Detection of new protein domains using co-occurrence: application to Plasmodium falciparum
Detection of new protein domains using co-occurrence: application to Plasmodium falciparum
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DOI：
10.1093/bioinformatics/btp560
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发表时间：
2009-12-01
期刊：
影响因子：
5.8
通讯作者：
Breehelin, Laurent
中科院分区：
文献类型：
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作者：
Terrapon, Nicolas;Gascuel, Olivier;Breehelin, Laurent
动机：隐马尔可夫模型（HMM）已被证明是新测序生物体中蛋白质结构域识别的强大工具。然而，高度差异的蛋白质中可能会遗漏许多结构域。恶性疟原虫蛋白就是这种情况，它是人类疟疾的主要致病因子。结果：我们提出了一种方法，通过利用这些结构域与蛋白质中其他一些最喜欢的结构域优先出现的趋势来提高 HMM 结构域检测的灵敏度。当序列信息本身不足以保证特定域的存在时，我们的方法可以根据其他 Pfam 或 InterPro 域的存在进行检测。此外，改组过程允许我们估计与结果相关的错误发现率。应用于恶性疟原虫时，我们的方法识别了 585 个新的 Pfam 域（相对于 Pfam 数据库中的 3683 个已知域），估计错误率 < 20%。这些新域为恶性疟原虫蛋白质组提供了 387 个新的基因本体 (GO) 注释。当将该方法应用于相关疟原虫物种（间日疟原虫和约氏疟原虫）时，获得了类似且一致的结果。
Motivation: Hidden Markov models (HMMs) have proved to be a powerful tool for protein domain identification in newly sequenced organisms. However, numerous domains may be missed in highly divergent proteins. This is the case for Plasmodium falciparum proteins, the main causal agent of human malaria.Results: We propose a method to improve the sensitivity of HMM domain detection by exploiting the tendency of the domains to appear preferentially with a few other favorite domains in a protein. When sequence information alone is not sufficient to warrant the presence of a particular domain, our method enables its detection on the basis of the presence of other Pfam or InterPro domains. Moreover, a shuffling procedure allows us to estimate the false discovery rate associated with the results. Applied to P.falciparum, our method identifies 585 new Pfam domains (versus the 3683 already known domains in the Pfam database) with an estimated error rate < 20%. These new domains provide 387 new Gene Ontology (GO) annotations to the P.falciparum proteome. Analogous and congruent results are obtained when applying the method to related Plasmodium species (P.vivax and P.yoelii).