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.