Department of Chemistry, University of California, Berkeley, CA 94720-1460, USA.
J Am Soc Mass Spectrom. 2011 Apr;22(4):624-32. doi: 10.1007/s13361-011-0081-4. Epub 2011 Feb 25.
A method that uses the abundances of large clusters formed in electrospray ionization to determine the solution-phase molar fractions of amino acids in multi-component mixtures is demonstrated. For solutions containing either four or 10 amino acids, the relative abundances of protonated molecules differed from their solution-phase molar fractions by up to 30-fold and 100-fold, respectively. For the four-component mixtures, the molar fractions determined from the abundances of larger clusters consisting of 19 or more molecules were within 25% of the solution-phase molar fractions, indicating that the abundances and compositions of these clusters reflect the relative concentrations of these amino acids in solution, and that ionization and detection biases are significantly reduced. Lower accuracy was obtained for the 10-component mixtures where values determined from the cluster abundances were typically within a factor of three of their solution molar fractions. The lower accuracy of this method with the more complex mixtures may be due to specific clustering effects owing to the heterogeneity as a result of significantly different physical properties of the components, or it may be the result of lower S/N for the more heterogeneous clusters and not including the low-abundance more highly heterogeneous clusters in this analysis. Although not as accurate as using traditional standards, this clustering method may find applications when suitable standards are not readily available.
一种利用电喷雾电离中形成的大簇的丰度来确定多组分混合物中氨基酸在溶液相摩尔分数的方法得到了证明。对于含有四种或十种氨基酸的溶液,质子化分子的相对丰度与其溶液相摩尔分数的差异分别高达 30 倍和 100 倍。对于四组分混合物,由 19 个或更多分子组成的较大簇的丰度确定的摩尔分数与溶液相摩尔分数相差在 25%以内,这表明这些簇的丰度和组成反映了这些氨基酸在溶液中的相对浓度,并且电离和检测偏差显著降低。对于更复杂的 10 组分混合物,获得的准确性较低,因为从簇丰度确定的值通常与其溶液摩尔分数相差一个因子三。这种方法在更复杂的混合物中准确性较低,可能是由于由于各组分的物理性质明显不同而导致的异质性引起的特定聚类效应,也可能是由于更不均匀的簇的信噪比较低,并且在这种分析中不包括低丰度的更高度不均匀的簇所致。尽管不如使用传统标准准确,但当合适的标准不易获得时,这种聚类方法可能会有应用。