Predicting GPCR-G-protein coupling using hidden Markov models
Predicting GPCR-G-protein coupling using hidden Markov models
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DOI：
10.1093/bioinformatics/bth434
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发表时间：
2004-12-12
期刊：
影响因子：
5.8
通讯作者：
Gulukota, K
中科院分区：
文献类型：
--
作者：
Sreekumar, KR;Huang, YP;Gulukota, K
动机：确定G蛋白偶联受体（GPCR）的偶联特异性对于理解这类重要蛋白的生物学是重要的。目前可在硅片上预测GPCR-G-蛋白偶联特异性的方法有很高的错误率。方法：我们介绍了一种新的方法，用于创建隐马尔可夫模型（HALGORY）的基础上的第一个猜测的各种残基的重要性。我们称这些知识受限的HMM强调的事实是，HMM的状态空间是由先验知识的应用程序的限制。具体地，我们仅使用可能与G蛋白相互作用的GPCR的那些氨基酸残基，即预测在细胞内环中的那些。此外，我们将这些预测的循环连接成一个序列，而不是将它们视为四个不同的单元。这通过大幅减少序列长度来减少HMM状态空间。结果：我们的知识受限的基于HMM的方法来预测GPCR-G-蛋白偶联特异性的错误率为
Motivation: Determining the coupling specificity of G-protein coupled receptors (GPCRs) is important for understanding the biology of this class of pharmacologically important proteins. Currently available in silico methods for predicting GPCR-G-protein coupling specificity have high error rate.Method: We introduce a new approach for creating hidden Markov models (HMMs) based on a first guess about the importance of various residues. We call these knowledge restricted HMMs to emphasize the fact that the state space of the HMM is restricted by the application of a priori knowledge. Specifically, we use only those amino acid residues of GPCRs which are likely to interact with G-proteins, namely those that are predicted to be in the intra-cellular loops. Furthermore, we concatenate these predicted loops into one sequence rather than considering them as four disparate units. This reduces the HMM state space by drastically decreasing the sequence length.Results: Our knowledge restricted HMM based method to predict GPCR-G-protein coupling specificity has an error rate of