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PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency
Rayane Monique Bernardes-Loch1, Gustavo de Oliveira Almeida2, Igor Teixeira Brasiliano2
1Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa-MG 36570-900, Brazil.
Bioinformatics Advances
|September 29, 2025
Summary
PerseuCPP is a new machine learning tool that accurately identifies cell-penetrating peptides (CPPs) for drug delivery. This computational strategy speeds up the discovery of effective CPPs, overcoming limitations of experimental methods.
Area of Science:
- Biochemistry
- Computational Biology
- Drug Delivery
Background:
- Cell-penetrating peptides (CPPs) are crucial for intracellular drug delivery, offering a way to transport therapeutic molecules without harming cell membranes.
- Traditional experimental identification of CPPs is costly and time-consuming, hindering rapid advancements in the field.
- Computational strategies present a scalable and cost-effective alternative for identifying and designing novel CPPs.
Purpose of the Study:
- To introduce PerseuCPP, a novel machine learning strategy for the identification and efficiency prediction of cell-penetrating peptides.
- To develop a computational tool that overcomes the limitations of experimental CPP identification methods.
- To provide insights into the key molecular descriptors that facilitate cellular penetration by peptides.
Main Methods:
- The PerseuCPP strategy utilizes Extremely Randomized Trees, a machine learning algorithm, for CPP prediction.
- The model is trained on descriptors including physicochemical, structural, and atomic composition properties of peptides.
- A two-stage approach was employed: one for CPP identification and another for uptake efficiency prediction, both validated using cross-validation and independent datasets.
Main Results:
- The CPP predictor achieved high performance with MCC of 0.854, Recall of 0.860, and AUC of 0.984, outperforming existing state-of-the-art methods.
- The efficiency predictor demonstrated competitive results with Recall of 0.761 and AUC of 0.690.
- PerseuCPP provides an interpretable model, revealing critical descriptors for effective cell penetration.
Conclusions:
- PerseuCPP offers a powerful and efficient computational tool for identifying cell-penetrating peptides.
- The strategy significantly accelerates the discovery and design of CPPs for enhanced drug delivery applications.
- This work is expected to advance biomedical research and therapeutic development utilizing CPPs.
