.png){kind=icon}
Return
CASP11 refinement experiments with ROSETTA
.png){kind=icon}
OA
AI
DOI：10.1002/prot.24862.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We report new Rosetta-based approaches to tackling the major issues that confound protein structure refinement, and the testing of these approaches in the CASP11 experiment. Automated refinement protocols were developed that integrate a range of sampling methods using parallel computation and multiobjective optimization. In CASP11, we used a more aggressive large-scale structure rebuilding approach for poor starting models, and a less aggressive local rebuilding plus core refinement approach for starting models likely to be closer to the native structure. The more incorrectly modeled a structure was predicted to be, the more it was allowed to vary during refinement. The CASP11 experiment revealed strengths and weaknesses of the approaches: the high-resolution strategy incorporating local rebuilding with core refinement consistently improved starting structures, while the low-resolution strategy incorporating the reconstruction of large parts of the structures improved starting models in some cases but often considerably worsened them, largely because of model selection issues. Overall, the results suggest the high-resolution refinement protocol is a promising method orthogonal to other approaches, while the low-resolution refinement method clearly requires further development. (C) 2015 Wiley Periodicals, Inc.
Keywords:
structure prediction
structure refinement
protein loop modeling
protein homology modeling
Monte Carlo simulation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
P
IF:
2.8
Papers:
6.6K
Citations:
1.4W
Organization
Cited Papers
ShareSaveGOAP: A Generalized Orientation-Dependent, All-Atom Statistical Potential for Protein Structure Prediction.png)
BIOPHYSICAL JOURNALOAAI
BIOPHYSICAL JOURNALIF3.1
OAAIShareSaveAtomic-accuracy models from 4.5-Å cryo-electron microscopy data with density-guided iterative local refinementNATURE METHODSOAAI
NATURE METHODSIF32.1
OAAIShareSaveShareSaveShareSaveShareSave
View more