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Hydrophilic properties as a new contribution for computer-aided identification of short peptides in complex mixtures

机译:亲水特性为复杂混合物中计算机辅助识别短肽的新贡献

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A new method to predict elementary amino acid (AA) composition of peptides (molar mass <1,000 g/mol) is described. This procedure is based on a computer-aided method using three combined analyses—reversed phase liquid chromatography (RPLC), hydrophilic interaction chromatography (HILIC) and capillary electrophoresis coupled with mass spectrometry—and using a software calculating all possible amino acid combinations from the mass of any given peptide. The complementarity between HILIC and RPLC was demonstrated. Peptide retention prediction in HILIC was successfully modelled, and the achieved prediction accuracy was as high as r² = 0.97. This mathematical model, based on amino acid retention contributions and peptide length, provided the information about peptide hydrophilicity that was not redundant with its hydrophobicity. Correlations between respectively the hydrophobicity coefficients and RPLC retention time, hydrophilicity and HILIC retention time, and electrophoretic mobility and migration time were used for ranking all potential AA combinations corresponding to the given mass. The essential contribution of HILIC in this identification strategy and the need to combine the three models to significantly increase identification capabilities were both shown. Applied to an 18-standard peptide mixture, the identification procedure enabled the actual AA combination determination of the 14 di- to pentapeptides, in addition to an over 98 % reduction of possible combination numbers for the four hexapeptides. This procedure was then applied to the identification of 24 unknown peptides in a rapeseed protein hydrolysate. The effective AA composition was found for ten peptides, whereas for the 14 other peptides, the number of possible combinations was reduced by over 95 % thanks to the association of the three analyses. Finally, as a result of the information provided by the analytical techniques about peptides present in the mixture, the proposed method could become a highly valuable tool to recover bioactive peptides from undefined protein hydrolysates.
机译:描述了一种预测肽的基本氨基酸(AA)组成(摩尔质量<1,000 g / mol)的新方法。该程序基于计算机辅助方法,该方法使用了三种组合分析方法—反相液相色谱(RPLC),亲水相互作用色谱法(HILIC)和毛细管电泳与质谱联用–并使用软件根据质量计算所有可能的氨基酸组合任何给定的肽。证明了HILIC和RPLC之间的互补性。成功建模了HILIC中的肽保留预测模型,并且获得的预测精度高达r²= 0.97。基于氨基酸保留贡献和肽长度的该数学模型提供了有关肽亲水性的信息,该信息与其疏水性无关。分别使用疏水性系数与RPLC保留时间,亲水性和HILIC保留时间以及电泳迁移率和迁移时间之间的相关性,对与给定质量相对应的所有潜在AA组合进行排名。都显示了HILIC在这种识别策略中的重要贡献以及将三种模型结合起来以显着提高识别能力的需求。通过将鉴定方法应用于18种标准肽混合物,不仅可以对14种二肽至五肽进行实际的AA组合测定,而且还可以将四种六肽的组合数减少98%以上。然后将该程序用于鉴定菜籽蛋白水解物中的24种未知肽。发现有效的AA组成可用于10个肽段,而对于其他14个肽段,由于这三个分析的关联,可能的组合数量减少了95%以上。最后,由于分析技术提供的有关混合物中存在的肽的信息,所提出的方法可能成为从不确定的蛋白水解物中回收生物活性肽的极有价值的工具。

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