IMPROVED PREDICTION OF PROTEIN SECONDARY STRUCTURE BY USE OF SEQUENCE PROFILES AND NEURAL NETWORKS
IMPROVED PREDICTION OF PROTEIN SECONDARY STRUCTURE BY USE OF SEQUENCE PROFILES AND NEURAL NETWORKS
复制标题
DOI：
10.1073/pnas.90.16.7558
复制
发表时间：
1993-08-15
影响因子：
11.1
通讯作者：
SANDER, C
中科院分区：
文献类型：
--
作者：
ROST, B;SANDER, C
随着大规模测序项目的开展，蛋白质序列的爆炸性积累与蛋白质结构的实验测定速度慢得多形成了鲜明的对比。因此，需要单独从基因序列进行结构预测的改进方法。在这里，我们报告了大量增加的准确性和质量的二级结构预测，使用神经网络算法。主要的改进来自多序列比对的使用（更好的整体准确性），来自“平衡训练”（更好地预测β链），以及来自“结构上下文训练”（更好地预测螺旋和链长度）。该方法在七个不同的测试集上进行了交叉验证，清除了与学习集的序列相似性，实现了69.7%的三状态预测准确率，明显优于以前的方法。此外，预测的结构具有更真实的螺旋和链段分布。这些预测可能适合于在实践中用作新测序蛋白质的结构类型的第一估计。
The explosive accumulation of protein sequences in the wake of large-scale sequencing projects is in stark contrast to the much slower experimental determination of protein structures. Improved methods of structure prediction from the gene sequence alone are therefore needed. Here, we report a substantial increase in both the accuracy and quality of secondary-structure predictions, using a neural-network algorithm. The main improvements come from the use of multiple sequence alignments (better overall accuracy), from ''balanced training'' (better prediction of beta-strands), and from ''structure context training'' (better prediction of helix and strand lengths). This method, cross-validated on seven different test sets purged of sequence similarity to learning sets, achieves a three-state prediction accuracy of 69.7%, significantly better than previous methods. In addition, the predicted structures have a more realistic distribution of helix and strand segments. The predictions may be suitable for use in practice as a first estimate of the structural type of newly sequenced proteins.