Dept. of Teoría de la Señal Telemática y Comunicaciones, Universidad de Granada, Granada, Spain.
Graz University of Technology, Signal Processing and Speech Communication Laboratory, Graz, Austria.
PLoS One. 2018 Jun 1;13(6):e0197912. doi: 10.1371/journal.pone.0197912. eCollection 2018.
The query-template alignment of proteins is one of the most critical steps of template-based modeling methods used to predict the 3D structure of a query protein. This alignment can be interpreted as a temporal classification or structured prediction task and first order Conditional Random Fields have been proposed for protein alignment and proven to be rather successful. Some other popular structured prediction problems, such as speech or image classification, have gained from the use of higher order Conditional Random Fields due to the well known higher order correlations that exist between their labels and features. In this paper, we propose and describe the use of higher order Conditional Random Fields for query-template protein alignment. The experiments carried out on different public datasets validate our proposal, especially on distantly-related protein pairs which are the most difficult to align.
蛋白质的查询模板比对是基于模板的建模方法中最关键的步骤之一，该方法用于预测查询蛋白质的 3D 结构。这种比对可以解释为时间分类或结构预测任务，并且已经提出了一阶条件随机场用于蛋白质比对，并被证明非常成功。其他一些流行的结构预测问题，如语音或图像分类，由于标签和特征之间存在众所周知的高阶相关性，因此从使用高阶条件随机场中受益。在本文中，我们提出并描述了高阶条件随机场在查询模板蛋白质比对中的使用。在不同的公共数据集上进行的实验验证了我们的建议，特别是在最难以对齐的远距离相关蛋白质对上。
