A multi-objective optimization approach accurately resolves protein domain architectures.
A multi-objective optimization approach accurately resolves protein domain architectures.
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DOI：
10.1093/bioinformatics/btv582
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发表时间：
2016-02-01
期刊：
影响因子：
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通讯作者：
Carbone A
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文献类型：
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作者：
Bernardes JS;Vieira FR;Zaverucha G;Carbone A
动机：给定一个蛋白质序列和一些与之匹配的潜在结构域，该序列的结构域内容和最可能的结构域结构是什么？这个问题在蛋白质注释中是至关重要的，是所有预测注释策略的主要步骤之一。另一方面，当潜在的领域有几个并且由于重叠的领域边界而发生冲突时，找到问题的解决方案可能会变得困难。多结构域蛋白结构域结构的准确预测为功能预测、比较基因组学和分子进化提供了重要信息。结果：我们开发了DAMA (Domain Annotation by a Multi-objective Approach)，这是一种通过多目标优化算法结合领域匹配分数、先前观察到的多领域共现和领域重叠来识别架构的新方法。DAMA已经在一个已知的基于CATH结构域分配的基准数据集和恶性疟原虫蛋白集上进行了验证。当与现有工具在这两个数据集上进行比较时，它的性能优于所有工具。可用性和实现：DAMA软件是用c++实现的，源代码可以在http://www.lcqb.upmc.fr/DAMA上找到。联系方式：juliana.silva_bernardes@upmc.fr或alessandra.carbone@lip6.fr补充信息：补充数据可在Bioinformatics在线获取。
Motivation: Given a protein sequence and a number of potential domains matching it, what are the domain content and the most likely domain architecture for the sequence? This problem is of fundamental importance in protein annotation, constituting one of the main steps of all predictive annotation strategies. On the other hand, when potential domains are several and in conflict because of overlapping domain boundaries, finding a solution for the problem might become difficult. An accurate prediction of the domain architecture of a multi-domain protein provides important information for function prediction, comparative genomics and molecular evolution. Results: We developed DAMA (Domain Annotation by a Multi-objective Approach), a novel approach that identifies architectures through a multi-objective optimization algorithm combining scores of domain matches, previously observed multi-domain co-occurrence and domain overlapping. DAMA has been validated on a known benchmark dataset based on CATH structural domain assignments and on the set of Plasmodium falciparum proteins. When compared with existing tools on both datasets, it outperforms all of them. Availability and implementation: DAMA software is implemented in C++ and the source code can be found at http://www.lcqb.upmc.fr/DAMA. Contact: juliana.silva_bernardes@upmc.fr or alessandra.carbone@lip6.fr Supplementary information: Supplementary data are available at Bioinformatics online.