The ability to cross the membrane and gain access to the intracellular environment and to specific sub-cellular compartments is critical for the development of novel nanomedicine and nanodiagnostic tools. The objective of this project is the development of molecular motifs capable of interacting with cellular membranes and promoting translocation of the nanosystem into the cytoplasm and to a specific intracellular domain.
We pursue this goal through the rational design of cell penetrating peptides (CPPs), short aminoacidic sequences able to penetrate the cell membrane at low micromolar concentrations in vivo and in vitro without causing irreversible membrane damage. A prerequisite of this research is a mechanistic understanding of the capability of CPPS to interact with the cell membrane and promote translocation into the cell cytoplasm. To this end, selected motifs are synthesized, purified, and functionalized with fluorophores for the detection of their activity in biological samples (e.g. live cells or tissues). Vector localization and activity are quantitatively investigated by state-of-the-art optical microscopy techniques down to the single-molecule detection limit (see section "Advanced microscopy methods") (Fig. 1).
Entrapment in the endocytic vesicles, however, may limit the ability of a selected CPP-cargo to reach specific intracellular targets. To overcome this limitation, we seek to engineer peptidic sequences that are able to escape the endocytes, while preserving CPP activity, before enzymatic degradation occurs in the lysozomal compartment (Fig. 2). To build these features into a peptidic sequence, we follow a twofold strategy. Firstly, we take inspiration from peptides derived from natural sources such as viruses, toxins, and antimicrobials in an attempt to design modular systems with multiple activity. Alternatively, we apply a bioinformatic approach to design artificial peptides through in silico discovery and optimization. More specifically, a set of well characterized chemicophysical features (percent amino acid composition, secondary structure, molecular weight, peptide length, positive charge, hydrophobicity, etc.) are combined with biological data (amino acid conservation, phylogenetic features and pairwise alignment) to build and refine a mathematical model able to predict activity of peptidic sequences. Heuristic algorithms (artificial neural network, genetic algorithms and support vector machines) are then applied in order to optimize and design new peptides with cell-penetrating and endosome escaping capabilities. Particular emphasis is placed on the application of artificial amino acids in order to extend compounds half-life and fine-tune their properties.
This project is supported by a dedicated peptide synthesis facility equipped with a microwave-aided solid phase peptide synthesizer (SPPS) apt to produce complex peptide sequences with high overall yield through Fmoc chemistry. With this technology, it is possible to incorporate non-natural amino acids into the peptide chain, thus allowing greater flexibility in the development of new targeting systems.