Hollow CoFe Nanozymes Integrated with Oncolytic Peptides Designed via Machine-Learning for Tumor Therapy.

Abstract

Developing novel substances to synergize with nanozymes is a challenging yet indispensable task to enable the nanozyme-based therapeutics to tackle individual variations in tumor physicochemical properties. The advancement of machine learning (ML) has provided a useful tool to enhance the accuracy and efficiency in developing synergistic substances. In this study, ML models to mine low-cytotoxicity oncolytic peptides are applied. The filtering Pipeline is constructed using a traversal design and the Autogluon framework. Through the Pipeline, 37 novel peptides with high oncolytic activity against cancer cells and low cytotoxicity to normal cells are identified from a library of 25,740 sequences. Combining dataset testing with cytotoxicity experiments, an 80% accuracy rate is achieved, verifying the reliability of ML predictions. Peptide C2 is proven to possess membranolytic functions specifically for tumor cells as targeted by Pipeline. Then Peptide C2 with CoFe hollow hydroxide nanozyme (H-CF) to form the peptide/H-CF composite is integrated. The new composite exhibited acid-triggered membranolytic function and potent peroxidase-like (POD-like) activity, which induce ferroptosis to tumor cells and inhibits tumor growth. The study suggests that this novel ML-assisted design approach can offer an accurate and efficient paradigm for developing both oncolytic peptides and synergistic peptides for catalytic materials.