MESSA: MEta-Server for protein Sequence Analysis.
MESSA: MEta-Server for protein Sequence Analysis.
复制标题
DOI：
10.1186/1741-7007-10-82
复制
发表时间：
2012-10-02
期刊：
影响因子：
5.4
通讯作者：
Grishin NV
中科院分区：
文献类型：
--
作者：
Cong Q;Grishin NV
计算序列分析是从蛋白质序列中预测蛋白质的局部序列性质、同源物、空间结构和功能的一种有效方法，它为获得所需的蛋白质信息提供了一种有效途径。由于可靠的预测通常是基于许多计算机程序的共识，元服务器已开发，以满足这种需求。大多数元服务器专注于序列分析的一个方面，而其他元服务器则包含更多信息，例如用于局部序列特征预测的PredictProtein，用于结构域架构和序列基序注释的SMART，以及用于二级和空间结构预测的GeneSilico。然而，由于局部序列性质、三维结构和功能的预测通常交织在一起，因此将它们一起解决是有益的。我们开发了一个MEta-Server for protein Sequence Analysis（MESSA），以促进全面的蛋白质序列分析，并收集感兴趣蛋白质的结构和功能预测。对于输入序列，服务器利用许多选择工具来预测局部序列特性，例如二级结构、结构无序区域、卷曲螺旋、信号肽和跨膜螺旋;检测同源蛋白质并将查询分配到蛋白质家族;识别三维结构模板并生成结构模型;并提供有关蛋白质功能的预测性陈述，包括功能注释、基因本体论术语、酶分类和可能的功能相关蛋白质。我们测试了MESSA的蛋白质组的柑橘黄龙病菌亚洲。人工处理表明，MESSA生成的三维结构模型覆盖了该蛋白质组中约75%的残基，并且可以预测80%的蛋白质的功能。MESSA可在http://prodata.swmed.edu/MESSA/上免费用于非商业用途
Computational sequence analysis, that is, prediction of local sequence properties, homologs, spatial structure and function from the sequence of a protein, offers an efficient way to obtain needed information about proteins under study. Since reliable prediction is usually based on the consensus of many computer programs, meta-severs have been developed to fit such needs. Most meta-servers focus on one aspect of sequence analysis, while others incorporate more information, such as PredictProtein for local sequence feature predictions, SMART for domain architecture and sequence motif annotation, and GeneSilico for secondary and spatial structure prediction. However, as predictions of local sequence properties, three-dimensional structure and function are usually intertwined, it is beneficial to address them together. We developed a MEta-Server for protein Sequence Analysis (MESSA) to facilitate comprehensive protein sequence analysis and gather structural and functional predictions for a protein of interest. For an input sequence, the server exploits a number of select tools to predict local sequence properties, such as secondary structure, structurally disordered regions, coiled coils, signal peptides and transmembrane helices; detect homologous proteins and assign the query to a protein family; identify three-dimensional structure templates and generate structure models; and provide predictive statements about the protein's function, including functional annotations, Gene Ontology terms, enzyme classification and possible functionally associated proteins. We tested MESSA on the proteome of Candidatus Liberibacter asiaticus. Manual curation shows that three-dimensional structure models generated by MESSA covered around 75% of all the residues in this proteome and the function of 80% of all proteins could be predicted. MESSA is free for non-commercial use at http://prodata.swmed.edu/MESSA/