de la Cruz Xavier, Hutchinson E Gail, Shepherd Adrian, Thornton Janet M
Institut Català per la Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys, 23, 08018 Barcelona, Spain.
Proc Natl Acad Sci U S A. 2002 Aug 20;99(17):11157-62. doi: 10.1073/pnas.162376199. Epub 2002 Aug 12.
Although secondary structure prediction methods have recently improved, progress from secondary to tertiary structure prediction has been limited. A promising but largely unexplored route to this goal is to predict structure motifs from secondary structure knowledge. Here we present a novel method for the recognition of beta hairpins that combines secondary structure predictions and threading methods by using a database search and a neural network approach. The method successfully predicts 48 and 77%, respectively, of all of hairpin and nonhairpin beta-coil-beta motifs in a protein database. We find that the main contributors to motif recognition are predicted accessibility and turn propensities.
尽管二级结构预测方法近来已有改进，但从二级结构预测到三级结构预测的进展仍然有限。实现这一目标的一条有前景但在很大程度上尚未探索的途径是根据二级结构知识预测结构基序。在此，我们提出了一种识别β发夹的新方法，该方法通过数据库搜索和神经网络方法，将二级结构预测与穿线法相结合。该方法分别成功预测了蛋白质数据库中所有发夹型和非发夹型β-螺旋-β基序的48%和77%。我们发现，基序识别的主要贡献因素是预测的可及性和转角倾向。
