Cell-Penetrating Peptides Translocate across the Plasma Membrane by Inducing Vesicle Budding and Collapse.

Cell-penetrating peptides (CPPs) enter the cell by two different mechanisms-endocytosis followed by endosomal escape and direct translocation at the plasma membrane. The mechanism of direct translocation remains unresolved. In this work, the direct translocation of nonaarginine (R9) and two cyclic CPPs (CPP12 and CPP17) into Jurkat cells was monitored by time-lapse confocal microscopy. Our results provide direct evidence that all three CPPs translocate across the plasma membrane by a recently discovered vesicle budding-and-collapse (VBC) mechanism. Membrane translocation is preceded by the formation of nucleation zones. Up to four different types of nucleation zones and three variations of the VBC mechanism were observed. The VBC mechanism reconciles the enigmatic and conflicting observations in the literature.

Bismuth-Cyclized Cell-Penetrating Peptides.

Intracellular delivery of biological cargos, which would yield new research tools and novel therapeutics, remains an active area of research. A convenient and potentially general approach involves the conjugation of a cell-penetrating peptide to a cargo of interest. However, linear CPPs lack sufficient cytosolic entry efficiency and metabolic stability, while previous backbone cyclized CPPs have several drawbacks including the necessity for chemical synthesis and posttranslational conjugation to peptide/protein cargos and epimerization during cyclization. We report here a new class of bismuth cyclized CPPs with excellent cytosolic entry efficiencies, proteolytic stability, and potential compatibility with genetic encoding and recombinant production.

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations.

We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target ß-catenin and NF-κB essential modulator. Among the twelve ß-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds ß-catenin with an IC50 of 0.010 ± 0.06 µM, which is 15-fold better than the parent peptide. For NF-κB essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design.