Indiana University School of Informatics, Indiana University-Purdue University and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Structure. 2009 Nov 11;17(11):1515-27. doi: 10.1016/j.str.2009.09.006.
Local structures predicted from protein sequences are used extensively in every aspect of modeling and prediction of protein structure and function. For more than 50 years, they have been predicted at a low-resolution coarse-grained level (e.g., three-state secondary structure). Here, we combine a two-state classifier with real-value predictor to predict local structure in continuous representation by backbone torsion angles. The accuracy of the angles predicted by this approach is close to that derived from NMR chemical shifts. Their substitution for predicted secondary structure as restraints for ab initio structure prediction doubles the success rate. This result demonstrates the potential of predicted local structure for fragment-free tertiary-structure prediction. It further implies potentially significant benefits from using predicted real-valued torsion angles as a replacement for or supplement to the secondary-structure prediction tools used almost exclusively in many computational methods ranging from sequence alignment to function prediction.
从蛋白质序列中预测的局部结构广泛应用于蛋白质结构和功能建模和预测的各个方面。50 多年来，它们一直以低分辨率的粗粒度水平进行预测（例如，三态二级结构）。在这里，我们将二态分类器与实值预测器相结合，通过骨架扭转角以连续表示来预测局部结构。该方法预测的角度的准确性接近从 NMR 化学位移得出的结果。将它们替代预测的二级结构作为从头预测结构的约束条件，可将成功率提高一倍。这一结果表明，预测的局部结构具有无片段三级结构预测的潜力。这进一步意味着，使用预测的实值扭转角作为替代物或补充物来替代或补充几乎在从序列比对到功能预测的许多计算方法中都被广泛使用的二级结构预测工具，可能会带来显著的好处。
