PLMTHP: An Ensemble Framework for Tumor Homing Peptide Prediction based on Protein Language Model

Tumor homing peptides (THPs) play significant role in recognizing and specifically binding to tumor cells. Traditional experimental methods can accurately identify THPs but often suffer from high measurement costs and long experimental cycles. In-silico methods can rapidly screen THPs and accelerate the THPs identification process. Existing THPs prediction methods primarily rely on constructing peptide sequence features and machine learning approaches. These tools rely on manual feature extraction, and the combination of features is crucial for both model performance and robustness. In this study, we propose a method called PLMTHP based on protein language model and integrate multiple machine learning models. Compared to existing models, PLMTHP improves predictions by incorporating high-dimensional features encoded through a protein language model. Our method achieved impressive performance on an independent test set, with ACC of 0.915, MCC of 0.831, and AUC of 0.964. PLMTHP can be downloaded from the following website: https://github.com/Chenyb939/PLMTHP