ArticleJournal of chemical theory and computation2017
Iterative Molecular Dynamics-Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities.
Article in Journal of chemical theory and computation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- An overview on olfaction in the biological, analytical, computational, and machine learning fields.Archiv der Pharmazie · 2025Review
- Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver.Briefings in bioinformatics · 2023Article
- TMKit: a Python interface for computational analysis of transmembrane proteins.Briefings in bioinformatics · 2023Article
- Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction.Nature communications · 2022Article
- Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly.Nature communications · 2022Article
- Review
- Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps.Biomolecules · 2021Article
- Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density Maps.Journal of chemical information and modeling · 2021Article
- Accurate protein structure prediction with hydroxyl radical protein footprinting data.Nature communications · 2021Article
- Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View.Methods in molecular biology (Clifton, N.J.) · 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
- Practical Considerations for Atomistic Structure Modeling with Cryo-EM Maps.Journal of chemical information and modeling · 2020Article
- In Silico Modeling of the α7 Nicotinic Acetylcholine Receptor: New Pharmacological Challenges Associated with Multiple Modes of Signaling.Mini reviews in medicinal chemistry · 2020Review
- Improving the Efficiency of Ligand-Binding Protein Design with Molecular Dynamics Simulations.Journal of chemical theory and computation · 2019Article
- Cryo_fit: Democratization of flexible fitting for cryo-EM.Journal of structural biology · 2019Article
- Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data.ACS central science · 2019Article
- Measuring Intrinsic Disorder and Tracking Conformational Transitions Using Rosetta ResidueDisorder.The journal of physical chemistry. B · 2019Article
- Article
- Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data.Analytical chemistry · 2018Article
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Abstract
Knowing atomistic details of proteins is essential not only for the understanding of protein function but also for the development of drugs. Experimental methods such as X-ray crystallography, NMR, and cryo-electron microscopy (cryo-EM) are the preferred forms of protein structure determination and have achieved great success over the most recent decades. Computational methods may be an alternative when experimental techniques fail. However, computational methods are severely limited when it comes to predicting larger macromolecule structures with little sequence similarity to known structures. The incorporation of experimental restraints in computational methods is becoming increasingly important to more reliably predict protein structure. One such experimental input used in structure prediction and refinement is cryo-EM densities. Recent advances in cryo-EM have arguably revolutionized the field of structural biology. Our previously developed cryo-EM-guided Rosetta-MD protocol has shown great promise in the refinement of soluble protein structures. In this study, we extended cryo-EM density-guided iterative Rosetta-MD to membrane proteins. We also improved the methodology in general by picking models based on a combination of their score and fit-to-density during the Rosetta model selection. By doing so, we have been able to pick models superior to those with the previous selection based on Rosetta score only and we have been able to further improve our previously refined models of soluble proteins. The method was tested with five membrane spanning protein structures. By applying density-guided Rosetta-MD iteratively we were able to refine the predicted structures of these membrane proteins to atomic resolutions. We also showed that the resolution of the density maps determines the improvement and quality of the refined models. By incorporating high-resolution density maps (∼4 Å), we were able to more significantly improve the quality of the models than when medium-resolution maps (6.9 Å) were used. Beginning from an average starting structure root mean square deviation (RMSD) to native of 4.66 Å, our protocol was able to refine the structures to bring the average refined structure RMSD to 1.66 Å when 4 Å density maps were used. The protocol also successfully refined the HIV-1 CTD guided by an experimental 5 Å density map.
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