Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA.
Proteins. 2009;77 Suppl 9(0 9):89-99. doi: 10.1002/prot.22540.
We describe predictions made using the Rosetta structure prediction methodology for the Eighth Critical Assessment of Techniques for Protein Structure Prediction. Aggressive sampling and all-atom refinement were carried out for nearly all targets. A combination of alignment methodologies was used to generate starting models from a range of templates, and the models were then subjected to Rosetta all atom refinement. For the 64 domains with readily identified templates, the best submitted model was better than the best alignment to the best template in the Protein Data Bank for 24 cases, and improved over the best starting model for 43 cases. For 13 targets where only very distant sequence relationships to proteins of known structure were detected, models were generated using the Rosetta de novo structure prediction methodology followed by all-atom refinement; in several cases the submitted models were better than those based on the available templates. Of the 12 refinement challenges, the best submitted model improved on the starting model in seven cases. These improvements over the starting template-based models and refinement tests demonstrate the power of Rosetta structure refinement in improving model accuracy.
我们描述了使用 Rosetta 结构预测方法学进行第八次蛋白质结构预测技术关键评估的预测结果。几乎所有的目标都进行了激进的采样和全原子细化。使用多种对齐方法学从一系列模板中生成起始模型，然后对模型进行 Rosetta 全原子细化。对于 64 个具有易于识别模板的域，在 24 个情况下，提交的最佳模型优于最佳模板在蛋白质数据库中的最佳对齐，在 43 个情况下，优于最佳起始模型。对于 13 个仅检测到与已知结构蛋白质的非常远的序列关系的目标，使用 Rosetta 从头结构预测方法学生成模型，然后进行全原子细化；在几种情况下，提交的模型优于基于可用模板的模型。在 12 个细化挑战中，在 7 个情况下，最佳提交模型改进了起始模型。这些对基于起始模板模型和细化测试的改进表明了 Rosetta 结构细化在提高模型准确性方面的强大功能。
