Abstract:
CPPs are short peptide sequences that have the ability to pass through cell
membranes and deliver a range of molecular cargoes within cells. Their
remarkable ability to enter cells without inflicting significant harm to the
membrane makes them crucial for uses like as intracellular imaging, targeted
drug delivery, and gene therapy. I introduced Deep_Hybrid_CPP, an innovative
computational approach for efficiently and effectively identifying cellpenetrating peptides through a multi-view feature fusion framework. This study
integrates 10 diverse sequence-based feature extractors from different
perspectives with 12 prominent machine learning (ML) algorithms in
Deep_Hybrid_CPP to create multi-view features that thoroughly represent the
essential information of cell-penetrating peptides. To enhance the distinguishing
capability of my tailored genetic algorithm, Additionally employed it to select a
collection of multi-view features. Based on a series of comparative experiments,
my multi-view features outperformed certain traditional feature extractors
regarding their discriminative capabilities. Additionally, regarding the
independent test dataset, Deep_Hybrid_CPP achieved the top accuracy (ACC)
and Matthew's correlation coefficient (MCC) of 97% and 94%, respectively,
showing increases of 5.06% and 10.97%. Using this study's innovative
computational approach, I expect to effectively evaluate and prioritize candidate
peptides that may demonstrate favorable cell-penetrating peptide
characteristics
