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首页> 外文期刊>Current Bioinformatics >GPCRTOP: A Novel G Protein-Coupled Receptor Topology Prediction Method Based on Hidden Markov Model Approach Using Viterbi Algorithm
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GPCRTOP: A Novel G Protein-Coupled Receptor Topology Prediction Method Based on Hidden Markov Model Approach Using Viterbi Algorithm

机译：GPCRTOP：一种新的基于维特比算法的隐马尔可夫模型方法的G蛋白偶联受体拓扑预测方法

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摘要

Knowledge about the topology of G protein-coupled receptors (GPCRs) can be very useful in predicting diverse range of properties about these proteins, such as function,three dimensional structure, and ligand binding site. Considering that only few GPCRs have known structures, many computational efforts have been carried out to develop methods for predicting their topology. A novel method to predict the location and the length of transmembrane helices in GPCRs was proposed. This method consists of a "one by one" amino acid feature extraction window which makes it possible for the method to learn the amino acid distribution in helical segments of GPCR proteins. It is based on hidden Markov model (HMM) with a specific architecture that takes advantage of Viterbi decoding algorithm and the observed frequency values for adjusting the transition probabilities. The prediction capability of the method was evaluated for per-protein, per-segment and per-residue accuracies on two datasets consisting of 649 (at least one GPCR from each family) and 2898 (all GPCRs) sequences extracted from UniProt database and compared with other commonly used existing methods. It was found that in all three assessments, the prediction accuracies for the new method on the larger dataset,i.e., 2898 GPCRs, were higher than that obtained by other methods. The results showed that our method was able to predict the topology of GPCR proteins without any sequence length limitation with the accuracies of 88.9 % and 87.4% for the small (i.e., 649 GPCRs) and large (i.e., 2898 GPCRs) datasets, respectively. (Availability status: The source code is available upon request from the authors)
机译：有关G蛋白偶联受体（GPCR）拓扑的知识对于预测这些蛋白的多种特性（例如功能，三维结构和配体结合位点）非常有用。考虑到只有很少的GPCR具有已知的结构，因此已经进行了许多计算努力来开发预测其拓扑的方法。提出了一种预测GPCRs中跨膜螺旋的位置和长度的新方法。该方法由“一对一”氨基酸特征提取窗口组成，这使该方法有可能学习GPCR蛋白螺旋段中的氨基酸分布。它基于具有特定架构的隐马尔可夫模型（HMM），该架构利用了Viterbi解码算法和观察到的频率值来调整过渡概率。在从UniProt数据库中提取的649个序列（每个家族至少一个GPCR）和2898个序列（所有GPCR）组成的两个数据集上，评估了该方法对每种蛋白质，每个片段和每个残基的准确性的预测能力，并与其他常用的现有方法。结果发现，在所有这三个评估中，新方法在较大数据集（即2898个GPCR）上的预测准确性均高于其他方法。结果表明，我们的方法能够预测GPCR蛋白的拓扑结构而不受任何序列长度的限制，对于小型（即649个GPCR）和大型（即2898个GPCR）数据集，其准确性分别为88.9％和87.4％。 （可用性状态：源代码可应作者要求提供）

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