ReviewKeynoteCell-penetrating peptides: classes, origin, and current landscape
Highlights
► More than 100 CPPs are presented, divided into cationic, amphipathic, and hydrophobic. ► Origin-based classification: natural proteins/peptides; designed; random libraries ► Natural sources of CPPs: heparin-, DNA/RNA binding proteins; viral proteins; antimicrobial/signal peptides.
Section snippets
Physical–chemical properties-based classification
Even though CPPs have a great sequence variety, it is possible to identify three major classes: cationic, amphipathic and hydrophobic. Figure 1 presents a broad overview of the current CPPs landscape. The data presented contain more than 100 diverse CPPs (corresponding to the CPPs shown in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6) collected from publications and patents, excluding mutants around the same peptides. Most of the CPPs in this set have a net positive charge (83%);
Cationic CPPs
The first CPP discovered was cationic and was derived from the HIV-1 protein Tat (RKKRRQRRR) [34]. Studies on arginine-based peptides (from R3 to R12) have shown that the minimal sequence for cellular uptake is octaarginine (R8), and that increasing the number of arginines increases the level of uptake [20]. Polylysine, in comparison, has a much poorer uptake profile [20].
Studies suggest that at least eight positive charges are needed for efficient uptake of several other cationic CPPs [35].
Primary amphipathic
Several primary amphipathic CPPs are chimeric peptides obtained by covalently attaching a hydrophobic domain for efficient targeting to cell membranes to a NLS. For example, MPG (GLAFLGFLGAAGSTMGAWSQPKKKRKV) and Pep-1 (KETWWETWWTEWSQPKKRKV) are both based on the SV40 NLS PKKRKV. The hydrophobic domain of MPG was derived from the fusion sequence of the HIV glycoprotein 41 (GALFLGFLGAAGSTMGA), while that of Pep-1 corresponds to a tryptophan-rich cluster (KETWWETWWTEW), which has high affinity for
Hydrophobic CPPs
In this article I consider hydrophobic peptides that either: contain only apolar residues; have a low net charge (less than 20% of the sequence); or have a hydrophobic motif or chemical group that is crucial for uptake regardless of the rest of the sequence. Compared with cationic and amphipathic peptides, only a few hydrophobic CPPs have been discovered, however this might be a mere reflection of an historical bias towards cationic and amphipathic CPPs, which were discovered first. Indeed, it
Prenylated peptides
The addition of either a farnesyl (C15) or geranylgeranyl (C20) isoprenoid moiety, known as prenylation, has been reported to give peptides inherent cell-penetrating ability through an ATP-independent, non-endocytic pathway [72]. Although more work is needed to fully understand the role of prenylation on cellular uptake, current studies suggest that uptake is independent of the specific sequence, as is the case for stapled peptides.
Pepducins
Pepducins are a class of N-terminally lipidated peptides that can cross the cell membrane and bind to the cytosolic region of a variety of transmembrane proteins (GPCRs, MMPs, among others) [73]. The N-terminal lipidation consists of either palmitoyl or other fatty acids. Two pepducins, x1/2pal-i3 and x1/2LCA-i1, have entered preclinical development. Contrary to CPPs, pepducins remain anchored to the cell membrane, and are not released in the cytosol, thus focusing their application to
The role of peptide 3D structure on cellular uptake
The role of peptide secondary structure in cell-penetration remains elusive. First, secondary structure depends on the medium [74]. Peptides can adopt completely different conformations depending on whether they are in water, near the membrane interface, inside the membrane, or bound to a protein. Second, the importance of the secondary structure depends on the mode of uptake and on the peptide class (cationic, amphipathic or hydrophobic). α-helical and β-strand CPPs can be sensitive to
Origin-based classification
The discovery of the first CPP, the cationic peptide Tat, was followed by the identification of a cationic and partially amphipathic CPP, Penetratin, from the homeodomain of Antennapedia. Mutation studies demonstrated that positive charges and amphipathicity are important features for cell penetration, and these characteristics were carefully considered to design new synthetic CPPs. Concurrently, researchers also looked for these features within domains of natural proteins. Presently, new CPPs
CPPs derived from natural proteins or peptides
Natural protein motifs are a rich source of CPPs, but establishing a link between a CPP and its role in the protein from which it is derived presents considerable challenges. Proteins might be able to enter the cell through a variety of motifs which, if isolated, would not be cell-penetrating. For example, certain plasma membrane proteins are ubiquinated at the level of their cytosolic tail to control their subsequent endocytosis. Many transmembrane proteins contain specific and diverse
Designed CPPs and CPPs derived from large peptide libraries
While the majority of designed peptides have a characteristic amphipathic structure (Table 5), peptides discovered from high-throughput screening on large peptide libraries are much more diverse, with many having a larger number of hydrophobic, rather than cationic amino acids, and no clear preference for amphipathic structures.
CPPs from large combinatorial peptide libraries
Randomized peptide libraries can be obtained through DNA-encoded peptide libraries and thus enable the generation of billions of peptides. Several methods are available to link peptides to their encoding DNA, such as phage display, plasmid display, micro-organism surface display and ribosome display [88]. Alternatively, peptide arrays can be used for high-throughput synthesis of tens of thousands peptides. Several CPPs have been discovered from such libraries, such as SG3 [66] (plasmid
Phylomers
Phylomers are libraries of natural peptides encoded by natural genes of diverse bacterial genome [93]. Compared with random peptide sequences obtained by phage display, phylomer libraries contain millions of peptides with subdomains that already evolved to maintain some structural stability. New CPPs obtained from this approach have also been reported, some cationic, others amphipathic with a net negative charge, and others mainly hydrophobic. Examples include peptides BEN_1079 and BEN_0805
CPPs prediction from natural proteins and random libraries
Only a few prediction models for CPPs have been proposed, including that by Hansen et al. [94] based on Sandberg z descriptors for amino acids [95], and that by Sanders et al. based on biochemical properties of peptides (such as amino acid composition, peptide length and net charge) [96]. Peptides discovered through these methods, which are reported in Table 5 have a varied amino acid composition.
Concluding remarks
With a great sequence variety and large differences in terms of physical chemical properties, CPPs can be linear, cyclical, cationic, anionic, hydrophobic, hydrophilic, amphipathic, non-amphipathic, random coiled, α-helical, or β-sheets. However, CPPs differ from most other peptides with respect to specific features that reflect various mechanisms used to enter the cell.
Highly cationic CPPs (i.e. CPPs with at least eight positive charges) can interact with GAGs and enter the cell through
Francesca Milletti leads the Cheminformatics and Statistics group at Roche (Nutley, NJ, USA). She joined Roche in 2010 after a postdoctoral fellowship at Novartis, Basel. Before that, she was a visiting student at the University of California, San Francisco (2008). She received her PhD and undergraduate degree in chemistry from the University of Perugia, Italy, where she developed computational tools for pKa prediction and tautomer enumeration. Her research focuses on novel computational
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Francesca Milletti leads the Cheminformatics and Statistics group at Roche (Nutley, NJ, USA). She joined Roche in 2010 after a postdoctoral fellowship at Novartis, Basel. Before that, she was a visiting student at the University of California, San Francisco (2008). She received her PhD and undergraduate degree in chemistry from the University of Perugia, Italy, where she developed computational tools for pKa prediction and tautomer enumeration. Her research focuses on novel computational methods for drug discovery with a special interest on cell-penetrating peptides.