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 used the PMO activity data to fit a random decision forest classifier to predict whether or not covalent attachment of a given peptide would enhance PMO activity at least 3-fold. To validate the model experimentally, seven novel sequences were generated, synthesized, and tested in the fluorescence reporter assay. All computationally predicted positive sequences were positive in the assay, and one sequence performed better than 80% of the tested literature CPPs. These results demonstrate the power of machine learning algorithms to identify peptide sequences with particular functions and illustrate the importance of tailoring a CPP sequence to the cargo of interest.

中文翻译：

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机器学习预测反义传递的细胞穿透肽

细胞穿透肽（CPP）可以促进细胞内大分子治疗相关分子的传递，包括蛋白质和寡核苷酸。尽管文献中描述了数百种CPP序列，但预测有效序列仍然很困难。在这里，我们特别专注于预测CPPs用于递送二氨基氨基磷酸吗啉代寡核苷酸（PMO），这是一种引人注目的反义疗法，最近已被FDA批准用于治疗杜兴氏肌营养不良症。使用文献CPP序列，合成了64种共价PMO-CPP共轭物，并在基于荧光的报告分子分析中评估了PMO活性。在此分析中表现良好的序列与仅与小分子荧光团偶联时表现良好的序列之间观察到显着差异。结果，我们预见到我们的PMO-CPP库对于计算模型预测PMO交付的CPP将是有用的训练集。我们使用PMO活性数据来拟合随机决策森林分类器，以预测给定肽的共价连接是否会增强PMO活性至少3倍。为了通过实验验证该模型，生成了七个新序列，进行了合成，并在荧光报告基因分析中进行了测试。所有计算预测的阳性序列在测定中均为阳性，并且一个序列的性能优于被测文献CPP的80％。