CASP11 refinement experiments with ROSETTA

Abstract

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 multi-objective 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.

High‐accuracy refinement using Rosetta in CASP13

“…Model 1 from our human prediction has a GDT‐HA of 93 and Cα‐RMSD of 0.48 å. Post analysis on this target indicated a complementary role of Rosetta and explicit water MD as observed previously: while Rosetta successfully recovered a critical error at the C‐terminus and side‐chain orientations around it, MD successfully brought the positions of backbones and side‐chains even closer to the crystal structure. Our MD refinement alone on the input model did not result in this level of accuracy (GDT‐HA of 83 and RMSD 1.6 å).…”

“…It has been consistently observed both in CASP and in our previous studies that refinement of proteins larger than 120 residues is very difficult due to the rapidly increasing size of conformational space. This is likely why we did not observe significant improvements with our low‐resolution protocol in this CASP: there were no refinement targets shorter than 110 residues in this category (when starting model's GDT‐HA <50).…”

Distinct structural mechanisms determine substrate affinity and kinase activity of protein kinase Cα

“…The Rosetta software suite has many functionalities that have been shown to perform well consistently, for protein structure refinement and side chain repacking 4 . We have previously combined the GneimoSim software 5 with the Rosetta software to (1) perform torsion MD simulations with Rosetta forcefield 6 and to (2) enhance the conformational sampling during protein structure refinement by combining the benefits torsional MD in GNEIMO with the torsional Monte Carlo sampling and side chain repacking in Rosetta using a rotamer library.…”

Rotamer Libraries for the High-Resolution Design of β-Amino Acid Foldamers

“…Since the total number of β-amino acid rotamers in the PDB is so small, we cannot perform this analysis on only those rotamers with sufficiently high-quality structural data supporting their conformations. Rather, we propose that, with the recent development of new structural refinement tools within Rosetta (Park et al, 2015; Chou et al, 2013), these and other β-amino acid containing structures may be further refined, particularly now that there exist general, backbone-dependent rotamer libraries for the task.…”