Unilever Foods Innovation Centre - Hive, Bronland 14, 6708 WH, Wageningen, the Netherlands.
Laboratory of Organic Chemistry, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, the Netherlands.
Anal Bioanal Chem. 2023 Jun;415(14):2715-2726. doi: 10.1007/s00216-023-04670-2. Epub 2023 Mar 31.
Peptides are an important group of compounds contributing to the desired, as well as the undesired taste of a food product. Their taste impressions can include aspects of sweetness, bitterness, savoury, umami and many other impressions depending on the amino acids present as well as their sequence. Identification of short peptides in foods is challenging. We developed a method to assign identities to short peptides including homologous structures, i.e. peptides containing the same amino acids with a different sequence order, by accurate prediction of the retention times during reversed phase separation. To train the method, a large set of well-defined short peptides with systematic variations in the amino acid sequence was prepared by a novel synthesis strategy called 'swapped-sequence synthesis'. Additionally, several proteins were enzymatically digested to yield short peptides. Experimental retention times were determined after reversed phase separation and peptide MS data was acquired using a high-resolution mass spectrometer operated in data-dependent acquisition mode (DDA). A support vector regression model was trained using a combination of existing sequence-independent peptide descriptors and a newly derived set of selected amino acid index derived sequence-specific peptide (ASP) descriptors. The model was trained and validated using the experimental retention times of the 713 small food-relevant peptides prepared. Whilst selecting the most useful ASP descriptors for our model, special attention was given to predict the retention time differences between homologous peptide structures. Inclusion of ASP descriptors greatly improved the ability to accurately predict retention times, including retention time differences between 157 homologous peptide pairs. The final prediction model had a goodness-of-fit (Q) of 0.94; moreover for 93% of the short peptides, the elution order was correctly predicted.
肽是一类重要的化合物，对食品的可口和不可口味道都有贡献。它们的味道印象可以包括甜味、苦味、鲜味、鲜味等方面，具体取决于存在的氨基酸以及它们的序列。鉴定食品中的短肽具有挑战性。我们开发了一种方法，通过准确预测反相分离过程中的保留时间，来确定短肽的身份，包括同源结构，即含有相同氨基酸但序列顺序不同的肽。为了训练该方法，我们采用了一种称为“交换序列合成”的新合成策略，制备了具有系统氨基酸序列变化的大量定义良好的短肽，以作为训练集。此外，还通过酶解几种蛋白质来获得短肽。在反相分离后确定实验保留时间，并使用在数据依赖采集模式（DDA）下运行的高分辨率质谱仪获取肽 MS 数据。使用现有的序列无关肽描述符和一组新推导的选定氨基酸指数衍生的序列特异性肽（ASP）描述符的组合来训练支持向量回归模型。使用 713 种小食品相关肽的实验保留时间对模型进行训练和验证。在选择最有用的 ASP 描述符时，我们特别注意预测同源肽结构之间的保留时间差异。包括 ASP 描述符极大地提高了准确预测保留时间的能力，包括 157 对同源肽对之间的保留时间差异。最终预测模型的拟合度（Q）为 0.94；此外，对于 93%的短肽，洗脱顺序被正确预测。
