ArticleACS central science2018
Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery.
Article in ACS central science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 84 citations in OpenAlex.
- Membrane-Traversing Peptides: Mechanistic Landscapes, Assays, and Applications.Chemical reviews · 2026Review
- CPP2Vec: A representation learning approach for cell-penetrating peptides prediction.PLoS computational biology · 2026Article
- Inhibition of tumor growth using A conjugated nanobody that specifically targets c-MYC.Oncogene · 2025Article
- Exploring the Chemical Features and Biomedical Relevance of Cell-Penetrating Peptides.International journal of molecular sciences · 2024Review
- Development of bioconjugate-based delivery systems for nucleic acids.RNA (New York, N.Y.) · 2024Review
- MuCoCP: a priori chemical knowledge-based multimodal contrastive learning pre-trained neural network for the prediction of cyclic peptide membrane penetration ability.Bioinformatics (Oxford, England) · 2024Article
- Conditional Cell-Penetrating Peptide Exposure as Selective Nanoparticle Uptake Signal.ACS applied materials & interfaces · 2024Article
- Machine learning for antimicrobial peptide identification and design.Nature reviews bioengineering · 2024Article
- Dynamic carriers for therapeutic RNA delivery.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Protein-based nanoparticles for therapeutic nucleic acid delivery.Biomaterials · 2024Review
- mCherry on Top: A Positive Read-Out Cellular Platform for Screening DMD Exon Skipping Xenopeptide-PMO Conjugates.Bioconjugate chemistry · 2023Article
- Discovery of reactive peptide inhibitors of human papillomavirus oncoprotein E6.Chemical science · 2023Article
- In-Cell Penetration Selection-Mass Spectrometry Produces Noncanonical Peptides for Antisense Delivery.ACS chemical biology · 2023Article
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- MicroRNA-375 is a therapeutic target for castration-resistant prostate cancer through the PTPN4/STAT3 axis.Experimental & molecular medicine · 2022Article
- Can pulmonary RNA delivery improve our pandemic preparedness?Journal of controlled release : official journal of the Controlled Release Society · 2022Article
- Cell-Penetrating d-Peptides Retain Antisense Morpholino Oligomer Delivery Activity.ACS bio & med chem Au · 2022Article
- Biological Membrane-Penetrating Peptides: Computational Prediction and Applications.Frontiers in cellular and infection microbiology · 2022Review
- Synthetic Molecular Evolution of Cell Penetrating Peptides.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Cell-Penetrating Peptides.Methods in molecular biology (Clifton, N.J.) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.