meRfi®-GM
Antimicrobial peptides (AMPs)
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Contents
Antimicrobial resistance threatens public health, driving the need for alternative therapies like antimicrobial peptides (AMPs). Protein language models (PLMs) enhance protein structure and function predictions, aiding AMP discovery. We created the antimicrobial peptide structural evolution miner (AMP-SEMiner), an AI framework that combines PLMs, structural clustering, and evolutionary analysis to identify AMPs from small open reading frames and AMP-containing proteins in metagenome-assembled genomes. AMP-SEMiner found over 1.6 million AMP candidates in various environments. Experiments confirmed antimicrobial activity in 9 of the 20 tested candidates, with 5 outperforming antibiotics; variant peptides…
References (Sources)
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- Identification of peptides from honeybee gut symbionts as potential antimicrobial agents against Melissococcus plutonius
- Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning
- Shigella sonnei encodes a functional T6SS used for interbacterial competition and niche occupancy