Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
Nucleic Acids Res. 2010 Jan;38(Database issue):D167-80. doi: 10.1093/nar/gkp1016. Epub 2009 Nov 17.
Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a 'Bar Code' format, which also displays known instances from homologous proteins through a novel 'Instance Mapper' protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation.
线性基序是多结构域蛋白质的短片段,可独立于蛋白质三级结构提供调节功能。许多细胞内信号转导都通过线性基序的蛋白质修饰进行。现在已经报道了数千个线性基序实例,其中最著名的是磷酸化位点。尽管线性基序显然非常丰富,但由于难以获得稳健的统计评估,因此很难在蛋白质序列中从头预测它们。ELM 资源(http://elm.eu.org/)提供了一个不断扩展的知识库,目前涵盖 146 个已知基序,并对包括 >1300 个实验报告实例的注释进行了注释。ELM 也是一种探索性工具,可用于建议在感兴趣的蛋白质中具有已知线性基序的新候选者。有关蛋白质结构域、蛋白质结构和天然无序、细胞和分类上下文的信息用于减少或降低假阳性匹配。结果以“条形码”格式显示,该格式还通过基于 PHI-BLAST 的新颖“实例映射器”协议显示同源蛋白质中的已知实例。ELM 服务器输出提供了到 ELM 注释以及许多远程资源的链接。使用这些链接,研究人员可以探索基序、蛋白质、复杂结构和相关文献,以评估候选基序是否值得进行实验研究。