Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery

Cell-penetrating peptides (CPPs) can facilitate the intracellular delivery of large therapeutically relevant molecules, including proteins and oligonucleotides. Although hundreds of CPP sequences are described in the literature, predicting efficacious sequences remains difficult. Here, we focus specifically on predicting CPPs for the delivery of phosphorodiamidate morpholino oligonucleotides (PMOs), a compelling type of antisense therapeutic that has recently been FDA approved for the treatment of Duchenne muscular dystrophy. Using literature CPP sequences, 64 covalent PMO–CPP conjugates were synthesized and evaluated in a fluorescence-based reporter assay for PMO activity. Significant discrepancies were observed between the sequences that performed well in this assay and the sequences that performed well when conjugated to only a small-molecule fluorophore. As a result, we envisioned that our PMO–CPP library would be a useful training set for a computational model to predict CPPs for PMO delivery. We use...