Goldman N, Thorne J L, Jones D T
Department of Genetics, University of Cambridge, UK.
J Mol Biol. 1996 Oct 25;263(2):196-208. doi: 10.1006/jmbi.1996.0569.
Previously proposed methods for protein secondary structure prediction from multiple sequence alignments do not efficiently extract the evolutionary information that these alignments contain. The predictions of these methods are less accurate than they could be, because of their failure to consider explicitly the phylogenetic tree that relates aligned protein sequences. As an alternative, we present a hidden Markov model approach to secondary structure prediction that more fully uses the evolutionary information contained in protein sequence alignments. A representative example is presented, and three experiments are performed that illustrate how the appropriate representation of evolutionary relatedness can improve inferences. We explain why similar improvement can be expected in other secondary structure prediction methods and indeed any comparative sequence analysis method.
先前提出的从多序列比对预测蛋白质二级结构的方法，无法有效提取这些比对中所包含的进化信息。由于未能明确考虑与比对后的蛋白质序列相关的系统发育树，这些方法的预测准确性未能达到应有的水平。作为一种替代方法，我们提出了一种用于二级结构预测的隐马尔可夫模型方法，该方法能更充分地利用蛋白质序列比对中包含的进化信息。文中给出了一个具有代表性的例子，并进行了三个实验，这些实验说明了进化相关性的适当表示方式如何能够改进推理。我们解释了为什么在其他二级结构预测方法以及实际上任何比较序列分析方法中都有望实现类似的改进。
