CCHMM_PROF：一种基于隐马尔可夫模型的带有进化信息的卷曲螺旋预测器。

CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information.

机构信息

Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna, Italy.

出版信息

Bioinformatics. 2009 Nov 1;25(21):2757-63. doi: 10.1093/bioinformatics/btp539. Epub 2009 Sep 10.

DOI:10.1093/bioinformatics/btp539

PMID:19744995

Abstract

MOTIVATION

The widespread coiled-coil structural motif in proteins is known to mediate a variety of biological interactions. Recognizing a coiled-coil containing sequence and locating its coiled-coil domains are key steps towards the determination of the protein structure and function. Different tools are available for predicting coiled-coil domains in protein sequences, including those based on position-specific score matrices and machine learning methods.

RESULTS

In this article, we introduce a hidden Markov model (CCHMM_PROF) that exploits the information contained in multiple sequence alignments (profiles) to predict coiled-coil regions. The new method discriminates coiled-coil sequences with an accuracy of 97% and achieves a true positive rate of 79% with only 1% of false positives. Furthermore, when predicting the location of coiled-coil segments in protein sequences, the method reaches an accuracy of 80% at the residue level and a best per-segment and per-protein efficiency of 81% and 80%, respectively. The results indicate that CCHMM_PROF outperforms all the existing tools and can be adopted for large-scale genome annotation.

AVAILABILITY

The dataset is available at http://www.biocomp.unibo.it/ approximately lisa/coiled-coils. The predictor is freely available at http://gpcr.biocomp.unibo.it/cgi/predictors/cchmmprof/pred_cchmmprof.cgi.

CONTACT

piero@biocomp.unibo.it.

摘要

动机

蛋白质中广泛存在的卷曲螺旋结构基序，已知能介导多种生物相互作用。识别包含卷曲螺旋的序列并定位其卷曲螺旋结构域，是确定蛋白质结构和功能的关键步骤。有多种工具可用于预测蛋白质序列中的卷曲螺旋结构域，包括基于位置特异性评分矩阵和机器学习方法的工具。

结果

在本文中，我们介绍了一种隐马尔可夫模型（CCHMM_PROF），该模型利用多序列比对（profile）中包含的信息来预测卷曲螺旋区域。该新方法对卷曲螺旋序列的区分准确率为 97%，真阳性率为 79%，假阳性率仅为 1%。此外，在预测蛋白质序列中卷曲螺旋结构域的位置时，该方法在残基水平上的准确率达到 80%，每个结构域和每个蛋白质的最佳效率分别为 81%和 80%。结果表明，CCHMM_PROF 优于所有现有的工具，可用于大规模基因组注释。