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We developed a two-layer prediction framework for machine-learning-based prediction of cell-penetrating peptides (MLCPP). The first layer predicts whether a given peptide is a CPP or non-CPP, while the second layer predicts the uptake efficiency of the predicted CPPs. We employed four machine-learning-based models for a two-layer prediction framework using features computed from the peptide sequence, including amino acid composition, dipeptide composition, amino acid index, composition-transition-distribution, and physiochemical properties. Extremely randomized tree outperformed the state-of-the-art predictors in CPP prediction when tested on independent datasets; in CPP uptake efficiency, our RF-based predictor was better than the state-of-the-art predictor. We anticipate that MLCPP will be a potent tool for predicting CPPs and their uptake efficiency, and may facilitate hypothesis-driven experimental designs

Enter the peptide sequences in FASTA format (Example)



Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy ( J. Proteome Res. 2018, 17, 2715−272 ).
 [Please cite this paper if you find MLCPP useful in your research