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首页> 外文期刊>BMC Bioinformatics >Template-based C8-SCORPION: a protein 8-state secondary structure prediction method using structural information and context-based features
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Template-based C8-SCORPION: a protein 8-state secondary structure prediction method using structural information and context-based features

机译：基于模板的C8-SCORPION：使用结构信息和基于上下文的特征的蛋白质8状态二级结构预测方法

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BackgroundSecondary structures prediction of proteins is important to many protein structure modeling applications. Correct prediction of secondary structures can significantly reduce the degrees of freedom in protein tertiary structure modeling and therefore reduces the difficulty of obtaining high resolution 3D models.MethodsIn this work, we investigate a template-based approach to enhance 8-state secondary structure prediction accuracy. We construct structural templates from known protein structures with certain sequence similarity. The structural templates are then incorporated as features with sequence and evolutionary information to train two-stage neural networks. In case of structural templates absence, heuristic structural information is incorporated instead.ResultsAfter applying the template-based 8-state secondary structure prediction method, the 7-fold cross-validated Q8 accuracy is 78.85%. Even templates from structures with only 20%~30% sequence similarity can help improve the 8-state prediction accuracy. More importantly, when good templates are available, the prediction accuracy of less frequent secondary structures, such as 3-10 helices, turns, and bends, are highly improved, which are useful for practical applications.ConclusionsOur computational results show that the templates containing structural information are effective features to enhance 8-state secondary structure predictions. Our prediction algorithm is implemented on a web server named "C8-SCORPION" available at: http://hpcr.cs.odu.edu/c8scorpion.
机译：背景技术蛋白质的二级结构预测对于许多蛋白质结构建模应用很重要。正确预测二级结构可以显着降低蛋白质三级结构建模的自由度，从而降低获得高分辨率3D模型的难度。方法在这项工作中，我们研究了基于模板的方法来提高8状态二级结构预测的准确性。我们从具有一定序列相似性的已知蛋白质结构构建结构模板。然后将结构模板作为具有序列和进化信息的特征并入以训练两阶段神经网络。结果在应用基于模板的八态二级结构预测方法后，交叉验证的Q8准确性为78.85％。即使来自序列相似性仅为20％〜30％的结构的模板也可以帮助提高八态预测精度。更重要的是，当有好的模板可用时，不经常使用的二级结构（例如3-10个螺旋，转弯和弯曲）的预测精度会大大提高，这对实际应用是有用的。结论我们的计算结果表明，包含结构的模板信息是增强八态二级结构预测的有效特征。我们的预测算法在名为“ C8-SCORPION”的Web服务器上实现，该服务器可从以下网站获得：http://hpcr.cs.odu.edu/c8scorpion。

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