Prediction of Tumor-Homing Peptide by Fusing Deep Learning with Protein Language Models

Abstract

Tumor-homing peptides (THPs) are short amino acid sequences that minimize off-target toxicity while selectively binding to molecular markers on tumor cells to enable targeted drug delivery and diagnostic imaging. The development of peptide-based cancer treatments depends on the accurate identification of THPs, but conventional experimental screening is time-consuming and previous computational predictors have shown limitations. For example, early machine-learning models (e.g., SVM-based approaches by Sharma et al. 2013) only achieved moderate accuracy because of small datasets and limited feature scope. These models relied on basic sequence features like amino acid composition. More potent tools for THP prediction have been made available by recent developments in artificial intelligence. As demonstrated by models like PLMTHP and LLM4THP as well as related peptide prediction frameworks, protein language model (PLM) embeddings combined with deep learning have shown promise.As demonstrated by models like PLMTHP and LLM4THP, as well as by related peptide prediction frameworks like PLMACPred for anticancer peptides and PepCNN for peptide–protein interactions, protein language model (PLM) embeddings combined with deep learning have demonstrated promise. Nevertheless, a lot of these approaches either use restricted feature combinations or concentrate on tasks related to THP prediction, which leaves space for more accurate and complex sequence pattern capture. We suggest a GAN-enhanced bidirectional semi-temporal CNN architecture that combines deep learning with PLM-based descriptors for THP prediction in order to close these gaps.As proven by models like PLMTHP and LLM4THP, and additionally by related peptide prediction frameworks like PLMACPred for anticancer peptides and PepCNN for peptide–protein interactions, protein language model (PLM) embeddings combined with deep learning have demonstrated promise. Nevertheless, a lot of these approaches either use restricted feature combinations or concentrate on tasks related to THP prediction, which leaves space for more accurate and complex sequence pattern capture. We suggest a GAN-enhanced bidirectional semi-temporal CNN architecture that combines deep learning with PLM-based descriptors for THP prediction in order to close these gaps.In conclusion, our work provides a cutting-edge computational tool for THP identification that connects biological understanding with computational prediction. The high accuracy and interpretability of the suggested model highlight the wider impact of AI-driven peptide discovery in precision oncology and support its potential as a basis for peptide-based drug delivery systems and cancer therapeutics development.