Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence

Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in a database search. There are methods to accurately predict peptide ion mobility through drift tube devices, but methods to predict mobility through high-field asymmetric waveform ion mobility (FAIMS) are underexplored. Here, we successfully model peptide ions’ FAIMS mobility using a multi-label classification scheme to account for non-normal transmission distributions. We trained two models from over 100,000 human peptide precursors: a random forest and a long-term short-term memory (LSTM) neural network. Both models had different strengths, and the ensemble average of model predictions produced a higher F2 score than either model alone. Finally, we explored cases where the models make mistakes and demonstrate the predictive performance of F2 = 0.66 (AUROC = 0.928) on a new test data set of nearly 40,000 E. coli peptide ions. The deep learning model is easily accessible via https://faims.xods.org.肽离子迁移率为基于质谱的蛋白质组学增添了额外的分离维度。精准预测肽离子迁移率的能力，可用于加快分析方法开发，并在数据库检索中甄别真实匹配结果。目前已有借助漂移管设备精准预测肽离子迁移率的方法，但针对高场不对称波形离子迁移率（high-field asymmetric waveform ion mobility, FAIMS）的迁移率预测方法仍未得到充分探索。本研究采用多标签分类框架以适配非正态传输分布，成功构建了肽离子FAIMS迁移率的预测模型。我们基于超过10万条人类肽前体样本训练了两种模型：随机森林与长短期记忆（long-term short-term memory, LSTM）神经网络。两种模型各有优劣，将模型预测结果进行集成平均后，得到的F2分数相较于单一模型更高。最后，我们分析了模型出现预测失误的场景，并在包含近4万条大肠杆菌（E. coli）肽离子的全新测试数据集上验证了模型的预测性能：F2分数达0.66，受试者工作特征曲线下面积（area under the receiver operating characteristic curve, AUROC）为0.928。该深度学习模型可通过https://faims.xods.org便捷获取。