Department of Systems and Computational Biology, Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
Structure. 2013 Jun 4;21(6):891-9. doi: 10.1016/j.str.2013.04.012. Epub 2013 May 16.
A remaining challenge in protein modeling is to predict structures for sequences with no sequence similarity to any experimentally solved structure. Based on earlier observations, the library of protein backbone supersecondary structure motifs (Smotifs) saturated about a decade ago. Therefore, it should be possible to build any structure from a combination of existing Smotifs with the help of limited experimental data that are sufficient to relate the backbone conformations of Smotifs between target proteins and known structures. Here, we present a hybrid modeling algorithm that relies on an exhaustive Smotif library and on nuclear magnetic resonance chemical shift patterns without any input of primary sequence information. In a test of 102 proteins, the algorithm delivered 90 homology-model-quality models, among them 24 high-quality ones, and a topologically correct solution for almost all cases. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.
蛋白质建模的一个遗留挑战是预测与任何实验解决结构没有序列相似性的序列的结构。基于早期的观察结果，蛋白质骨架超二级结构基序 (Smotif) 库在大约十年前就已经饱和。因此，应该有可能通过使用足以将目标蛋白和已知结构之间的 Smotif 骨架构象联系起来的有限实验数据，从现有 Smotif 的组合构建任何结构。在这里，我们提出了一种混合建模算法，该算法依赖于详尽的 Smotif 库和核磁共振化学位移模式，而无需输入任何一级序列信息。在对 102 个蛋白质的测试中，该算法提供了 90 个同源模型质量模型，其中 24 个是高质量模型，并且几乎所有情况下都提供了拓扑正确的解决方案。当前的方法为解决可提供化学位移的更大蛋白质结构的建模问题开辟了一个途径。
