ArticleBiopolymers2012
Ab initio protein modeling into CryoEM density maps using EM-Fold.
Article in Biopolymers, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
21 citing papers in PubMed.
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.Journal of chemical theory and computation · 2026Article
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.bioRxiv : the preprint server for biology · 2026Article
- Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design.Frontiers in pharmacology · 2022Review
- Prediction of Protein Complex Structure Using Surface-Induced Dissociation and Cryo-Electron Microscopy.Analytical chemistry · 2021Article
- Hybrid methods for combined experimental and computational determination of protein structure.The Journal of chemical physics · 2020Review
- Using NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD Protein Structure Refinement.Journal of chemical information and modeling · 2020Article
- Simulation-Based Methods for Model Building and Refinement in Cryoelectron Microscopy.Journal of chemical information and modeling · 2020Review
- Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data.ACS central science · 2019Article
- Utility of Covalent Labeling Mass Spectrometry Data in Protein Structure Prediction with Rosetta.Journal of chemical theory and computation · 2019Article
- Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data.Analytical chemistry · 2018Article
- Finding the needle in the haystack: towards solving the protein-folding problem computationally.Critical reviews in biochemistry and molecular biology · 2018Review
- Iterative Molecular Dynamics-Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities.Journal of chemical theory and computation · 2017Article
- CASP11--An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline.PloS one · 2016Article
- Constraint methods that accelerate free-energy simulations of biomolecules.The Journal of chemical physics · 2015Review
- Protein structure prediction guided by crosslinking restraints--A systematic evaluation of the impact of the crosslinking spacer length.Methods (San Diego, Calif.) · 2015Article
- Improved cryoEM-Guided Iterative Molecular Dynamics--Rosetta Protein Structure Refinement Protocol for High Precision Protein Structure Prediction.Journal of chemical theory and computation · 2015Article
- Integrative Modeling of Macromolecular Assemblies from Low to Near-Atomic Resolution.Computational and structural biotechnology journal · 2015Review
- Computational modeling of membrane proteins.Proteins · 2015Review
- Collective variable approaches for single molecule flexible fitting and enhanced sampling.Chemical reviews · 2014Review
- Iterative Molecular Dynamics-Rosetta Protein Structure Refinement Protocol to Improve Model Quality.Journal of chemical theory and computation · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
EM-Fold was used to build models for nine proteins in the maps of GroEL (7.7 Å resolution) and ribosome (6.4 Å resolution) in the ab initio modeling category of the 2010 cryo-electron microscopy modeling challenge. EM-Fold assembles predicted secondary structure elements (SSEs) into regions of the density map that were identified to correspond to either α-helices or β-strands. The assembly uses a Monte Carlo algorithm where loop closure, density-SSE length agreement, and strength of connecting density between SSEs are evaluated. Top-scoring models are refined by translating, rotating, and bending SSEs to yield better agreement with the density map. EM-Fold produces models that contain backbone atoms within SSEs only. The RMSD values of the models with respect to native range from 2.4 to 3.5 Å for six of the nine proteins. EM-Fold failed to predict the correct topology in three cases. Subsequently, Rosetta was used to build loops and side chains for the very best scoring models after EM-Fold refinement. The refinement within Rosetta's force field is driven by a density agreement score that calculates a cross-correlation between a density map simulated from the model and the experimental density map. All-atom RMSDs as low as 3.4 Å are achieved in favorable cases. Values above 10.0 Å are observed for two proteins with low overall content of secondary structure and hence particularly complex loop modeling problems. RMSDs over residues in secondary structure elements range from 2.5 to 4.8 Å.
Indexed as
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.