POSEIDON: Peptidic Objects SEquence-based Interaction with cellular DOmaiNs: a new database and predictor.

ABSTRACT: Cell-penetrating peptides (CPPs) are short chains of amino acids that have shown remarkable potential to cross the cell membrane and deliver coupled therapeutic cargoes into cells. Designing and testing different CPPs to target specific cells or tissues is crucial to ensure high delivery efficiency and reduced toxicity. However, in vivo/in vitro testing of various CPPs can be both time-consuming and costly, which has led to interest in computational methodologies, such as Machine Learning (ML) approaches, as faster and cheaper methods for CPP design and uptake prediction. However, most ML models developed to date focus on classification rather than regression techniques, because of the lack of informative quantitative uptake values. To address these challenges, we developed POSEIDON, an op

SUBMITTER: Preto AJ 

PROVIDER: S-EPMC10874016 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature