A probabilistic model for secondary structure prediction from protein chemical shifts
A probabilistic model for secondary structure prediction from protein chemical shifts
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DOI：
10.1002/prot.24249
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发表时间：
2013-06-01
影响因子：
2.9
通讯作者：
Habeck, Michael
中科院分区：
文献类型：
--
作者：
Mechelke, Martin;Habeck, Michael
蛋白质化学位移编码了详细的结构信息，这些信息在基本水平上很难描述，而且计算成本很高。统计和机器学习方法已经被用来从实验化学位移中推断化学位移和二级结构之间的相关性。这些方法从简单的统计方法，如化学位移指数，到使用神经网络的复杂方法。尽管精度较高，但更复杂的方法往往会模糊二级结构和化学位移之间的关系，并且往往涉及许多需要训练的参数。我们提出了具有高斯发射概率的隐马尔可夫模型(HMM)来模拟蛋白质化学位移和二级结构之间的依赖关系。对于给定的氨基酸和二级结构类型，连续发射概率被建模为条件概率。使用这些分布作为一阶和二阶隐马尔可夫模型的输出，我们获得了82.3%的预测准确率，这与现有的从蛋白质化学位移预测二级结构的方法具有竞争力。将基于序列的二级结构预测引入到我们的隐马尔可夫模型中，预测准确率提高到84.0%。我们的发现表明，以二级结构为条件的相关高斯分布的隐马尔可夫模型提供了一个适当的化学位移的生成模型。《蛋白质》2013年；(C)2012年，威利期刊公司。
Protein chemical shifts encode detailed structural information that is difficult and computationally costly to describe at a fundamental level. Statistical and machine learning approaches have been used to infer correlations between chemical shifts and secondary structure from experimental chemical shifts. These methods range from simple statistics such as the chemical shift index to complex methods using neural networks. Notwithstanding their higher accuracy, more complex approaches tend to obscure the relationship between secondary structure and chemical shift and often involve many parameters that need to be trained. We present hidden Markov models (HMMs) with Gaussian emission probabilities to model the dependence between protein chemical shifts and secondary structure. The continuous emission probabilities are modeled as conditional probabilities for a given amino acid and secondary structure type. Using these distributions as outputs of first- and second-order HMMs, we achieve a prediction accuracy of 82.3%, which is competitive with existing methods for predicting secondary structure from protein chemical shifts. Incorporation of sequence-based secondary structure prediction into our HMM improves the prediction accuracy to 84.0%. Our findings suggest that an HMM with correlated Gaussian distributions conditioned on the secondary structure provides an adequate generative model of chemical shifts. Proteins 2013; (c) 2012 Wiley Periodicals, Inc.