Institute of Biotechnology, Graiciūno 8, LT-02241 Vilnius, Lithuania.
BMC Bioinformatics. 2010 Feb 17;11:89. doi: 10.1186/1471-2105-11-89.
Detection of common evolutionary origin (homology) is a primary means of inferring protein structure and function. At present, comparison of protein families represented as sequence profiles is arguably the most effective homology detection strategy. However, finding the best way to represent evolutionary information of a protein sequence family in the profile, to compare profiles and to estimate the biological significance of such comparisons, remains an active area of research.
Here, we present a new homology detection method based on sequence profile-profile comparison. The method has a number of new features including position-dependent gap penalties and a global score system. Position-dependent gap penalties provide a more biologically relevant way to represent and align protein families as sequence profiles. The global score system enables an analytical solution of the statistical parameters needed to estimate the statistical significance of profile-profile similarities. The new method, together with other state-of-the-art profile-based methods (HHsearch, COMPASS and PSI-BLAST), is benchmarked in all-against-all comparison of a challenging set of SCOP domains that share at most 20% sequence identity. For benchmarking, we use a reference ("gold standard") free model-based evaluation framework. Evaluation results show that at the level of protein domains our method compares favorably to all other tested methods. We also provide examples of the new method outperforming structure-based similarity detection and alignment. The implementation of the new method both as a standalone software package and as a web server is available at http://www.ibt.lt/bioinformatics/coma.
Due to a number of developments, the new profile-profile comparison method shows an improved ability to match distantly related protein domains. Therefore, the method should be useful for annotation and homology modeling of uncharacterized proteins.
检测共同的进化起源（同源性）是推断蛋白质结构和功能的主要手段。目前，比较序列特征表示的蛋白质家族被认为是最有效的同源检测策略。然而，在特征中找到表示蛋白质序列家族进化信息的最佳方法，比较特征并估计此类比较的生物学意义，仍然是一个活跃的研究领域。
在这里，我们提出了一种基于序列特征-特征比较的新同源检测方法。该方法具有许多新功能，包括位置相关的空位罚分和全局评分系统。位置相关的空位罚分提供了一种更具生物学相关性的方法来表示和对齐蛋白质家族作为序列特征。全局评分系统使我们能够分析解决估计特征-特征相似性统计显著性所需的统计参数。该新方法与其他基于特征的最新方法（HHsearch、COMPASS 和 PSI-BLAST）一起，在 SCOP 域的全对全比较中进行了基准测试，这些域共享的序列同一性最多为 20%。对于基准测试，我们使用参考（“黄金标准”）无模型基于评估框架。评估结果表明，在蛋白质域的水平上，我们的方法与所有其他测试方法相比具有优势。我们还提供了新方法在表现优于结构相似性检测和比对的例子。该新方法的实现既作为独立的软件包，也作为网络服务器，可在 http://www.ibt.lt/bioinformatics/coma 上获得。
由于多项发展，新的特征-特征比较方法显示出更好的匹配远距离相关蛋白质域的能力。因此，该方法对于未表征蛋白质的注释和同源建模应该是有用的。
Zotero 插件
