Wang Junru, Wang Cong, Cao Jiajia, Liu Xiaoqing, Yao Yuhua, Dai Qi
College of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
Gene. 2015 Jan 10;554(2):241-8. doi: 10.1016/j.gene.2014.10.037. Epub 2014 Oct 24.
Many efficient methods have been proposed to advance protein structural class prediction, but there are still some challenges where additional insight or technology is needed for low-similarity sequences. In this work, we schemed out a new prediction method for low-similarity datasets using reduced PSSM and position-based secondary structural features. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed method achieved the best performance among the evaluated methods, with overall accuracy 3-5% higher than the existing best-performing method. This paper also found that the reduced alphabets with size 13 simplify PSSM structures efficiently while reserving its maximal information. This understanding can be used to design more powerful prediction methods for protein structural class.
为推进蛋白质结构类预测，人们已经提出了许多有效的方法，但对于低相似性序列，仍存在一些挑战，需要更多的见解或技术。在这项工作中，我们设计了一种针对低相似性数据集的新预测方法，该方法使用了简化的位置特异性得分矩阵（PSSM）和基于位置的二级结构特征。我们通过四个实验对所提出的方法进行了评估，并将其与现有的竞争预测方法进行了比较。结果表明，在所评估的方法中，所提出的方法取得了最佳性能，总体准确率比现有的最佳性能方法高3-5%。本文还发现，大小为13的简化字母表在保留最大信息的同时有效地简化了PSSM结构。这一认识可用于设计更强大的蛋白质结构类预测方法。
