An HMM model for coiled-coil domains and a comparison with PSSM-based predictions
An HMM model for coiled-coil domains and a comparison with PSSM-based predictions
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DOI：
10.1093/bioinformatics/18.4.617
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发表时间：
2002-04-01
期刊：
影响因子：
5.8
通讯作者：
Speed, T
中科院分区：
文献类型：
--
作者：
Delorenzi, M;Speed, T
动机：大规模的序列数据需要自动标注蛋白质结构域的方法。许多预测方法要么基于固定长度的位置特定评分矩阵（PSSM），要么基于无窗隐马尔可夫模型（HMM）。测试了这两种方法在线圈域（CCDs）中的性能。ccd的预测被频繁使用，其优化似乎是值得的。结果：我们构思了MARCOIL，一个基因组尺度上具有CCD的蛋白质识别HMM。一项交叉验证的研究表明，与传统的PSSM算法相比，MARCOIL改进了预测，特别是对于一些蛋白质家族和短ccd。这项研究旨在揭示这两种方法的内在差异。通过使用相同的氨基酸倾向，并在交叉验证过程中保持HMM的转移概率不变，避免了参数空间维度和参数值差异等潜在的混淆因素。可用性：预测程序和数据库可在http://www.wehi.edu.au/bioweb/Mauro/ MarcoilContact: delorenzi@wehi.edu.au上获得。
Motivation: Large-scale sequence data require methods for the automated annotation of protein domains. Many of the predictive methods are based either on a Position Specific Scoring Matrix (PSSM) of fixed length or on a windowless Hidden Markov Model (HMM). The performance of the two approaches is tested for Coiled-Coil Domains (CCDs). The prediction of CCDs is used frequently, and its optimization seems worthwhile.Results: We have conceived MARCOIL, an HMM for the recognition of proteins with a CCD on a genomic scale. A cross-validated study suggests that MARCOIL improves predictions compared to the traditional PSSM algorithm, especially for some protein families and for short CCDs. The study was designed to reveal differences inherent in the two methods. Potential confounding factors such as differences in the dimension of parameter space and in the parameter values were avoided by using the same amino acid propensities and by keeping the transition probabilities of the HMM constant during cross-validation.Availability: The prediction program and the databases are available at http://www.wehi.edu.au/bioweb/Mauro/ MarcoilContact: delorenzi@wehi.edu.au.