ArticleNature chemistry2021
Deep learning to design nuclear-targeting abiotic miniproteins.
Article in Nature chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.International journal of molecular sciences · 2023Pooled it
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Kinetics, thermodynamics, and mechanisms of PMO interactions from computational molecular modeling.Molecular therapy. Nucleic acids · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Discovering Plastic-Binding Peptides with Favorable Affinity, Water Solubility, and Binding Specificity Through Deep Learning and Biophysical Modeling.bioRxiv : the preprint server for biology · 2026Article
- AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides.Journal of advanced research · 2026Article
- Computational design of superstable proteins through maximized hydrogen bonding.Nature chemistry · 2026Article
- Beyond natural amino acids: extending immunogenicity risk assessment to modified peptides through chemical feature encoding. A case study on citrullination.Frontiers in immunology · 2026Article
- pCPPs-sADNN: predicting cell-penetrating peptides using self-attention based deep neural network.Scientific reports · 2025Article
- LassoESM a tailored language model for enhanced lasso peptide property prediction.Nature communications · 2025Article
- Molecular properties and intramolecular interactions of peptide-conjugated phosphorodiamidate morpholino oligonucleotides.Molecular therapy. Nucleic acids · 2025Article
- PepINVENT: generative peptide design beyond natural amino acids.Chemical science · 2025Article
- Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder.Nature communications · 2025Article
- Hastened Fusion-Dependent Endosomal Escape Improves Activity of Delivered Enzyme Cargo.ACS central science · 2025Article
- Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning.eLife · 2025Article
- Molecular Dynamics (MD)-Derived Features for Canonical and Noncanonical Amino Acids.Journal of chemical information and modeling · 2025Article
- TG-CDDPM: text-guided antimicrobial peptides generation based on conditional denoising diffusion probabilistic model.Briefings in bioinformatics · 2024Article
- Machine learning for antimicrobial peptide identification and design.Nature reviews bioengineering · 2024Article
- 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
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables the de novo design of abiotic nuclear-targeting miniproteins to traffic antisense oligomers to the nucleus of cells. We combined high-throughput experimentation with a directed evolution-inspired deep-learning approach in which the molecular structures of natural and unnatural residues are represented as topological fingerprints. The model is able to predict activities beyond the training dataset, and simultaneously deciphers and visualizes sequence-activity predictions. The predicted miniproteins, termed 'Mach', reach an average mass of 10 kDa, are more effective than any previously known variant in cells and can also deliver proteins into the cytosol. The Mach miniproteins are non-toxic and efficiently deliver antisense cargo in mice. These results demonstrate that deep learning can decipher design principles to generate highly active biomolecules that are unlikely to be discovered by empirical approaches.
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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.