Abstract
Cell penetrating peptides (CPPs) are molecules capable of passing through biological membranes. This capacity has been used to deliver impermeable molecules into cells, such as drugs and DNA probes, among others. However, the internalization of these peptides lacks specificity: CPPs internalize indistinctly on different cell types. Two major approaches have been described to address this problem: (i) targeting, in which a receptor-recognizing sequence is added to a CPP, and (ii) activation, where a non-active form of the CPP is activated once it interacts with cell target components. These strategies result in multifunctional peptides (i.e., penetrate and target recognition) that increase the CPP’s length, the cost of synthesis and the likelihood to be degraded or become antigenic. In this work we describe the use of machine-learning methods to design short selective CPP; the reduction in size is accomplished by embedding two or more activities within a single CPP domain, hence we referred to these as moonlighting CPPs. We provide experimental evidence that these designed moonlighting peptides penetrate selectively in targeted cells and discuss areas of opportunity to improve in the design of these peptides.
Highlights
An important limitation of these vectors is a relative lack of specificity, that is, Cell-penetrating peptides (CPPs) may deliver their cargo inside many different cell types without distinction
The design in that previous work required the training of a classifier to identify CPPs from non-CPPs sequences. These three peptides presented a high probability to comply with CPP rules (P(CPP) >= 0.88), and we have previously shown in the aforementioned publication that all these peptides penetrate mammalian cells
An important limitation of CPPs is the lack of specificity, but two different approaches have been proposed to overcome this limitation: add a ligand to target a surface protein or activate the CPPs until a condition is met
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Testing our designs on these two experimental systems has several goals, for instance, to show the usefulness of our computational designs in mammalian and microbial cells, opening the possibilities to use this computational strategy to label or target these two types of cells. In both cases, the CPP activity was achieved by adding 4 or up to 12 residues, depending on the starting sequence. We discuss different areas of opportunities to apply this strategy and areas for future development
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