Rosenbach D, Rosenfeld R
BioMolecular Engineering Research Center, Boston University College of Engineering, Massachusetts 02215, USA.
Protein Sci. 1995 Mar;4(3):496-505. doi: 10.1002/pro.5560040316.
The most reliable methods for predicting protein structure are by way of homologous extension, using structural information from a closely related protein, or by "threading" through a set of predefined protein folds ("inverse folding"). Both sets of methods provide a model for the core of the protein--the structurally conserved secondary structures. Due to the large variability both in sequence and size of the loops that connect these secondary structures, they generally cannot be modeled using these techniques. Loop-closure algorithms are aimed at predicting loop structures, given their end-to-end distance. Various such algorithms have been described, and all have been tested by predicting the structure of a single loop in a known protein. In this paper we propose a method, which is based on the bond-scaling-relaxation loop-closure algorithm, for simultaneously predicting the structures of multiple loops, and demonstrate that, for two spatially close loops, simultaneous closure invariably leads to more accurate predictions than sequential closure. The accuracy of the predictions obtained for pairs of loops in the size range of 5-7 residues each is comparable to that obtained by other methods, when predicting the structures of single loops: the RMS deviations from the native conformations of various test cases modeled are approximately 0.6-1.7 A for backbone atoms and 1.1-3.3 A for all-atoms.
预测蛋白质结构最可靠的方法是通过同源延伸，利用来自密切相关蛋白质的结构信息，或者通过“穿线”一组预定义的蛋白质折叠（“反向折叠”）。这两种方法都能提供蛋白质核心部分的模型——结构保守的二级结构。由于连接这些二级结构的环在序列和大小上变化很大，通常无法用这些技术对其进行建模。环闭合算法旨在根据环的端到端距离预测环结构。已经描述了各种此类算法，并且所有算法都通过预测已知蛋白质中单个环的结构进行了测试。在本文中，我们提出了一种基于键缩放松弛环闭合算法的方法，用于同时预测多个环的结构，并证明对于两个空间上接近的环，同时闭合总是比顺序闭合能得到更准确的预测。当预测单个环的结构时，对于每个大小在5 - 7个残基范围内的环对所获得的预测准确性与通过其他方法获得的准确性相当：对于各种测试案例建模的主链原子与天然构象的均方根偏差约为0.6 - 1.7 Å，对于所有原子为1.1 - 3.3 Å。
