Profile-profile methods provide improved fold-recognition: A study of different profile-profile alignment methods
Profile-profile methods provide improved fold-recognition: A study of different profile-profile alignment methods
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DOI：
10.1002/prot.20184
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发表时间：
2004-10-01
影响因子：
2.9
通讯作者：
Elofsson, A
中科院分区：
文献类型：
--
作者：
Ohlson, T;Wallner, B;Elofsson, A
为了改善相关蛋白的检测，在查询和靶蛋白中都包含进化信息通常很有用。包含此信息的一种方法是使用配置文件对齐，其中将查询蛋白的轮廓与目标蛋白的曲线进行了比较。配置文件对齐方式可以通过几种根本不同的方式实现。可以使用点产品，概率模型或信息理论度量来计算两个位置之间的相似性。在这里，我们提供了不同曲线profile比对方法的大规模比较。我们表明，剖面profile方法的性能至少比标准序列profile方法高30％，既可以识别超家族相关的蛋白质和所获得的比对的质量。尽管所有方法的性能都非常相似，但是使用概率评分功能的配置型profile方法具有优势，因为它们可以创建良好的对齐方式并使用相同的差距 - 折叠式识别能力显示出良好的折叠识别能力，而其他方法则需要使用其他方法不同的参数以获得可比的性能。 （c）2004 Wiley-Liss，Inc。
To improve the detection of related proteins, it is often useful to include evolutionary information for both the query and target proteins. One method to include this information is by the use of profile-profile alignments, where a profile from the query protein is compared with the profiles from the target proteins. Profile-profile alignments can be implemented in several fundamentally different ways. The similarity between two positions can be calculated using a dot-product, a probabilistic model, or an information theoretical measure. Here, we present a large-scale comparison of different profile-profile alignment methods. We show that the profile-profile methods perform at least 30% better than standard sequence-profile methods both in their ability to recognize superfamily-related proteins and in the quality of the obtained alignments. Although the performance of all methods is quite similar, profile-profile methods that use a probabilistic scoring function have an advantage as they can create good alignments and show a good fold recognition capacity using the same gap-penalties, while the other methods need to use different parameters to obtain comparable performances. (C) 2004 Wiley-Liss, Inc.