Bioprospecting of culturable marine biofilm bacteria for novel antimicrobial peptides

Antimicrobial peptides (AMPs) have become a viable source of novel antibiotics that are effective against human pathogenic bacteria. In this study, we construct a bank of culturable marine biofilm bacteria constituting 713 strains and their nearly complete genomes and predict AMPs using ribosome profiling and deep learning. Compared with previous approaches, ribosome profiling has improved the identification and validation of small open reading frames (sORFs) for AMP prediction. Among the 80,430 expressed sORFs, 341 are identified as candidate AMPs with high probability. Most potential AMPs have less than 40% similarity in their amino acid sequence compared to those listed in public databases. Furthermore, these AMPs are associated with bacterial groups that are not previously known to produce AMPs. Therefore, our deep learning model has acquired characteristics of unfamiliar AMPs. Chemical synthesis of 60 potential AMP sequences yields 54 compounds with antimicrobial activity, including potent inhibitory effects on various drug-resistant human pathogens. This study extends the range of AMP compounds by investigating marine biofilm microbiomes using a novel approach, accelerating AMP discovery. In the context of marine resource bioprospecting, we construct a bank of culturable marine biofilm bacteria encompassing 713 strains and their nearly complete genomes. Using ribosome profiling and a new deep learning algorithm, we get more than 300 candidate antimicrobial peptides (AMPs). We further conduct antimicrobial activity and cytotoxicity experiments to evaluate their potential as druggable molecules.image Bacterial isolation, cultivation, and genome sequencing have laid the foundation for antimicrobial peptide (AMP) discovery from marine biofilms, where AMPs are largely unexplored. The incorporation of ribosome profiling has enhanced the sequence filtering process, enabling more accurate identification of small open reading frames before AMP prediction. A multilayered model-based filtering pipeline exhibits outstanding predictive performance in screening candidate AMPs. Experimentally validated AMPs show excellent antibacterial efficacy, highlighting their potential as candidate drugs.