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

TL;DR

This paper introduces DeepRT, a deep learning-based tool that accurately predicts peptide retention times in liquid chromatography, leveraging transfer learning to improve predictions across different conditions and modifications.

Contribution

DeepRT is the first to combine ResNet and LSTM architectures with transfer learning for peptide RT prediction from raw sequences, outperforming traditional methods.

Findings

Achieved R2 of 0.987 for unmodified peptides

Enhanced prediction accuracy with transfer learning (R2 of 0.992 and 0.978) for modified peptides

Demonstrated deep transfer learning effectively models complex peptide chromatographic behaviors

Abstract

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.