Coupled prediction of protein secondary and tertiary structure
Coupled prediction of protein secondary and tertiary structure
复制标题
DOI：
10.1073/pnas.1831973100
复制
发表时间：
2003-10-14
影响因子：
11.1
通讯作者：
Baker, D
中科院分区：
文献类型：
--
作者：
Meiler, J;Baker, D
大多数结构预测方法都忽略了蛋白质折叠中二级和三级结构形成之间的强耦合。在这项工作中，我们研究了预测三级结构中的非局域相互作用在多大程度上可用于改进二级结构预测。利用多个序列比对的用于二级结构预测的神经网络的体系结构被扩展为接受低分辨率非局部三级结构信息作为附加输入。通过使用这种修改后的网络，结合来自本机结构的三级结构信息，Q(3) 预测精度平均提高 7-10%，在独立测试数据的个别情况下提高高达 35%。通过使用 ROSETTA de novo 三级结构预测方法生成的模型中的三级结构信息，中小型单结构域蛋白质的 Q(3) 预测精度平均提高 4-5%。使用三级结构信息对在二级结构预测方面有特别大改进的蛋白质进行分析，可以深入了解从三级结构到二级结构的反馈。
The strong coupling between secondary and tertiary structure formation in protein folding is neglected in most structure prediction methods. In this work we investigate the extent to which nonlocal interactions in predicted tertiary structures can be used to improve secondary structure prediction. The architecture of a neural network for secondary structure prediction that utilizes multiple sequence alignments was extended to accept low-resolution nonlocal tertiary structure information as an additional input. By using this modified network, together with tertiary structure information from native structures, the Q(3)-prediction accuracy is increased by 7-10% on average and by up to 35% in individual cases for independent test data. By using tertiary structure information from models generated with the ROSETTA de novo tertiary structure prediction method, the Q(3)-prediction accuracy is improved by 4-5% on average for small and medium-sized single-domain proteins. Analysis of proteins with particularly large improvements in secondary structure prediction using tertiary structure information provides insight into the feedback from tertiary to secondary structure.