ArticleJACS Au2021
Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers.
Article in JACS Au, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed, 24 citations in OpenAlex.
- Article
- Directed discovery of high-loading nanoaggregates enabled by drug-matched oligo-peptide excipients.Chem · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons.ACS central science · 2024Article
- Protein-based nanoparticles for therapeutic nucleic acid delivery.Biomaterials · 2024Review
- In-Cell Penetration Selection-Mass Spectrometry Produces Noncanonical Peptides for Antisense Delivery.ACS chemical biology · 2023Article
- In Silico Screening and Optimization of Cell-Penetrating Peptides Using Deep Learning Methods.Biomolecules · 2023Article
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
- Application of Peptides in Construction of Nonviral Vectors for Gene Delivery.Nanomaterials (Basel, Switzerland) · 2022Review
- Macromolecular Structure of Linearly Arranged Eukaryotic Chromosomes.International journal of molecular sciences · 2022Review
- The Dynamic Range of Acidity: Tracking Rules for the Unidirectional Penetration of Cellular Compartments.Chembiochem : a European journal of chemical biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Therapeutic macromolecules such as proteins and oligonucleotides can be highly efficacious but are often limited to extracellular targets due to the cell's impermeable membrane. Cell-penetrating peptides (CPPs) are able to deliver such macromolecules into cells, but limited structure-activity relationships and inconsistent literature reports make it difficult to design effective CPPs for a given cargo. For example, polyarginine motifs are common in CPPs, promoting cell uptake at the expense of systemic toxicity. Machine learning may be able to address this challenge by bridging gaps between experimental data in order to discern sequence-activity relationships that evade our intuition. Our earlier data set and deep learning model led to the design of miniproteins (>40 amino acids) for antisense delivery. Here, we leveraged and expanded our model with data augmentation in the short CPP sequence space of the data set to extrapolate and discover short, low-arginine-content CPPs that would be easier to synthesize and amenable to rapid conjugation to desired cargo, and with minimal in vivo toxicity. The lead predicted peptide, termed
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.