Saravanan K Mani, Selvaraj Samuel
Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, 620024, Tamil Nadu, India.
Biopolymers. 2013 Apr;100(2):148-53. doi: 10.1002/bip.22178.
Several approaches for predicting secondary structures from sequences have been developed and reached a fair accuracy. One of the most rigorous tests for these prediction methods is their ability to correctly predict identical fragments of protein sequences adopting different secondary structures in unrelated proteins. In our previous work, we obtained 30 identical octapeptide sequence fragments adopting different backbone conformations. It is of interest to find whether the presence of structurally ambivalent fragments in proteins will affect the accuracy of secondary structure prediction methods or not. Hence, in this work, we have made a systematic comparative analysis on secondary structure prediction results of 30 identical octapeptide pairs and 52 identical heptapeptide pairs adopting different conformations with the aid of segment overlap measure. The results reveal the better performance of profile-based methods such as PSIpred and JPred and misprediction by classical rule-based methods such as Garnier Osguthorpe Robson Method and Double Prediction Method. The results discussed here insist that modern secondary structure prediction methods are able to better discriminate conformationally ambivalent peptide fragments.
已经开发出几种从序列预测二级结构的方法,并达到了相当高的准确率。对这些预测方法最严格的测试之一是它们能否正确预测在不相关蛋白质中采用不同二级结构的蛋白质序列的相同片段。在我们之前的工作中,我们获得了30个采用不同主链构象的相同八肽序列片段。研究蛋白质中结构模糊片段的存在是否会影响二级结构预测方法的准确性是很有意义的。因此,在这项工作中,我们借助片段重叠度量对30对相同的八肽对和52对相同的七肽对采用不同构象的二级结构预测结果进行了系统的比较分析。结果显示基于轮廓的方法(如PSIpred和JPred)表现更好,而经典的基于规则的方法(如Garnier Osguthorpe Robson方法和双重预测方法)存在错误预测。这里讨论的结果表明现代二级结构预测方法能够更好地区分构象模糊的肽片段。