Prediction of Hemolytic Peptides and their Hemolytic Concentration (HC 50 )
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Several peptide-based drugs 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. In this study, we developed a classification model and regression model to identify and quantify the hemolytic activity of peptides. Our models were trained and validated on 1924 peptides with experimentally determined HC50 against mammalian RBCs. Analysis indicates that hydrophobic and positively charged residues were associated with higher hemolytic activity. Our classification models achieved a maximum AUC of 0.909 using a hybrid model of ESM-2 and a motif-based approach. Regression models using compositional features achieved R of 0.739 with R² of 0.543. Our models outperform existing methods and are implemented in the web-based platform HemoPI2 and standalone software for designing hemolytic peptides with desired HC50 values ( http://webs.iiitd.edu.in/raghava/hemopi2/ ).
Highlights
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Developed classification and regression models to predict hemolytic activity and HC50 values of peptides.
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A hybrid model combining machine learning and motif prediction excels in accuracy.
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Benchmarking of the existing classification methods on independent datasets.
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Web server, standalone software, and pip package for hemolytic activity prediction of peptides/proteins.
