PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency.

ABSTRACT:

Motivation

Cell-penetrating peptides (CPPs) are promising tools for transporting therapeutic molecules into cells without damaging the cellular membrane. These peptides serve as efficient drug delivery systems, capable of carrying diverse biologically active substances while exhibiting low cytotoxicity compared to non-native molecules. However, identifying CPPs through experimental methods is expensive and time-consuming, making computational strategies an attractive alternative due to their cost-effectiveness and scalability.

Results

This study introduces PerseuCPP, a machine learning strategy designed to identify CPPs. Based on descriptors including physicochemical and structural properties as well as atomic composition, our strategy employs the Extremely Randomized Trees

SUBMITTER: Bernardes-Loch RM 

PROVIDER: S-EPMC12462384 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature