Prediction of hemolytic peptides and their hemolytic concentration.

ABSTRACT: Peptide-based drugs often fail in clinical trials due to their toxicity or hemolytic activity against red blood cells (RBCs). Existing methods predict hemolytic peptides but not the concentration (HC₅₀) required to lyse 50% of RBCs. This study develops classification and regression models to identify and quantify hemolytic activity. These models train on 1926 peptides with experimentally determined HC₅₀ against mammalian RBCs. Analysis indicates that hydrophobic and positively charged residues were associated with higher hemolytic activity. Among classification models, including machine learning (ML), quantum ML, and protein language models, a hybrid model combining random forest (RF) and a motif-based approach achieves the highest area under the receiver operating ch

SUBMITTER: Rathore AS 

PROVIDER: S-EPMC11794569 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature