Protein homology model refinement by large-scale energy optimization

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Improved protein structure refinement guided by deep learning based accuracy estimation (2020, methodology): “A separate 7 targets from Park et al (5, 21) were used to tune the restraint parameters and were excluded from the benchmarking in this study.”
Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, methodology): “22,44 In CASP13, we ran short MD simulations to obtain RMSFs for residues and directly assigned those values as the predicted local errors.”
High-accuracy refinement using Rosetta in CASP13 (2019, positive): “Overall, the framework is similar to that in our previous studies 1,2 ; here we provide a brief description.”
Evaluation of model refinement in CASP13 (2019, methodology): “other hand, the Baker-Autorefine method that includes more aggressive conformational searching led to substantial improvements in both metrics.(23) The Seok and Seok-server groups (which combined molecular dynamics approaches similar to Feig with local rebuilding) yielded a somewhat more modest impr”
Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14 (2021, unknown): “Twenty models selected by DeepAccNet-MSA from the entire iterations were subjected to 10 iterations of all-atom refinement protocol, 22 and the top 5 models selected by DeepAccNet-MSA at the end were submitted.”