Structure prediction for CASP8 with all-atom refinement

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.

我们描述了在第八届蛋白质结构预测技术关键评估中使用罗塞塔（Rosetta）结构预测方法所做的预测。对几乎所有目标都进行了积极的采样和全原子优化。综合使用了多种比对方法，从一系列模板生成起始模型，然后对这些模型进行罗塞塔全原子优化。对于64个容易确定模板的结构域，在24个案例中，提交的最佳模型优于蛋白质数据库中最佳模板的最佳比对结果，在43个案例中优于最佳起始模型。对于13个仅检测到与已知结构蛋白质有非常远的序列关系的目标，使用罗塞塔从头结构预测方法生成模型，随后进行全原子优化；在几个案例中，提交的模型优于基于现有模板的模型。在12个优化挑战中，提交的最佳模型在7个案例中比起始模型有所改进。这些相对于基于起始模板的模型的改进以及优化测试证明了罗塞塔结构优化在提高模型准确性方面的能力。