Application of machine learning in the discovery of antimicrobial peptides: Exploring their potential for ulcerative colitis therapy

Abstract

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with rising global prevalence, yet existing treatments are not universally effective. Antimicrobial peptides (AMPs), produced by the immune system, have diverse antimicrobial and immune-regulatory functions, making them promising candidates for UC therapy. Using machine learning, we developed a machine learning-based prediction model to identify novel AMPs. The predicted peptides demonstrated significant biological activity in vitro and in vivo. In a dextran sulfate sodium-induced UC mouse model, engineered AMPs notably improved UC-related parameters, such as body weight, disease activity index (DAI), and colon length. These effects were likely mediated by modulation of Akkermansia muciniphila. This study highlights the potential of machine learning-identified AMPs as future therapeutic candidates for UC. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/660148v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@187dc6borg.highwire.dtl.DTLVardef@9800deorg.highwire.dtl.DTLVardef@1611c42org.highwire.dtl.DTLVardef@866406_HPS_FORMAT_FIGEXP M_FIG C_FIG