Towards Accurate Identification of Anti-Hepatitis C Peptides Using Stack-AHCP

Hepatitis C virus (HCV) infection remains a significant global health burden, contributing to progressive hepatic pathologies including chronic hepatitis, cirrhosis, and hepatocellular carcinoma. While anti-hepatitis C peptides (AHCPs) have emerged as promising therapeutic candidates with distinct antiviral mechanisms, conventional wet-lab approaches for AHCP discovery face critical limitations in throughput and scalability. To overcome these constraints, we present Stack-Ahcp, an innovative stacked ensemble learning framework that synergistically integrates multiple machine learning algorithms through a meta-classification strategy. Our model achieves unprecedented predictive performance with 93.1% accuracy and an MCC of 0.863, substantially outperforming existing computational methods. Through comprehensive Shapley additive explanations (SHAP) analysis, we further delineate critical key intrinsic features and determinants governing AHCP bioactivity, enhancing the mechanistic interpretability of the prediction system. To facilitate translational applications, we have implemented an intuitive web interface (accessible at https://awi.cuhk.edu.cn/~biosequence/StackAHCP/index.php) that enables rapid screening and prioritization of candidate peptides. This resource is anticipated to streamline the identification of next-generation peptide therapeutics against HCV while reducing experimental validation costs. Beyond virology applications, our methodological framework establishes a paradigm for interpretable machine learning in biological sequence analysis, with potential adaptability to diverse multi-omics investigation scenarios.