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A fully automatic prediction for peptide retention time (RT) in liquidchromatography (LC), termed as DeepRT, was developed using deep learningapproach, an ensemble of Residual Network (ResNet) and Long Short-Term Memory(LSTM). In contrast to the traditional predictor based on the hand-craftedfeatures for peptides, DeepRT learns features from raw amino acid sequences andmakes relatively accurate prediction of peptide RTs with 0.987 R2 forunmodified peptides. Furthermore, by virtue of transfer learning, DeepRTenables utilization of the peptides datasets generated from different LCconditions and of different modification status, resulting in the RT predictionof 0.992 R2 for unmodified peptides and 0.978 R2 for post-translationallymodified peptides. Even though chromatographic behaviors of peptides are quitecomplicated, the study here demonstrated that peptide RT prediction could belargely improved by deep transfer learning. The DeepRT software is freelyavailable at https://github.com/horsepurve/DeepRT, under Apache2 open sourceLicense.

Retention Time of Peptides in Liquid Chromatography Is Well Estimated upon Deep Transfer Learning