  A fully automatic prediction for peptide retention time (RT) in liquid
chromatography (LC), termed as DeepRT, was developed using deep learning
approach, an ensemble of Residual Network (ResNet) and Long Short-Term Memory
(LSTM). In contrast to the traditional predictor based on the hand-crafted
features for peptides, DeepRT learns features from raw amino acid sequences and
makes relatively accurate prediction of peptide RTs with 0.987 R2 for
unmodified peptides. Furthermore, by virtue of transfer learning, DeepRT
enables utilization of the peptides datasets generated from different LC
conditions and of different modification status, resulting in the RT prediction
of 0.992 R2 for unmodified peptides and 0.978 R2 for post-translationally
modified peptides. Even though chromatographic behaviors of peptides are quite
complicated, the study here demonstrated that peptide RT prediction could be
largely improved by deep transfer learning. The DeepRT software is freely
available at https://github.com/horsepurve/DeepRT, under Apache2 open source
License.
