DeepLC can predict retention times for peptides that carry as-yet unseen modifications

DeepLC, a deep learning-based peptide retention time predictor, can predict retention times for unmodified peptides as well as peptides with previously unseen modifications. The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC's ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.

Automated summary

DeepLC is a deep learning-based peptide retention time predictor that can accurately predict retention times for unmodified peptides and peptides with previously unseen modifications, addressing peptide identification ambiguity in complex mass spectrometry workflows. By using atomic composition-based peptide encoding, DeepLC is able to accurately predict retention times for a wide range of modifications, including those not seen during training, potentially enabling incorrect identifications to be flagged in proteome data analysis.