ArticleScience advances2025
Massive experimental quantification allows interpretable deep learning of protein aggregation.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Emergence and evolution of protein-coding de novo genes.Nature reviews. Genetics · 2026Review
- Ultra-Broadband Microwave Absorption and Programmable Multispectral Camouflage Enabled by Neural-Network-Driven Impedance-Gradient Metadevices.Nano-micro letters · 2026Article
- amyloid-predict and LLPS-predict: Predicting phase separation propensities in the intrinsically disordered proteome.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Predicting Peptide Aggregation with Protein Language Model Embeddings.Journal of chemical information and modeling · 2026Article
- Black-box data: a new paradigm for biomedicine in the AI era.Chemical science · 2026Review
- Experimentally validated deep learning control of protein aggregation.Communications chemistry · 2026Article
- AFP-GFuse: an antifungal peptide identification model with structural information fusion via multi-graph neural networks and cross-attention mechanism.Molecular diversity · 2026Article
- Concentration-dependent mutational scanning probes the cellular folding landscape of α-synuclein in yeast.Protein science : a publication of the Protein Society · 2026Article
- A predictive framework for stop-loss variants with C-terminal extensions.Nucleic acids research · 2026Article
- Dosa: A method to covalently barcode proteins for high-throughput biochemistry.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- PLM-eXplain: divide and conquer the protein embedding space.Bioinformatics (Oxford, England) · 2026Article
- Concentration-Dependent Mutational Scanning Probes the Cellular Folding Landscape of α-Synuclein in Yeast.bioRxiv : the preprint server for biology · 2025Article
- Global Analysis of Aggregation Determinants in Small Protein Domains.bioRxiv : the preprint server for biology · 2025Article
- Dosa: A method to covalently barcode proteins for high throughput biochemistry.bioRxiv : the preprint server for biology · 2025Article
- Learning sequence-function relationships with scalable, interpretable Gaussian processes.bioRxiv : the preprint server for biology · 2025Article
- Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation.Biomolecules · 2025Article
- Massively parallel genetic perturbation suggests the energetic structure of an amyloid-β transition state.Science advances · 2025Article
- Bridging prion biology and Alzheimer's disease: from pathogenic seeds to precision therapeutics.Frontiers in molecular neuroscience · 2025Review
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7 authors.
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Abstract
Protein aggregation is a pathological hallmark of more than 50 human diseases and a major problem for biotechnology. Methods have been proposed to predict aggregation from sequence, but these have been trained and evaluated on small and biased experimental datasets. Here we directly address this data shortage by experimentally quantifying the aggregation of >100,000 protein sequences. This unprecedented dataset reveals the limited performance of existing computational methods and allows us to train CANYA, a convolution-attention hybrid neural network that accurately predicts aggregation from sequence. We adapt genomic neural network interpretability analyses to reveal CANYA's decision-making process and learned grammar. Our results illustrate the power of massive experimental analysis of random sequence-spaces and provide an interpretable and robust neural network model to predict aggregation.
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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.