, antimicrobial peptides (AMPs) represent a promising alternative to conventional antibiotics. Methods: To improve computational AMP identification and accuracy of strain-level MIC prediction, we developed tsAMP, a comprehensive framework integrating the ESM-1v protein language model with multidimensional feature extraction. The model was trained on AMP and metagenome-derived non-AMP sequences. Results: tsAMP achieved an F1-score of 0.958 for AMP identification, outperforming state-of-the-art tools. For bacterial inhibition prediction, tsAMP consistently maintained F1-scores above 0.8 across 33 pathogenic species. In strain-specific MIC prediction, it attained high performance (MSE = 0.214, Discussion: tsAMP provides a computational framework to facilitate the identification of AMP candidates and support prioritization for downstream experimental characterization. The code is available on GitHub at https://github.com/YangLab-BUPT/tsAMP.
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