Abdul-Khalek Naim, Wimmer Reinhard, Overgaard Michael Toft, Gregersen Echers Simon
Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark.
Comput Struct Biotechnol J. 2023 Jul 22;21:3715-3727. doi: 10.1016/j.csbj.2023.07.027. eCollection 2023.
Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.
使用基于定量质谱（MS）的方法对复杂混合物中的肽进行准确和绝对定量需要先验知识和同位素标记标准品，从而增加了分析成本、时间消耗和人力，因此限制了能够准确量化的肽的数量。这源于肽之间不同的电离效率，因此，了解影响MS分析中电离和响应的物理化学性质对于开发限制较少的无标记定量方法至关重要。在这里，我们使用等摩尔肽库存储库数据来开发一个深度学习模型，该模型能够识别影响MS1响应的氨基酸。通过使用带有注意力机制的编码器-解码器，并将注意力权重与氨基酸物理化学性质相关联，我们深入了解了数据集中控制肽水平MS1响应的性质。虽然这个问题不能用一组单一的氨基酸和性质来描述，但可以重复获得不同的模式。这些性质分为与肽的疏水性、电荷和结构倾向相关的三个主要类别。此外，我们的模型仅基于肽序列输入就能预测在定义条件下的MS1强度输出。使用经过优化的训练数据集，该模型基于5折交叉验证预测对数转换后的肽MS1强度，平均误差为9.7±0.5%，并且在对数转换数据和实际尺度数据上均优于随机森林和岭回归模型。这项工作展示了深度学习如何促进对影响肽MS1响应的物理化学性质的识别，同时也说明了基于序列的响应预测和无标记肽水平定量如何可能影响定量蛋白质组学中的未来工作流程。
