Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery.
Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery.
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DOI：
10.1021/acscentsci.8b00098
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发表时间：
2018-04-25
影响因子：
18.2
通讯作者：
Pentelute BL
中科院分区：
文献类型：
--
作者：
Wolfe JM;Fadzen CM;Choo ZN;Holden RL;Yao M;Hanson GJ;Pentelute BL
细胞穿透肽（CPP）可以促进大的治疗相关分子（包括蛋白质和寡核苷酸）的细胞内递送。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，我们专注于预测CPPs提供磷酰二胺吗啉代寡核苷酸（PMO），一个引人注目的类型的反义治疗，最近已被FDA批准用于治疗杜氏肌营养不良症。使用文献CPP序列，合成64个共价PMO-CPP缀合物，并在基于荧光的报告基因测定中评价PMO活性。在该测定中表现良好的序列与仅与小分子荧光团缀合时表现良好的序列之间观察到显著差异。因此，我们设想，我们的PMO-CPP库将是一个有用的训练集的计算模型，以预测CPPs的PMO交付。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使PMO活性增强至少3倍。为了验证该模型的实验，七个新的序列产生，合成，并在荧光报告分析测试。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80%的测试文献CPP。这些结果证明了机器学习算法鉴定具有特定功能的肽序列的能力，并说明了为感兴趣的货物定制CPP序列的重要性。测试PM 0-肽缀合物文库的细胞活性。这些结果使得能够开发计算模型来预测改善PMO递送的新型肽序列。
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. A library of PMO-peptide conjugates was tested for cellular activity. The results enabled the development of a computational model to predict novel peptide sequences that improve PMO delivery.
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影响因子：
4.3
作者：
Sanders WS;Johnston CI;Bridges SM;Burgess SC;Willeford KO
通讯作者：
Willeford KO
肽仪的天然和非天然氨基酸表征的指数。
影响因子：
3.7
作者：
Liang G;Liu Y;Shi B;Zhao J;Zheng J
通讯作者：
Zheng J
影响因子：
15
作者：
LaRochelle JR;Cobb GB;Steinauer A;Rhoades E;Schepartz A
通讯作者：
Schepartz A
影响因子：
16.6
作者：
Buckley, Dennis L.;Crews, Craig M.
通讯作者：
Crews, Craig M.
影响因子：
14.8
作者：
Mijalis, Alexander J.;Thoma, Dale A., III;Pentelute, Bradley L.
通讯作者：
Pentelute, Bradley L.