Prediction of Hemolysis Tendency of Peptides using a Reliable Evaluation Method

TL;DR

This paper introduces a machine learning method for predicting the hemolytic tendency of peptides, utilizing a novel clustering-based train-test split to ensure reliable performance estimates on unseen data, aiding therapeutic peptide screening.

Contribution

The study presents a new clustering-based train-test splitting method and demonstrates its effectiveness in reliably predicting peptide hemolytic tendencies with high accuracy and AUC-ROC.

Findings

Achieved 97.79% accuracy and 0.9986 AUC-ROC on test set using traditional split.

Achieved 97.58% accuracy and 0.997 AUC-ROC with clustering-based split.

Recorded 79.5% accuracy on unseen data distribution.

Abstract

There are numerous peptides discovered through past decades, which exhibit antimicrobial and anti-cancerous tendencies. Due to these reasons, peptides are supposed to be sound therapeutic candidates. Some peptides can pose low metabolic stability, high toxicity and high hemolity of peptides. This highlights the importance for evaluating hemolytic tendencies and toxicity of peptides, before using them for therapeutics. Traditional methods for evaluation of toxicity of peptides can be time-consuming and costly. In this study, we have extracted peptides data (Hemo-DB) from Database of Antimicrobial Activity and Structure of Peptides (DBAASP) based on certain hemolity criteria and we present a machine learning based method for prediction of hemolytic tendencies of peptides (i.e. Hemolytic or Non-Hemolytic). Our model offers significant improvement on hemolity prediction benchmarks. we also propose a reliable clustering-based train-tests splitting method which ensures that no peptide in train set is more than 40% similar to any peptide in test set. Using this train-test split, we can get reliable estimated of expected model performance on unseen data distribution or newly discovered peptides. Our model tests 0.9986 AUC-ROC (Area Under Receiver Operating Curve) and 97.79% Accuracy on test set of Hemo-DB using traditional random train-test splitting method. Moreover, our model tests AUC-ROC of 0.997 and Accuracy of 97.58% while using clustering-based train-test data split. Furthermore, we check our model on an unseen data distribution (at Hemo-PI 3) and we recorded 0.8726 AUC-ROC and 79.5% accuracy. Using the proposed method, potential therapeutic peptides can be screened, which may further in therapeutics and get reliable predictions for unseen amino acids distribution of peptides and newly discovered peptides.