Tuckwell D S, Humphries M J, Brass A
School of Biological Sciences, University of Manchester, UK.
Comput Appl Biosci. 1995 Dec;11(6):627-32. doi: 10.1093/bioinformatics/11.6.627.
A number of methods exist for the prediction of protein secondary structure from primary sequence. One method identifies variable charged and conserved hydrophobic residues within large multiple alignments as a means of indicating outside and inside sites respectively in the protein structure. These sites are then manually fitted to secondary structure templates to generate a secondary structure prediction. Using the existing theoretical bases of this method, we present an algorithm (STAMA) which automatically carries out the initial variation/conservation analysis of the alignment. We also test the accuracy of complete predictions carried out by manual fitting of the STAMA-derived assignments to structure templates, using five large multiple alignments each including a protein of known structure. The method was found on average to predict only 57% of residues in the correct secondary structure, and was only as accurate as predictions carried out using the established and automated method of Garnier, Osguthorpe and Robson (1978) applied to a single sequence. When used in conjunction with other secondary structure prediction methods, however, the resulting consensus predictions were found to be very accurate, with 78% of the elements (alpha helices or beta strands) for which a consensus could be obtained being predicted correctly. The algorithm presented here, plus the assessment of the accuracy of prediction generated by this method, should enable this predictive approach to receive informed general use.
存在多种从蛋白质一级序列预测其二级结构的方法。其中一种方法是在大量多重比对中识别可变电荷残基和保守疏水残基，以此分别指示蛋白质结构中的外部和内部位点。然后将这些位点手动拟合到二级结构模板上，以生成二级结构预测。基于该方法现有的理论基础，我们提出了一种算法（STAMA），它能自动对比对进行初始的可变/保守性分析。我们还使用五个大型多重比对（每个比对都包含一个已知结构的蛋白质），测试了通过将STAMA得出的结果手动拟合到结构模板上进行完整预测的准确性。结果发现，该方法平均只能预测出处于正确二级结构中的57%的残基，其准确性仅与应用于单个序列的已确立的Garnier、Osguthorpe和Robson（1978）自动化方法的预测相当。然而，当与其他二级结构预测方法结合使用时，发现所得的一致性预测非常准确，对于能够获得一致性的元素（α螺旋或β链），有78%被正确预测。本文提出的算法，以及对该方法产生的预测准确性的评估，应能使这种预测方法得到明智的广泛应用。
