Pietrogrande Maria Chiara, Marchetti Nicola, Tosi Azzurra, Dondi Francesco, Righetti Pier Giorgio
Department of Chemistry, University of Ferrara, Ferrara, Italy.
Electrophoresis. 2005 Jul;26(14):2739-48. doi: 10.1002/elps.200410375.
This paper describes a mathematical approach applied for decoding the complex signal of two-dimensional polyacrylamide gel electrophoresis maps of protein mixtures. The method is helpful in extracting analytical information since separation of all the proteins present in the sample is still far from being achieved and co-migrating proteins are generally present in the same spot. The simplified method described is based on the study of the 2-D autocovariance function (2D-ACVF) computed on an experimental digitized map. The first part of the 2D-ACVF allows for the estimation of the number of proteins present in the sample (2D-ACVF computed at the origin) and of the separation performance (mean spot size). Moreover, the 2D-ACVF plot is a powerful tool in identifying order in the spot position, and singling it out from the complex separation pattern. This method was validated on synthetic maps obtained by computer simulation to describe 2-D PAGE real maps and reference maps retrieved from the SWISS-2DPAGE database. The results obtained are discussed by focusing on specific information relevant in proteomics: sample complexity, separation performance, and identification of spot trains related to post-translational modifications.
本文描述了一种用于解码蛋白质混合物二维聚丙烯酰胺凝胶电泳图谱复杂信号的数学方法。由于目前仍远未实现对样品中所有蛋白质的分离，且共迁移的蛋白质通常存在于同一斑点中，因此该方法有助于提取分析信息。所描述的简化方法基于对在实验数字化图谱上计算的二维自协方差函数（2D - ACVF）的研究。2D - ACVF的第一部分允许估计样品中存在的蛋白质数量（在原点处计算的2D - ACVF）和分离性能（平均斑点大小）。此外，2D - ACVF图是识别斑点位置顺序并将其从复杂分离模式中挑选出来的有力工具。该方法在通过计算机模拟获得的合成图谱上得到验证，以描述二维聚丙烯酰胺凝胶电泳（2 - D PAGE）真实图谱以及从SWISS - 2DPAGE数据库检索的参考图谱。通过关注蛋白质组学中相关的特定信息：样品复杂性、分离性能以及与翻译后修饰相关的斑点序列识别，对所得结果进行了讨论。
