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Rapid screening of membrane topology of secondary transport proteins.

机译:快速筛选次级转运蛋白的膜拓扑。

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摘要

Limited experimental data may be very useful to discriminate between membrane topology models of membrane proteins derived from different methods. A membrane topology screening method is proposed by which the cellular disposition of three positions in a membrane protein are determined, the N- and the C-termini and a position in the middle of the protein. The method involves amplification of the encoding genes or gene fragments by PCR, rapid cloning in dedicated vectors by ligation independent cloning, and determination of the cellular disposition of the three sites using conventional techniques. The N-terminus was determined by labeling with a fluorescent probe, the central position and the C-terminus by the reporter fusion technique using alkaline phosphatase (PhoA) and green fluorescence protein (GFP) as reporters. The method was evaluated using 16 transporter proteins of known function from four different structural classes. For 13 proteins a complete set of three localizations was obtained. The experimental data was used to discriminate between membrane topology models predicted by TMHMM, a widely used predictor using the amino acid sequence as input and by MemGen that uses hydropathy profile alignment and known 3D structures or existing models. It follows that in those cases where the models from the two methods were similar, the models were consistent with the experimental data. In those cases where the models differed, the MemGen model agreed with the experimental data. Three more recent predictors, MEMSAT3, OCTOPUS and TOPCONS showed a significantly higher consistency with the experimental data than observed with TMHMM.
机译:有限的实验数据对于区分源自不同方法的膜蛋白的膜拓扑模型可能非常有用。提出了一种膜拓扑学筛选方法,通过该方法可以确定膜蛋白中三个位置的细胞位置,即N和C端以及蛋白中间的位置。该方法包括通过PCR扩增编码基因或基因片段,通过连接独立克隆在专用载体中快速克隆,以及使用常规技术确定三个位点的细胞位置。通过使用碱性磷酸酶(PhoA)和绿色荧光蛋白(GFP)作为报告基因的报告基因融合技术,通过用荧光探针标记中心位置和C末端,确定N末端。使用来自四个不同结构类别的已知功能的16种转运蛋白评估了该方法。对于13种蛋白质,获得了三个定位的完整集合。实验数据用于区分由TMHMM预测的膜拓扑模型,TMHMM是使用氨基酸序列作为输入的广泛使用的预测器,而MemGen使用亲水性谱比对和已知的3D结构或现有模型来预测。因此,在两种方法的模型相似的情况下,模型与实验数据一致。在这些模型不同的情况下,MemGen模型与实验数据一致。与TMHMM相比,最新的三个预测因子MEMSAT3,OCTOPUS和TOPCONS与实验数据的一致性更高。

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