Das Rhiju, Qian Bin, Raman Srivatsan, Vernon Robert, Thompson James, Bradley Philip, Khare Sagar, Tyka Michael D, Bhat Divya, Chivian Dylan, Kim David E, Sheffler William H, Malmström Lars, Wollacott Andrew M, Wang Chu, Andre Ingemar, Baker David
Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA.
Proteins. 2007;69 Suppl 8:118-28. doi: 10.1002/prot.21636.
We describe predictions made using the Rosetta structure prediction methodology for both template-based modeling and free modeling categories in the Seventh Critical Assessment of Techniques for Protein Structure Prediction. For the first time, aggressive sampling and all-atom refinement could be carried out for the majority of targets, an advance enabled by the Rosetta@home distributed computing network. Template-based modeling predictions using an iterative refinement algorithm improved over the best existing templates for the majority of proteins with less than 200 residues. Free modeling methods gave near-atomic accuracy predictions for several targets under 100 residues from all secondary structure classes. These results indicate that refinement with an all-atom energy function, although computationally expensive, is a powerful method for obtaining accurate structure predictions.
我们描述了在蛋白质结构预测技术第七次关键评估中,使用Rosetta结构预测方法对基于模板建模和自由建模类别所做的预测。首次能够对大多数目标进行积极采样和全原子精修,这一进展得益于Rosetta@home分布式计算网络。对于大多数少于200个残基的蛋白质,使用迭代精修算法的基于模板建模预测比现有的最佳模板有所改进。自由建模方法对来自所有二级结构类别的几个少于100个残基的目标给出了接近原子精度的预测。这些结果表明,尽管全原子能量函数精修在计算上代价高昂,但它是获得准确结构预测的一种强大方法。