ArticleStructure (London, England : 1993)2024
One bead per residue can describe all-atom protein structures.
Article in Structure (London, England : 1993), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Modeling conformational transitions in DNA, RNA, and protein-nucleic acid complexes.Nucleic acids research · 2026Article
- Integrative structural analysis of human endosomal NHE6 reveals a lipid-associated gate and disordered C-terminus.Nature communications · 2026Article
- AF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome level.Protein science : a publication of the Protein Society · 2026Article
- CondenSimAdapter: A Versatile Builder for Multiscale Simulations of Protein Condensates with Broad Force-Field Compatibility and Robust Dense-Phase Relaxation.Journal of chemical information and modeling · 2026Article
- Molecular dynamics simulations of intrinsically disordered protein regions enable biophysical interpretation of variant-effect predictors.HGG advances · 2026Article
- HyRes: Accurate Physics-Based Simulation of Dynamic Protein Structures and Interactions in Complex Environments at Scale.bioRxiv : the preprint server for biology · 2026Article
- High-Rate Fingerprinting of Protein Isoforms by Quasi-regulated Enzyme-free Transport Through CytK Nanopores.Research square · 2026Article
- An optimized contact map for GōMartini 3 enabling conformational changes in protein assemblies.Biophysical journal · 2026Article
- Detecting misfolded non-covalent lasso entanglements in protein structures, simulation trajectories, and mass spectrometry data.bioRxiv : the preprint server for biology · 2026Article
- AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network.Nature methods · 2026Article
- Learning data-efficient coarse-grained molecular dynamics from forces and noise.Nature communications · 2026Article
- Efficient sampling of large-scale transition pathways and intermediate conformations in sub-mesoscopic protein complexes.Nature communications · 2026Article
- Multivalent Protein Nanorings for Broad and Potent SARS-CoV-2 Neutralization.Advanced healthcare materials · 2026Article
- Accurate conformational ensembles of intrinsically disordered proteins using reweighting based on NMR chemical shifts.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Structural organization of HBV pgRNA genome driven by phase separation in capsid confinement.Nature communications · 2026Article
- Biomolecular Condensates Dictate the Folding Landscape of Proteins.bioRxiv : the preprint server for biology · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Dormancy Versus Germination: 3D Protein Modeling and Evolutionary Analyses Define the Roles of Genetic Variants in the Barley MKK3 Enzyme.International journal of molecular sciences · 2026Article
- EGCPPIS: learning hierarchical equivariant graph representations with contrastive integration for protein-protein interaction site identification.BMC bioinformatics · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Atomistic resolution is the standard for high-resolution biomolecular structures, but experimental structural data are often at lower resolution. Coarse-grained models are also used extensively in computational studies to reach biologically relevant spatial and temporal scales. This study explores the use of advanced machine learning networks for reconstructing atomistic models from reduced representations. The main finding is that a single bead per amino acid residue allows construction of accurate and stereochemically realistic all-atom structures with minimal loss of information. This suggests that lower resolution representations of proteins may be sufficient for many applications when combined with a machine learning framework that encodes knowledge from known structures. Practical applications include the rapid addition of atomistic detail to low-resolution structures from experiment or computational coarse-grained models. The application of rapid, deterministic all-atom reconstruction within multi-scale frameworks is further demonstrated with a rapid protocol for the generation of accurate models from cryo-EM densities close to experimental structures.
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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.