Manitoba Centre for Proteomics and Systems Biology, Canada.
J Chromatogr A. 2011 Sep 16;1218(37):6348-55. doi: 10.1016/j.chroma.2011.06.092. Epub 2011 Jul 3.
The two leading RP-HPLC approaches for deriving hydrophobicity values of amino acids utilize either sets of designed synthetic peptides or extended random datasets often extracted from proteomics experiments. We find that the best examples of these two methods provide virtually identical results--with exception of Lys, Arg, and His. The intrinsic hydrophobicity values of the remaining residues as determined by Kovacs et al. (Biopolymers 84 (2006) 283) correlates with an R(2)-value of 0.995+ against amino acid retention coefficients from our Sequence Specific Retention Calculator model (Anal. Chem. 78 (2006) 7785). This novel finding lays the foundation for establishing consensus amino acids hydrophobicity scales as determined by RP-HPLC. Simultaneously, we find the assignment of hydrophobicity values for charged residues (Lys, Arg and His at pH 2) is ambiguous; their retention contribution is strongly affected by the overall peptide hydrophobicity. The unique behavior of the basic residues is related to the dualistic character of the RP peptide retention mechanism, where both hydrophobic and ion-pairing interactions are involved. We envision the introduction of "sliding" hydrophobicity scales for charged residues as a new element in peptide retention prediction models. We also show that when using a simple additive retention prediction model, the "correct" coefficient value optimization (0.98+ correlation against values determined by synthetic peptide approach) requires a training set of at least 100 randomly selected peptides.
两种主要的反相高效液相色谱(RP-HPLC)方法用于推导氨基酸的疏水性值,分别使用设计的合成肽集或通常从蛋白质组学实验中提取的扩展随机数据集。我们发现,这两种方法的最佳示例几乎提供了完全相同的结果——除了赖氨酸(Lys)、精氨酸(Arg)和组氨酸(His)。Kovacs 等人(Biopolymers 84 (2006) 283)确定的其余残基的固有疏水性值与我们的序列特异性保留计算器模型(Anal. Chem. 78 (2006) 7785)中氨基酸保留系数的 R(2)-值为 0.995+相关。这一新发现为建立通过 RP-HPLC 确定的共识氨基酸疏水性尺度奠定了基础。同时,我们发现带电荷残基(pH 2 时的赖氨酸、精氨酸和组氨酸)的疏水性值赋值不明确;它们的保留贡献受到整体肽疏水性的强烈影响。碱性残基的独特行为与 RP 肽保留机制的二元性有关,其中涉及疏水性和离子配对相互作用。我们设想为带电荷残基引入“滑动”疏水性尺度作为肽保留预测模型的新元素。我们还表明,当使用简单的加和保留预测模型时,“正确”系数值优化(与通过合成肽方法确定的值的 0.98+相关性)需要至少 100 个随机选择的肽的训练集。