ArticleNature methods2026
AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed.
- Evaluation of methods for AlphaFold-based integrative modeling.bioRxiv : the preprint server for biology · 2026Article
- From Isoprene Units to Polyprenols and Dolichols: 70 Years of Polyisoprenoid Biosynthesis Research.Cell biochemistry and biophysics · 2026Review
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Article
- AlphaInterp: Mechanistic Interpretability of AlphaFold 3 Reveals How Evolutionary Information Shapes Protein Structure Prediction.bioRxiv : the preprint server for biology · 2026Article
- Statistical crystallography reveals an allosteric network in SARS-CoV-2 MCommunications biology · 2026Article
- Article
- A robust workflow for 3D imaging of human mitochondria using cryo-electron tomography.bioRxiv : the preprint server for biology · 2026Article
- AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network.Nature methods · 2026Article
- Experimental Data Driven AI Framework for Flexible Protein Conformational Reconstruction.bioRxiv : the preprint server for biology · 2026Article
- Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.Computational and structural biotechnology journal · 2026Article
- Modeling Alternative Conformational States in CASP16.Proteins · 2026Article
- Cryo-electron microscopy ensemble optimization using individual particles and physical constraints.bioRxiv : the preprint server for biology · 2025Article
- Training bias and sequence alignments shape protein-peptide docking by AlphaFold and related methods.Protein science : a publication of the Protein Society · 2025Article
- Modeling Alternative Conformational States in CASP16.bioRxiv : the preprint server for biology · 2025Article
- Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, key challenges persist in modeling side-chain packing, condition-dependent conformational changes and biomolecular interactions, largely because of limited high-quality training data. At the same time, emerging experimental techniques such as cryo-electron microscopy (cryo-EM), cryo-electron tomography (cryo-ET) and high-throughput crystallography are generating vast amounts of structural information but converting these data into mechanistically interpretable atomic models often remains difficult. Here we show that integrating experimental measurements directly into protein structure prediction can overcome these limitations. We introduce ROCKET, an augmentation of AlphaFold2 that refines predicted structures using cryo-EM, cryo-ET and X-ray crystallography data. By optimizing structures in the space of coevolutionary embeddings rather than Cartesian coordinates, ROCKET captures biologically meaningful structural variation that is inaccessible to AlphaFold2 alone and to existing automated modeling approaches, especially when the signal-to-noise ratio is low. ROCKET enables scalable, automated model building without retraining and provides a general framework for integrating experimental observables with biomolecular machine learning.
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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.