CPPpred-En: Ensemble framework integrating a protein language model and conventional features for highly accurate...

Cell-penetrating peptides (CPPs) have gained significant attention for biomedical applications, including drug delivery and therapeutic development, due to their ability to penetrate cell membranes. The accurate prediction of CPPs is critical for accelerating the design and development of novel peptide-based therapies. Approaches for CPP prediction primarily depend on either peptide characteristic-based conventional features or one or two protein language models (PLMs), but these methods often fail to fully leverage the potential of combining diverse features. To address this limitation, we propose CPPpred-En, a prediction model that evaluates multiple conventional and PLM-based features across various machine learning classifiers, selects high-performing feature-classifier combinations, and integrates them through ensemble learning. The CPPpred-En model, which was trained on both the CPP924 and MLCPP 2.0 datasets, outperformed existing state-of-the-art predictors, achieving an accuracy (Acc) of 97.27 % and a matthews correlation coefficient (MCC) of 0.964 on the CPP924 dataset and an Acc of 96.10 % and an MCC of 0.707 on the MLCPP 2.0 dataset. The ensemble-based strategy demonstrated robustness across different datasets, highlighting the strong ability of the model to generalise. The combination of conventional and PLM features in an ensemble framework is promising approach for improving peptide-based therapeutics. The CPPpred-En model is a highly accurate and reliable tool for the identification of CPPs and their application in drug delivery and targeted therapy.