Rohl Carol A, Baker David
Department of Biochemistry, University of Washington, Seattle, WA 98195-7350, USA.
J Am Chem Soc. 2002 Mar 20;124(11):2723-9. doi: 10.1021/ja016880e.
As genome-sequencing projects rapidly increase the database of protein sequences, the gap between known sequences and known structures continues to grow exponentially, increasing the demand to accelerate structure determination methods. Residual dipolar couplings (RDCs) are an attractive source of experimental restraints for NMR structure determination, particularly rapid, high-throughput methods, because they yield both local and long-range orientational information and can be easily measured and assigned once the backbone resonances of a protein have been assigned. While very extensive RDC data sets have been used to determine the structure of ubiquitin, it is unclear to what extent such methods will generalize to larger proteins with less complete data sets. Here we incorporate experimental RDC restraints into Rosetta, an ab initio structure prediction method, and demonstrate that the combined algorithm provides a general method for de novo determination of a variety of protein folds from RDC data. Backbone structures for multiple proteins up to approximately 125 residues in length and spanning a range of topological complexities are rapidly and reproducibly generated using data sets that are insufficient in isolation to uniquely determine the protein fold de novo, although ambiguities and errors are observed for proteins with symmetry about an axis of the alignment tensor. The models generated are not high-resolution structures completely defined by experimental data but are sufficiently accurate to accelerate traditional high-resolution NMR structure determination and provide structure-based functional insights.
随着基因组测序项目迅速扩充蛋白质序列数据库，已知序列与已知结构之间的差距继续呈指数级增长，这使得加快结构测定方法的需求日益增加。剩余偶极耦合（RDCs）是核磁共振（NMR）结构测定中一种颇具吸引力的实验约束来源，对于快速、高通量方法而言尤其如此，因为它们能提供局部和远程取向信息，并且一旦蛋白质的主链共振被确定，就可以轻松测量和归属。虽然已经使用了非常广泛的RDC数据集来确定泛素的结构，但尚不清楚此类方法在多大程度上能推广到数据集不太完整的更大蛋白质上。在这里，我们将实验性RDC约束纳入从头算结构预测方法Rosetta中，并证明该组合算法提供了一种从RDC数据中从头确定多种蛋白质折叠的通用方法。使用单独不足以唯一确定蛋白质折叠的数据集，能快速且可重复地生成多种长度达约125个残基且拓扑复杂度各异的蛋白质的主链结构，不过对于关于对齐张量轴具有对称性的蛋白质，会观察到模糊性和误差。生成的模型并非由实验数据完全定义的高分辨率结构，但足够准确以加速传统的高分辨率NMR结构测定，并提供基于结构的功能见解。
