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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Protein tertiary structure prediction using hidden Markov model based on lattice
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Protein tertiary structure prediction using hidden Markov model based on lattice

机译：基于格子的隐马尔可夫模型蛋白质三级结构预测

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摘要

The prediction of protein structure from its amino acid sequence is one of the most prominent problems in computational biology. The biological function of a protein depends on its tertiary structure which is determined by its amino acid sequence via the process of protein folding. We propose a novel fold recognition method for protein tertiary structure prediction based on a hidden Markov model and 3D coordinates of amino acid residues. The method introduces states based on the basis vectors in Bravais cubic lattices to learn the path of amino acids of the proteins of each fold. Three hidden Markov models are considered based on simple cubic, body-centered cubic (BCC) and face-centered cubic (FCC) lattices. A 10-fold cross validation was performed on a set of 42 fold SCOP dataset. The proposed composite methodology is compared to fold recognition methods which have HMM as base of their algorithms having approaches on only amino acid sequence or secondary structure. The accuracy of proposed model based on face-centered cubic lattices is quite better in comparison with SAM, 3-HMM optimized and Markov chain optimized in overall experiment. The huge data of 3D space help the model to have greater performance in comparison to methods which use only primary structures or only secondary structures.
机译：从其氨基酸序列预测蛋白质结构是计算生物学中最突出的问题之一。蛋白质的生物学功能取决于其三级结构，其通过其氨基酸序列通过蛋白质折叠方法确定。我们提出了一种基于隐马尔可夫模型和氨基酸残基的3D坐标的蛋白质三级结构预测的新型折叠识别方法。该方法基于BRAVAIS立方体格子中的基载体介绍各种状态，以学习每个折叠蛋白的氨基酸的路径。基于简单的立方体，以基本为中心的立方（BCC）和面对中心的立方（FCC）格子，考虑了三种隐藏的马尔可夫模型。在一组42折SCOP数据集上执行10倍的交叉验证。将所提出的复合方法与折叠识别方法进行比较，其将HMM作为其算法的基础，其算法仅具有氨基酸序列或二级结构的方法。基于面对面的立方格子的提出模型的准确性与SAM，3-HMM优化和Markov链在整体实验中优化的比较方面更好。 3D空间的巨大数据有助于模型与仅使用主要结构或仅限二次结构的方法相比，具有更大的性能。

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