ArticleProteins2019
High-accuracy refinement using Rosetta in CASP13.
Article in Proteins, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Protein Retrieval via Integrative Molecular Ensembles (PRIME) through Extended Similarity Indices.Journal of chemical theory and computation · 2024Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Modeling of protein conformational changes with Rosetta guided by limited experimental data.Structure (London, England : 1993) · 2022Article
- Generalized Born Implicit Solvent Models Do Not Reproduce Secondary Structures ofJournal of chemical theory and computation · 2022Article
- Using metagenomic data to boost protein structure prediction and discovery.Computational and structural biotechnology journal · 2022Review
- Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14.Proteins · 2021Article
- Modeling SARS-CoV-2 proteins in the CASP-commons experiment.Proteins · 2021Article
- DeepRefiner: high-accuracy protein structure refinement by deep network calibration.Nucleic acids research · 2021Article
- Fast and effective protein model refinement using deep graph neural networks.Nature computational science · 2021Article
- Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?PLoS computational biology · 2021Article
- Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy.International journal of molecular sciences · 2021Article
- Improved protein structure refinement guided by deep learning based accuracy estimation.Nature communications · 2021Article
- In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19.Scientific reports · 2021Article
- Article
- Improved protein structure prediction using predicted interresidue orientations.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Homology modeling in the time of collective and artificial intelligence.Computational and structural biotechnology journal · 2020Review
- Article
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Authors and funding
6 authors.
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Abstract
Because proteins generally fold to their lowest free energy states, energy-guided refinement in principle should be able to systematically improve the quality of protein structure models generated using homologous structure or co-evolution derived information. However, because of the high dimensionality of the search space, there are far more ways to degrade the quality of a near native model than to improve it, and hence, refinement methods are very sensitive to energy function errors. In the 13th Critial Assessment of techniques for protein Structure Prediction (CASP13), we sought to carry out a thorough search for low energy states in the neighborhood of a starting model using restraints to avoid straying too far. The approach was reasonably successful in improving both regions largely incorrect in the starting models as well as core regions that started out closer to the correct structure. Models with GDT-HA over 70 were obtained for five targets and for one of those, an accuracy of 0.5 å backbone root-mean-square deviation (RMSD) was achieved. An important current challenge is to improve performance in refining oligomers and larger proteins, for which the search problem remains extremely difficult.
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